satellite/0000755000176200001440000000000013573407552012253 5ustar liggesuserssatellite/NAMESPACE0000644000176200001440000000476313573376470013510 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(addRasterMeta2Sat) export(addSatDataLayer) export(addSatLog) export(addSatMetaEntry) export(addSatMetaParam) export(alignGeometry) export(calcAtmosCorr) export(calcDODN) export(calcEarthSunDist) export(calcPathRadDOS) export(calcTOAIrradModel) export(calcTOAIrradRadRef) export(calcTOAIrradTable) export(calcTopoCorr) export(compFilePathLandsat) export(compMetaLandsat) export(convRad2BT) export(convRad2Ref) export(convRef2RadLinear) export(convSC2Rad) export(convSC2Ref) export(countSatDataLayers) export(createRasterMetaData) export(createSatBCDE) export(crop) export(demTools) export(extend) export(getSatBCDE) export(getSatBCDECalib) export(getSatBCDEFromSpectrum) export(getSatBCDEFromType) export(getSatBCDESolar) export(getSatBCDESolarCalib) export(getSatBCDESres) export(getSatBCDEThermal) export(getSatBCDEThermalCalib) export(getSatBCDEType) export(getSatBID) export(getSatBTK1) export(getSatBTK2) export(getSatBandInfo) export(getSatCalib) export(getSatDATE) export(getSatDataLayer) export(getSatDataLayers) export(getSatESD) export(getSatESUN) export(getSatLMAX) export(getSatLMIN) export(getSatLNBR) export(getSatLayerfromData) export(getSatLog) export(getSatMeta) export(getSatMetaBCDETemplate) export(getSatMetaLayer) export(getSatPRAD) export(getSatParam) export(getSatProjection) export(getSatRADA) export(getSatRADM) export(getSatREFA) export(getSatREFM) export(getSatRadMax) export(getSatRadMin) export(getSatRefMax) export(getSatRefMin) export(getSatRes) export(getSatSAZM) export(getSatSELV) export(getSatSID) export(getSatSZEN) export(getSatSensor) export(getSatSensorGroup) export(getSatSensorInfo) export(getSatSpectrum) export(getSatType) export(getSatXRes) export(getSatYRes) export(lutInfo) export(lutInfoBCDEFromBID) export(lutInfoBIDFromBCDE) export(lutInfoBandsFromSID) export(lutInfoRSRromSID) export(lutInfoSGRPfromFilename) export(lutInfoSIDfromFilename) export(lutInfoSensorFromSID) export(maskInvarFeatures) export(plot) export(satellite) export(setSatBCDE) export(sortFilesLandsat) export(subset) export(updateRasterMetaData) exportClasses(Satellite) exportClasses(SatelliteInfo) exportClasses(SatelliteLayers) exportClasses(SatelliteLog) exportClasses(SatelliteMetaData) exportMethods(brick) exportMethods(stack) import(Rcpp) import(methods) import(raster) importFrom(stats,mvfft) importFrom(stats4,plot) useDynLib(satellite, .registration = TRUE) satellite/LICENSE0000644000176200001440000000007613572464352013263 0ustar liggesusersYEAR: 2019 COPYRIGHT HOLDER: Environmental Informatics Marburgsatellite/README.md0000644000176200001440000000152113572463522013527 0ustar liggesusers#### Package downloads from the [RStudio CRAN Mirror](http://cran-logs.rstudio.com/) This month | In total --------------- | ----------- ![month](http://cranlogs.r-pkg.org/badges/satellite) | ![total](http://cranlogs.r-pkg.org/badges/grand-total/satellite)
# satellite This smorgasbord provides a variety of functions which are useful for handling, manipulating and visualizing remote sensing data. ==== ### Installation For the stable release version of **satellite** use ```S install.packages("satellite") ``` To install the development version use ```S library(devtools) install_github("environmentalinformatics-marburg/satellite", ref = "develop") ``` ### Contact Please file bug reports and feature requests at https://github.com/environmentalinformatics-marburg/satellite/issuessatellite/data/0000755000176200001440000000000013572463522013162 5ustar liggesuserssatellite/data/l7.rda0000644000176200001440000002405013573376520014177 0ustar liggesusersBZh91AY&SY}@@@*Iʍkm9l̖ 6-DKffV"DmmLJelɶfl(VU_UTh4ѣPdzCTaG 24@4@USd@M4&L=OS5=U?dM4&d6IzhF@ɑhAL5O4"2iM@A5?DDB&44)Ih#@ LS4Ijbi =4Mi@h4hh4 41CCOSOS<&GW^l &` c8#tpGq u`7i0``NhrG8&Jʲ1baeN7ܜ:ZӁ1LMK#N~^|Lu? 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FsJOU bT/97[<> WMa C.0'|f$K4Hܖ;#*Z\F4M``Po|^5=Sp/4H\QU@ƫ)UWwgHqZC wCuj>flE@>:ĭvyٟӈƓZܗIvP2Wl#1^T( PjE?URw%=BBd]8\ҳ .>qB%N뚯xA>&2Q.udҸCQO^ǝ%,y2&y"Y97'Fwh?qW&JKs2{$WȻ߲ =5mACWEh3@)U;;_!|޶}@lչNi悻i\O+X} aMssqrK4>n,޿sj O1$tW,wcL}^Md?d]c3TBfv\{ 33νn0dxN)$z >s*>~ݚw`:kMA;'"7~W"K1 20Hy3%dW"^%cGU m90|K[=ICx(әT` %XR3x  Uf- 9An[AAxSl  ن+,C ;γJp؇ ,,ѻ=;[ISU_ALuT{klUM.5?UT7 MU{~|,e,op}W>z1$$u&} geBh4%iZ]E\acԭuc8،`9ڮLN4fB/3?Z-n_9Ʊ;fsP{!n>$ҕVqbt\y=p\^Dl n ?;delT= BaPʚP|94%̡M=LKsiV 颾#@&t\btY&\ս{j3wpAޓ TM[ǀU84X,D3eV{L{@ĐU:qifiYRY(FY?05"i)/^ȈXA_WdXf.x,^U;L{33Doϛ\^ZfcP5I@]M؊?lۧ[Kqbl2)Hp˸W-ӌNAZ?,;( @b&[fUJe7ٔᥬ!}˿e\¿x*UD\v\ nъhΞiQ L-MPƒ{ -#fq οV. %ҵRFI(YtfaHA5LyKtدBp:Ts!Yo(mR5vrOBy%}C_IMwBpЋPx tLTO(-J߱|vV]>U6DD>q%ĬW},ǝvhF6jty&^w775XRWvE7*ʕbҀLH+g$mwƶK>Ц3m$`^WVR~,1j{gTKľix\oT鸸I!}X,)`FdɝG:"iO\aabca9߬W'aK|\4M^})>0 YZsatellite/man/0000755000176200001440000000000013572463733013030 5ustar liggesuserssatellite/man/SatelliteInfo-class.Rd0000644000176200001440000000113213572464203017152 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AAAsatelliteClasses.R \docType{class} \name{SatelliteInfo-class} \alias{SatelliteInfo-class} \title{An S4 class to represent a satellite data file} \description{ An S4 class to represent a satellite data file } \section{Slots}{ \describe{ \item{\code{name}}{name of the data file without extension} \item{\code{filepath}}{full path and file of the data file} \item{\code{path}}{path to the data file} \item{\code{file}}{filename incl. extension of the data file} \item{\code{extension}}{extension of the data file} }} satellite/man/stack.Rd0000644000176200001440000000146613572464203014424 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stack.R \docType{methods} \name{stack} \alias{stack} \alias{stack,Satellite-method} \title{Convert selected layers of a Satellite object to a RasterStack} \usage{ \S4method{stack}{Satellite}(x, layer = names(x), ...) } \arguments{ \item{x}{an object of class 'Satellite'} \item{layer}{character vector (bcde codes) or integer vector (index) of the layers to be stacked} \item{...}{additional arguments passed on to \code{\link{stack}}} } \description{ Convert selected layers of a Satellite object to a RasterStack } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) stck <- stack(sat, c("B001n", "B002n", "B003n")) stck } satellite/man/SatelliteLayers-class.Rd0000644000176200001440000000060713573376467017543 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AAAsatelliteClasses.R \docType{class} \name{SatelliteLayers-class} \alias{SatelliteLayers-class} \title{An S4 class to represent satellite data} \description{ An S4 class to represent satellite data } \section{Slots}{ \describe{ \item{\code{layers}}{a list object containing individual RasterLayer objects} }} satellite/man/convSC2Ref.Rd0000644000176200001440000000472713572464203015234 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/convSC2Ref.R \name{convSC2Ref} \alias{convSC2Ref} \alias{convSC2Ref,Satellite-method} \alias{convSC2Ref,RasterStack-method} \alias{convSC2Ref,RasterLayer-method} \title{Convert a band's scaled counts or radiance values to reflectance} \usage{ \S4method{convSC2Ref}{Satellite}(x, szen_correction = "TRUE", subset = FALSE) \S4method{convSC2Ref}{RasterStack}(x, mult, add, szen) \S4method{convSC2Ref}{RasterLayer}(x, mult, add, szen) } \arguments{ \item{x}{An object of class Satellite, raster::RasterStack or raster::RasterLayer providing scaled counts (DNs).} \item{szen_correction}{Logical; if \code{TRUE}, sun zenith correction is being applied.} \item{subset}{Logical; if \code{TRUE}, all layers but the cropped ones are being dropped; if \code{FALSE} (default), cropped layers are appended to the Satellite object.} \item{mult}{Multiplicative coefficient for value transformation (i.e. slope).} \item{add}{Additive coefficient for value transformation (i.e. offset).} \item{szen}{Cosine of solar zenith angle.} } \value{ If x is a Satellite object, a Satellite object with added converted layers; \cr if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with converted layer(s). } \description{ Convert a band's scaled counts to reflectance using a simple linear conversion without any kind of atmospheric correction etc. } \details{ The conversion functions are taken from USGS' Landsat 8 Data Users Handbook which is available online at \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. If the sensor does not provide linear conversion coefficients for reflectance computation, the reflectance is calculated using the solar irradiance following the functions taken from USGS' Landsat 7 manual, chapter 11.3.2, which is available online at \url{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- convSC2Ref(sat) # If you use a raster layer, supply required meta information bcde <- "B002n" convSC2Ref(x = getSatDataLayer(sat, bcde), mult = getSatRADM(sat, bcde), add = getSatRADA(sat, bcde)) } \seealso{ \code{\link{calcAtmosCorr}} for conversions of scaled counts to physical units including a scene-based atmospheric correction. } satellite/man/names.Rd0000644000176200001440000000140713572464203014415 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/names.R \name{names} \alias{names} \alias{names,Satellite-method} \alias{names<-,Satellite-method} \title{Get/set Satellite data layer names} \usage{ \S4method{names}{Satellite}(x) \S4method{names}{Satellite}(x) <- value } \arguments{ \item{x}{A Satellite object.} \item{value}{Band codes of the individual data layers.} } \value{ Satellite data layer names as character vector. } \description{ Get/set Satellite data layer names, i.e. the BCDE id. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) names(sat) new_names <- paste0(names(sat), "_test") names(sat) <- new_names } satellite/man/l8.Rd0000644000176200001440000000135613572464203013640 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/satellite-package.R \docType{data} \name{l8} \alias{l8} \title{Landsat 8 sample data} \format{\code{RasterStack} with bands 1-7, 9-11 (incl. QA) of 41 by 41 pixels.} \source{ \url{https://earthexplorer.usgs.gov/} } \description{ This dataset comes from the USGS. It contains part of the Landsat 8 scene LC08_L1TP_195025_20130707_20170503_01_T1 (Collection 1 Level-1) from 2013-07-07 over Maburg, Germany. } \details{ Use of this data requires your agreement to the USGS regulations on using Landsat data. } \examples{ plotRGB(l8, r = 4, g = 3, b = 2, stretch = "hist") # true-color composite plotRGB(l8, r = 5, g = 4, b = 3, stretch = "hist") # false-color composite } satellite/man/satellite-package.Rd0000644000176200001440000000151013572464203016664 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/satellite-package.R \docType{package} \name{satellite-package} \alias{satellite-package} \alias{satellitepackage} \title{Smorgasboard for remote sensing functions.} \description{ Smorgasbord for remote sensing functions } \details{ The package provides a variety of functions which are useful for handling, manipulating and visualizing remote sensing data. } \references{ Some functions are taken and/or adopted from Sarah C. Goslee (2011). Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software, 43(4), 1-25. Online available at \url{http://www.jstatsoft.org/v43/i04/}. } \author{ Thomas Nauss, Hanna Meyer, Florian Detsch, Tim Appelhans \cr \cr \emph{Maintainer:} Florian Detsch \email{fdetsch@web.de} } \keyword{package} satellite/man/alignGeometry.Rd0000644000176200001440000000373613572464203016127 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/alignGeometry.R \name{alignGeometry} \alias{alignGeometry} \alias{alignGeometry,Satellite-method} \alias{alignGeometry,RasterStack-method} \alias{alignGeometry,RasterLayer-method} \title{Align raster geometry between two data sets} \usage{ \S4method{alignGeometry}{Satellite}(x, template, band_codes, type, method = c("bilinear", "ngb")) \S4method{alignGeometry}{RasterStack}(x, template, method = c("bilinear", "ngb")) \S4method{alignGeometry}{RasterLayer}(x, template, method = c("bilinear", "ngb")) } \arguments{ \item{x}{Satellite or Raster* object to be resampled.} \item{template}{Raster* or spatial data set from which geometry can be extracted.} \item{band_codes}{Band ID(s) to be resampled. If not supplied and type is not given, too, all bands will be considered for resampling.} \item{type}{Type of bands (e.g. VIS, NIR) which should be considered. If not supplied, all types will be processed depending and bands to be processed can be defined by band_codes.} \item{method}{Method for resampling; "bilinear" for bilinear interpolation (default) or "ngb" for nearest neighbor interpolation. See e.g. \code{\link{resample}}, \code{\link{projectRaster}}.} } \value{ Satellite object with aligned geometries. raster::RasterStack object with aligned layers raster::RasterLayer object with aligned layer } \description{ Align raster data by bringing it in the same geometry and extent. If the data set is not in the same projection as the template, the alignment will be computed by reprojection. If the data has already the same projection, the data set will be cropped and aggregated prior to resampling in order to reduce computation time. } \examples{ path <- system.file("testdata/LC8", package = "satellite") files <- list.files(path, pattern = glob2rx("LC8*.TIF"), full.names = TRUE) sat <- satellite(files) alignGeometry(sat, template = getSatDataLayer(sat, "B008n"), band_codes = "B001n") } satellite/man/lutInfo.Rd0000644000176200001440000000670713572467605014753 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lutInfo.R \name{lutInfo} \alias{lutInfo} \alias{lutInfoBandsFromSID} \alias{lutInfoSensorFromSID} \alias{lutInfoBCDEFromBID} \alias{lutInfoBIDFromBCDE} \alias{lutInfoRSRromSID} \alias{lutInfoSIDfromFilename} \alias{lutInfoSGRPfromFilename} \title{Get or access internal LUT values used by various functions} \usage{ lutInfo() lutInfoBandsFromSID(sid) lutInfoSensorFromSID(sid) lutInfoBCDEFromBID(sid, bid) lutInfoBIDFromBCDE(bcde, sid) lutInfoRSRromSID(sid) lutInfoSIDfromFilename(files) lutInfoSGRPfromFilename(file) } \arguments{ \item{sid}{Sensor id as returned e.g. from \code{\link{lutInfoSensorFromSID}}.} \item{bid}{Band id as returned e.g. from \code{\link{lutInfoBIDFromBCDE}}.} \item{bcde}{Band code as returned e.g. from \code{\link{lutInfoBCDEFromBID}}.} \item{files}{Filename (or filepath) of one or more remote sensing data filenames} \item{file}{Filename of a remote sensing data file} } \value{ List containing several \code{data.frame} objects with LUT values. } \description{ Get internal look-up table (LUT) values from sysdata.rda which have been compiled using data-raw/lut_data.R. Metadata is stored in \code{lut$meta}. } \details{ The functions above return the following information: \itemize{ \item \code{lutInfoBandsFromSID} returns the band info block. \item \code{lutInfoBCDEFromBID} returns the band code. \item \code{lutInfoBIDFromBCDE} returns the band ids. \item \code{lutInfoRSRromSID} returns the relative spectral response (rsr) for the sensor. \item \code{lutInfoSensorFromSID} returns the sensor name. } The LUT contains the following band information taken, if not specified otherwise, from the \href{https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products}{USGS Landsat FAQ}: \describe{ \item{l4_band_wl}{Minimum/maximum wavelength for Landsat 4 bands.} \item{l5_band_wl}{Minimum/maximum wavelength for Landsat 5 bands.} \item{l7_band_wl}{Minimum/maximum wavelength for Landsat 7 bands.} \item{l8_band_wl}{Minimum/maximum wavelength for Landsat 8 bands.} \item{l7_rsr}{Landat 7 rsr (nm-1) taken from the \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} of the USGS Landsat FAQ.} \item{l8_rsr}{Landat 8 rsr (nm-1) taken from the \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} of the USGS Landsat FAQ.} \item{solar}{Solar irradiance (W m-2 nm-1) taken from the \href{http://rredc.nrel.gov/solar/spectra/am0/modtran.html}{National Renewable Energy Laboratory}.} \item{l7_esun}{Tabulated ESun values from \href{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}{tab 11.3 (Thuillier spectrum)} of the Landsat7 handbook.} \item{l5_esun, l4_esun}{Tabulated ESun values from \href{ftp://ftp.vt.tpu.ru/study/Tokareva/public/English_prof/GIS/L5TMLUTIEEE2003.pdf}{Chander G, Markham B (2003) Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transaction on Geoscience and Remote Sensing 41/11, doi:10.1109/LGRS.2007.898285}.} } } \section{Functions}{ \itemize{ \item \code{lutInfoBandsFromSID}: \item \code{lutInfoSensorFromSID}: \item \code{lutInfoBCDEFromBID}: \item \code{lutInfoBIDFromBCDE}: \item \code{lutInfoRSRromSID}: \item \code{lutInfoSIDfromFilename}: \item \code{lutInfoSGRPfromFilename}: }} \examples{ ls_li <- lutInfo() # str(ls_li) } satellite/man/crop.Rd0000644000176200001440000000315313572464203014255 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/crop.R \name{crop} \alias{crop} \alias{crop,Satellite-method} \title{Crop Satellite object} \usage{ \S4method{crop}{Satellite}(x, y, subset = TRUE, snap = "near") } \arguments{ \item{x}{Satellite object.} \item{y}{\code{\link{extent}} object.} \item{subset}{Logical; if \code{TRUE} (default), all layers but the cropped ones are being dropped; if \code{FALSE}, cropped layers are appended to the Satellite object.} \item{snap}{Direction towards which to align the extent as \code{character}. Available options are \code{"near"} (default), \code{"in"} and \code{"out"} (see \code{\link[raster]{alignExtent}}).} } \value{ A Satellite object consisting of cropped layers only. If \code{subset = FALSE}, a Satellite object with the cropped layers appended. } \description{ The function is a wrapper around the \code{\link{crop}} function to easily crop a Satellite object by an \code{\link{extent}} object. } \details{ Crop layers of a Satellite object to the size of a given \code{raster::extent} object. } \examples{ \dontrun{ ## sample data path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## geographic extent of georg-gassmann-stadium (utm 32-n) ext_ggs <- raster::extent(484015, 484143, 5627835, 5628020) ## crop satellite object by specified extent sat_ggs <- crop(sat, ext_ggs) plot(sat) plot(sat_ggs) } } \references{ Please refer to the respective functions for references. } \seealso{ This function is a wrapper for \code{raster::crop}. } satellite/man/Satellite-class.Rd0000644000176200001440000000045013573376467016357 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AAAsatelliteClasses.R \docType{class} \name{Satellite-class} \alias{Satellite-class} \title{An S4 class to represent a complete satellite dataset} \description{ An S4 class to represent a complete satellite dataset } satellite/man/compMetaLandsat.Rd0000644000176200001440000000442713572464203016373 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compMetaLandsat.R \name{compMetaLandsat} \alias{compMetaLandsat} \title{Get calibration information from Landsat 8 standard level 1B/T filename} \usage{ compMetaLandsat(files) } \arguments{ \item{files}{Path and filename of the Landsat metadata file.} } \value{ \code{data.frame} containing the following information for each band/layer: \itemize{ \item DATE date (e.g. 2013-07-07) \item SID sensor id (e.g. LC8) \item SENSOR sensor name (e.g. Landsat 8) \item SGRP sensor group (e.g. Landsat) \item BID band id (e.g. 7) \item BCDE band code (5 digit standard name, e.g B001n) \item SRES spatial resolution of the sensor band (e.g. 30 for 30 m x 30m) \item TYPE type of the sensor band regarding wavelength (e.g. VIS) \item SPECTRUM spectral range regarding radiation source (e.g. solar) \item CALIB type of applied calibration (e.g. SC for scaled counts) \item RID region id (e.g. R00001) for multi region Satellite objects \item RADA addtition coefficient for radiance conversion \item RADM multiplication coefficient for radiance conversion \item REFA addtition coefficient for reflectance conversion \item REFM multiplication coefficient for reflectance conversion \item BTK1 brightness temperature correction parameter \item BTK2 brightness temperature correction parameter \item SZEN sun zenith angle \item SAZM sun azimuth angle \item SELV sun elevation angle \item ESD earth-sun distance (AU) \item LMIN Minimum wavelength of the band (micrometer) \item LMAX Maximum wavelength of the band (micrometer) \item RADMIN Minimum radiance recorded by the band \item RADMAX Maximum radiance recorded by the band \item REFMIN Minimum reflectance recorded by the band \item REFMAX Maximum reflectance recorded by the band \item LNBR Layer number from 1 to n layers \item LAYER Layer name \item FILE Filepath of the data file \item METAFILE Filepath of the metadata file } } \description{ The function scans a Lansat metadata file for various calibration and orbit coefficients as well as some sensor specific data. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) compMetaLandsat(files) } satellite/man/subset.Rd0000644000176200001440000000202313572464203014612 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/subset.R \name{subset} \alias{subset} \alias{subset,Satellite-method} \alias{[[,Satellite,ANY,ANY-method} \title{Subset of Satellite object data layers} \usage{ \S4method{subset}{Satellite}(x, sid, cid) \S4method{[[}{Satellite,ANY,ANY}(x, i) } \arguments{ \item{x}{Satellite object providing the source band(s) to be adjusted.} \item{sid}{Band numbers or bcde which should be extracted} \item{cid}{Calibration information used for subsetting (only works if sid is not supplied to the function)} \item{i}{Layer index(es) for subsetting.} } \value{ A Satellite object A Satellite object A Satellite object } \description{ Create a subset of data layers from a \code{Satellite} object and return it as a standalone \code{Satellite} object. } \examples{ ## sample data path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat[[2:5]] subset(sat, cid = "SC") } satellite/man/maskInvarFeatures.Rd0000644000176200001440000000532313572464203016745 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/maskInvarFeatures.R \name{maskInvarFeatures} \alias{maskInvarFeatures} \alias{maskInvarFeatures,Satellite-method} \alias{maskInvarFeatures,RasterStack-method} \alias{maskInvarFeatures,RasterLayer-method} \title{Identify pseudo-invariant features from a satellite scene} \usage{ \S4method{maskInvarFeatures}{Satellite}(x) \S4method{maskInvarFeatures}{RasterStack}(x, quant = 0.01, id_vis = 1L, id_nir = 2L, id_swir = 3L) \S4method{maskInvarFeatures}{RasterLayer}(x, nir, swir, quant = 0.01) } \arguments{ \item{x}{A Satellite object or a \code{raster::RasterLayer} providing the sensor's vis band.} \item{quant}{A value v = [0...1] which is used to define the percentage threshold values (thv) for invariant features (nir/vis ratio < thv, swir band values > 1-thv).} \item{id_vis}{Index of the visible band.} \item{id_nir}{Index of the near infravis band.} \item{id_swir}{Index of the short-wave infravis band.} \item{nir}{A \code{raster::RasterLayer} containing the sensor's nir band.} \item{swir}{A \code{raster::RasterLayer} containing the sensor's swir band.} } \value{ If x is a Satellite object, a Satellite object with added layer; \cr if x is a \code{raster::RasterLayer} object, a a \code{raster::RasterLayer} object with added layers (1 indicates invariant pixels, 0 otherwise). } \description{ Identify pseudo-invariant features from a satellite scene based on a vis, near infravis and short-wave infravis band. } \details{ Invariant features are identified as pixels which belong to the group of (i) the n lowest VIS/NIR ratios and of (ii) the highest n SWIR values. The value of n is given by the parameter quant = [0...1]. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- maskInvarFeatures(sat) maskInvarFeatures(x = getSatDataLayer(sat, "B004n"), nir = getSatDataLayer(sat, "B005n"), swir = getSatDataLayer(sat, "B007n")) ## when dealing with a 'RasterStack' rst <- stack(files[c(6, 7, 9)]) maskInvarFeatures(rst) } \references{ This function is taken and only slightly modified from the PIF function by Sarah C. Goslee (2011). Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software,43(4), 1-25. URL \url{http://www.jstatsoft.org/v43/i04/}. The underlying theory has been published by Schott RJ, Salvaggio C and Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment 26/1, doi:10.1016/0034-4257(88)90116-2, available online at \url{http://www.sciencedirect.com/science/article/pii/0034425788901162}. } satellite/man/SatelliteMetaData-class.Rd0000644000176200001440000000060113573376467017756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AAAsatelliteClasses.R \docType{class} \name{SatelliteMetaData-class} \alias{SatelliteMetaData-class} \title{An S4 class to represent satellite metadata} \description{ An S4 class to represent satellite metadata } \section{Slots}{ \describe{ \item{\code{meta}}{a data frame object containing the data} }} satellite/man/compFilePathLandsat.Rd0000644000176200001440000000245313572464203017176 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compFilePathLandsat.R \name{compFilePathLandsat} \alias{compFilePathLandsat} \alias{sortFilesLandsat} \title{Get filename, bands and metadata file for Landsat 7 and 8 standard 1B/T format} \usage{ compFilePathLandsat(files) sortFilesLandsat(files, id = FALSE) } \arguments{ \item{files}{Path and filename(s) of one or more Landsat band files or, alternatively, one or more Landsat metadata files.} \item{id}{\code{logical}, defaults to \code{FALSE}. Determines whether to return sorted band files (ie default) or sorting order.} } \value{ \code{data.frame} containing filepaths, band numbers and metadata filepaths. If \code{id = FALSE} (default), sorted band files as \code{character}, else the corresponding sorting order as \code{integer}. } \description{ The function compiles the sensor, band, filename and metadata filename information for standard level 1B/T Landsat files. } \section{Functions}{ \itemize{ \item \code{sortFilesLandsat}: Sort Landsat band files in ascending order. }} \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) compFilePathLandsat(files) sortFilesLandsat(files) sortFilesLandsat(files, id = TRUE) # indices } satellite/man/convSC2Rad.Rd0000644000176200001440000000415113572464203015215 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/convSC2Rad.R \name{convSC2Rad} \alias{convSC2Rad} \alias{convSC2Rad,Satellite-method} \alias{convSC2Rad,RasterStack-method} \alias{convSC2Rad,RasterLayer-method} \title{Convert a band's scaled counts to radiance} \usage{ \S4method{convSC2Rad}{Satellite}(x, szen_correction = "TRUE", subset = FALSE) \S4method{convSC2Rad}{RasterStack}(x, mult, add, szen) \S4method{convSC2Rad}{RasterLayer}(x, mult, add, szen) } \arguments{ \item{x}{An object of class Satellite, raster::RasterStack or raster::RasterLayer providing scaled counts (DNs).} \item{szen_correction}{Logical; if \code{TRUE}, sun zenith correction is being applied.} \item{subset}{Logical; if \code{TRUE}, all layers but the cropped ones are being dropped; if \code{FALSE} (default), cropped layers are appended to the Satellite object.} \item{mult}{Multiplicative coefficient for value transformation (i.e. slope).} \item{add}{Additive coefficient for value transformation (i.e. offset).} \item{szen}{Cosine of solar zenith angle.} } \value{ If x is a Satellite object, a Satellite object with added converted layers; \cr if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with converted layer(s). } \description{ Convert a band's scaled counts to radiance using a simple linear conversion without any kind of atmospheric correction etc. } \details{ The conversion functions are taken from USGS' Landsat 8 Data Users Handbook which is available online at \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- convSC2Rad(sat) # If you use a raster layer, supply required meta information bcde <- "B002n" convSC2Rad(x = getSatDataLayer(sat, bcde), mult = getSatRADM(sat, bcde), add = getSatRADA(sat, bcde)) } \seealso{ \code{\link{calcAtmosCorr}} for conversions of scaled counts to physical units including a scene-based atmospheric correction. } satellite/man/SatelliteLog-class.Rd0000644000176200001440000000062213573376467017022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AAAsatelliteClasses.R \docType{class} \name{SatelliteLog-class} \alias{SatelliteLog-class} \title{An S4 class to represent satellite log data} \description{ An S4 class to represent satellite log data } \section{Slots}{ \describe{ \item{\code{log}}{a list object containing information on individual processing steps} }} satellite/man/calcPathRadDOS.Rd0000644000176200001440000001555013572467605016043 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcPathRadDOS.R \name{calcPathRadDOS} \alias{calcPathRadDOS} \alias{calcPathRadDOS,Satellite-method} \alias{calcPathRadDOS,numeric-method} \title{Compute path radiance based on the dark object method} \usage{ \S4method{calcPathRadDOS}{Satellite}(x, model = c("DOS2", "DOS4"), esun_method = "RadRef") \S4method{calcPathRadDOS}{numeric}( x, bnbr, band_wls, radm, rada, szen, esun, model = c("DOS2", "DOS4"), scat_coef = c(-4, -2, -1, -0.7, -0.5), dos_adjust = 0.01 ) } \arguments{ \item{x}{A Satellite object or the value (scaled count) of a dark object in \code{bnbr} (e.g. minimum raw count of selected raster \code{bnbr}). If x is a Satellite object, the value is computed using \code{\link{calcDODN}}.} \item{model}{Model to be used to correct for 1\% scattering (DOS2, DOS4; must be the same as used by \code{\link{calcAtmosCorr}}).} \item{esun_method}{If x is a Satellite object, name of the method to be used to compute esun using one of \code{\link{calcTOAIrradRadRef}} ("RadRef"), \code{\link{calcTOAIrradTable}} ("Table") or \code{\link{calcTOAIrradModel}} ("Model")} \item{bnbr}{Band number for which DNmin is valid.} \item{band_wls}{Band wavelengths to be corrected; \code{data.frame} with min (max) in first (second) column, see details.} \item{radm}{Multiplicative coefficient for radiance transformation (i.e. slope).} \item{rada}{Additive coefficient for radiance transformation (i.e. offset).} \item{szen}{Sun zenith angle.} \item{esun}{Actual (i.e. non-normalized) TOA solar irradiance, e.g. returned by \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} or \code{\link{calcTOAIrradModel}}.} \item{scat_coef}{Scattering coefficient; defaults to -4.0.} \item{dos_adjust}{Assumed reflection for dark object adjustment; defaults to 0.01.} } \value{ Satellite object with path radiance for each band in the metadata (W m-2 micrometer-1) Vector object with path radiance values for each band (W m-2 micrometer-1) } \description{ Compute an estimated path radiance for all sensor bands, which can then be used to roughly correct the radiance values for atmospheric scattering. Path radiance estimation is based on a dark object method. } \details{ If x is a Satellite object, the minimum raw count value (x) is computed using \code{\link{calcDODN}}. If the TOA solar irradiance is not part of the Satellite object's metadata, it is computed using \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} or \code{\link{calcTOAIrradModel}}. The dark object subtraction approach is based on an approximation of the atmospheric path radiance (i.e. upwelling radiation which is scattered into the sensors field of view, aka haze) using the reflectance of a dark object (i.e. reflectance ~1\%). Chavez (1988) proposed a method which uses the dark object reflectance in one band to predict the corresponding path radiances in all other \code{band_wls}. This is done using a relative radiance model which depends on the wavelength and overall atmospheric optical thickness (which is estimated based on the dark object's DN value). This has the advantage that the path radiance is actually correlated across different sensor \code{band_wls} and not computed individually for each band using independent dark objects. He proposed a relative radiance model which follows a wavelength dependent scattering that ranges from a power of -4 over -2, -1, -0.7 to -0.5 for very clear over clear, moderate, hazy to very hazy conditions. The relative factors are computed individually for each 1/1000 wavelength within each band range and subsequently averaged over the band as proposed by Goslee (2011). The atmospheric transmittance towards the sensor (Tv) is approximated by 1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992) The atmospheric transmittance from the sun (Tz) is approximated by the cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh scattering (DOS4, Moran et al. 1992). The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996) or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992). Equations for the path radiance are taken from Song et al. (2001). For some sensors, the band wavelengths are already included. See \code{lutInfo()[grepl("_BANDS", names(lutInfo()$META))]} if your sensor is included. To retrieve a sensor, use \code{lutInfo()$_BANDS}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- calcTOAIrradModel(sat) bds <- "B002n" val <- calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bds))), bnbr = getSatLNBR(sat, bds), band_wls = data.frame(LMIN = getSatLMIN(sat, getSatBCDESolar(sat)), LMAX = getSatLMAX(sat, getSatBCDESolar(sat))), radm = getSatRADM(sat, getSatBCDESolar(sat)), rada = getSatRADA(sat, getSatBCDESolar(sat)), szen = getSatSZEN(sat, getSatBCDESolar(sat)), esun = getSatESUN(sat, getSatBCDESolar(sat)), model = "DOS2", scat_coef = -4) val } \references{ Chavez Jr PS (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at \url{http://www.sciencedirect.com/science/article/pii/0034425788900193}. Chavez Jr PS (1996) Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62/9, available online at \url{https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved}. Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software, 43/4, 1-25, available online at \url{http://www.jstatsoft.org/v43/i04/}. Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evlauation of simplified procedures for rretrieval of land surface reflectane factors from satellite sensor output.Remote Sensing of Environment 41/2-3, 169-184, doi:10.1016/0034-4257(92)90076-V, available online at \url{http://www.sciencedirect.com/science/article/pii/003442579290076V}. Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment 75/2, doi:10.1016/S0034-4257(00)00169-3, available online at \url{http://www.sciencedirect.com/science/article/pii/S0034425700001693} If you refer to Sawyer and Stephen 2014, please note that eq. 5 is wrong. } \seealso{ This function is used by \code{\link{calcAtmosCorr}} to compute the path radiance. } satellite/man/demTools.Rd0000644000176200001440000000332713572464203015103 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/demTools.R \name{demTools} \alias{demTools} \alias{demTools,Satellite-method} \alias{demTools,RasterLayer-method} \title{Compute terrain characteristics from digital elevation models} \usage{ \S4method{demTools}{Satellite}(x, method = "hillShade", bcde = "DEM") \S4method{demTools}{RasterLayer}(x, sunElev, sunAzim, method = "hillShade") } \arguments{ \item{x}{A DEM provided as an object of class Satellite or RasterLayer.} \item{method}{Currently "slope", "aspect" and "hillshade" are implemented.} \item{bcde}{The name of the DEM layer in the Satellite object.} \item{sunElev}{If \code{method = "hillShade"}, the elevation angle of the sun in degrees. See parameter \code{angle} in \code{\link{hillShade}}.} \item{sunAzim}{If \code{method = "hillShade"}, the sun azimuth angle in degree. See parameter \code{direction} in \code{\link{hillShade}}.} } \value{ If x is a Satellite object, a Satellite object with added layer containing calculated terrain information; if x is a \code{raster::RasterLayer} object, a \code{raster::RasterLayer} object with calculated terrain information. } \description{ Compute terrain characteristics from digital elevation models (DEM) using \code{raster::terrain} or \code{raster::hillShade}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## dem files_dem <- list.files(path, pattern = "DEM", full.names = TRUE) DEM <- raster(files_dem) sat <- addSatDataLayer(sat, data = DEM, info = NULL, bcde = "DEM", in_bcde="DEM") sat <- demTools(sat) } \seealso{ \code{raster::terrain}, \code{raster::hillShade}. } satellite/man/extend.Rd0000644000176200001440000000264113572464203014602 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/extend.R \name{extend} \alias{extend} \alias{extend,Satellite-method} \title{Extend a Satellite object} \usage{ \S4method{extend}{Satellite}(x, y, subset = TRUE, value = NA) } \arguments{ \item{x}{Satellite object.} \item{y}{Target \code{Extent}, see \code{\link[raster]{extent}}.} \item{subset}{Logical. If \code{TRUE} (default), all layers but the extended ones are being dropped, else the extended layers are appended to the initial Satellite object.} \item{value}{Fill value assigned to new cells passed to \code{\link[raster]{extend}}, defaults to \code{NA}.} } \value{ A Satellite object consisting of extended layers only or, if \code{subset = FALSE}, a Satellite object with the extended layers appended. } \description{ The function is a wrapper around \code{\link[raster]{extend}} to easily extend a Satellite object to a larger spatial extent. } \examples{ \dontrun{ ## sample data path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## geographic extent of georg-gassmann-stadium (utm 32-n) ext_ggs <- raster::extent(482606.4, 482781.4, 5627239, 5627489) ## extend satellite object by specified extent sat_ggs <- extend(sat, ext_ggs) plot(sat) plot(sat_ggs) } } \seealso{ This function is a wrapper around \code{\link[raster]{extend}}. } satellite/man/calcTOAIrradRadRef.Rd0000644000176200001440000000574213572464203016634 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcTOAIrradRadRef.R \name{calcTOAIrradRadRef} \alias{calcTOAIrradRadRef} \alias{calcTOAIrradRadRef,Satellite-method} \alias{calcTOAIrradRadRef,numeric-method} \title{Compute top of atmosphere solar irradiance using radiation vs. reflection} \usage{ \S4method{calcTOAIrradRadRef}{Satellite}(x, normalize = TRUE, esd) \S4method{calcTOAIrradRadRef}{numeric}(x, ref_max, normalize = TRUE, esd) } \arguments{ \item{x}{A Satellite object or the maximum radiance of satellite band(s) as numeric object.} \item{normalize}{Logical; if \code{TRUE}, ESun is normalized to mean earth-sun distance.} \item{esd}{Earth-sun distance (AU, can be estimated using \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not supplied and necessary for normalization, it is tried to take it from the metadata, otherwise it is estimated by the day of the year using \code{\link{calcEarthSunDist}}.} \item{ref_max}{Maximum reflextance of satellite band(s).} } \value{ If x is a Satellite object, a Satellite object with ESun information added to the metadata; if x is numeric, a vector containing ESun for the respective band(s). } \description{ Compute extraterrestrial solar irradiance (ESun) using the actual maximum radiation and reflection values within each band. } \details{ The actual solar irradiance is computed using the following formula taken from the GRASS GIS \href{https://grass.osgeo.org/grass64/manuals/i.landsat.toar.html}{i.landsat.toar} module \deqn{ESun = (pi d^2) RADIANCE_MAXIMUM / REFLECTANCE_MAXIMUM} where d is the earth-sun distance (in AU) and RADIANCE_MAXIMUM and REFLECTANCE_MAXIMUM are the maximum radiance and reflection values of the respective band. All these parameters are taken from the scene's metadata file if a Satellite object is passed to the function. By default, the resulting actual ESun will be normalized to a mean earth-sun distance to be compatible with other default results from \code{\link{calcTOAIrradTable}} or \code{\link{calcTOAIrradModel}}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- calcTOAIrradModel(sat) getSatESUN(sat) calcTOAIrradRadRef(x = getSatRadMax(sat, getSatBCDESolar(sat)), ref_max = getSatRefMax(sat, getSatBCDESolar(sat)), normalize = FALSE, esd = calcEarthSunDist("2015-01-01")) } \seealso{ \code{\link{calcTOAIrradTable}} for tabulated solar irradiance values from the literature or \code{\link{calcTOAIrradModel}} for the computation of the solar irradiance based on look-up tables for the sensor's relative spectral response and solar irradiation spectral data. See \code{\link{calcEarthSunDist}} for calculating the earth-sun distance based on the day of the year which is called by this function if ESun should be corrected for actual earth-sun distance. } satellite/man/calcTOAIrradTable.Rd0000644000176200001440000000546113572467256016531 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcTOAIrradTable.R \name{calcTOAIrradTable} \alias{calcTOAIrradTable} \alias{calcTOAIrradTable,Satellite-method} \alias{calcTOAIrradTable,factor-method} \alias{calcTOAIrradTable,character-method} \title{Get top of atmosphere solar irradiance using readily tabulated values} \usage{ \S4method{calcTOAIrradTable}{Satellite}(x, normalize = TRUE, esd) \S4method{calcTOAIrradTable}{factor}(x, normalize = TRUE, esd) \S4method{calcTOAIrradTable}{character}(x, normalize = TRUE, esd) } \arguments{ \item{x}{A Satellite object or sensor id ("LT4, LT5, LE7") as character.} \item{normalize}{Logical; if \code{TRUE}, ESun is normalized to mean earth-sun distance.} \item{esd}{Earth-sun distance (AU, can be estimated using \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not supplied and necessary for normalization, it is tried to take it from the metadata, otherwise it is estimated by the day of the year using \code{\link{calcEarthSunDist}}.} } \value{ Satellite object with ESun information added to the metadata Vector object containing ESun for the respective band(s) Vector object containing ESun for the respective band(s) } \description{ Get mean extraterrestrial solar irradiance (ESun) using published values. } \details{ Currently implemented sensors are Landsat 4, 5 and 7. If results should not be normalized to a mean earth-sun distance, the actual earth-sun distance is approximated by the day of the year using \code{\link{calcEarthSunDist}}. Please note that ESun values are not required for converting Landsat 8 data to reflectance as the corresponding metadata files provide coefficients necessary to convert digital numbers to radiance and reflectance (taken from \url{http://www.gisagmaps.com/landsat-8-atco/}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LE07*.TIF"), full.names = TRUE) sat <- satellite(files) calcTOAIrradTable(sat) calcTOAIrradTable(x = "LE7", normalize = FALSE, calcEarthSunDist("2015-01-01")) } \references{ Tabulated values of the solar irradiance for all Landsat sensors are taken from \url{https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product}. } \seealso{ \code{\link{calcTOAIrradRadRef}} for the computation of the solar irradiance based on maximum radiation and reflection values of the dataset or \code{\link{calcTOAIrradModel}} for the computation of the solar irradiance based on look-up tables for the sensor's relative spectral response and solar irradiation spectral data. See \code{\link{calcEarthSunDist}} for calculating the earth-sun distance based on the day of the year which is called by this function if ESun should be corrected for actual earth-sun distance. } satellite/man/calcDODN.Rd0000644000176200001440000000324213572464203014660 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcDODN.R \name{calcDODN} \alias{calcDODN} \alias{calcDODN,Satellite-method} \alias{calcDODN,RasterLayer-method} \title{Compile dark object DN for given sensor band} \usage{ \S4method{calcDODN}{Satellite}(x, bcde) \S4method{calcDODN}{RasterLayer}(x) } \arguments{ \item{x}{Satellite object or RasterLayer with sensor band data, e.g. returned by \code{\link{getSatDataLayer}}.} \item{bcde}{If 'x' is a Satellite object, a band code as character.} } \value{ Numeric value of the DN for the dark object. } \description{ The function estimates the DN value of a "dark object" which is used for atmospheric correction using the DOS2 and DOS4 model. Therefore, the frequency distribution of the smallest 1\% of the data values is analyzed and the value for which the first derivate has the absolute maximum is taken as the DN for a dark object. } \details{ The DN for a dark object is extracted from a histogram similar to Chavez (1988). } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) calcDODN(sat, bcde = "B002n") calcDODN(getSatDataLayer(sat, bcde = "B002n")) } \references{ Chavez Jr PS (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at \url{http://www.sciencedirect.com/science/article/pii/0034425788900193} } \seealso{ The DN is used by \code{\link{calcPathRadDOS}} for computing the path radiance based on the dark object method. } satellite/man/brick.Rd0000644000176200001440000000146613572464203014411 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/brick.R \docType{methods} \name{brick} \alias{brick} \alias{brick,Satellite-method} \title{Convert selected layers of a Satellite object to a RasterBrick} \usage{ \S4method{brick}{Satellite}(x, layer = names(x), ...) } \arguments{ \item{x}{an object of class 'Satellite'} \item{layer}{character vector (bcde codes) or integer vector (index) of the layers to be stacked} \item{...}{additional arguments passed on to \code{\link{brick}}} } \description{ Convert selected layers of a Satellite object to a RasterBrick } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) brck <- brick(sat, c("B001n", "B002n", "B003n")) brck } satellite/man/satellite.Rd0000644000176200001440000000427613572467256015322 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/satellite.R \name{satellite} \alias{satellite} \alias{satellite,character-method} \alias{satellite,Raster-method} \alias{satellite,list-method} \title{Create a Satellite object} \usage{ \S4method{satellite}{character}(x, meta, log) \S4method{satellite}{Raster}(x, meta, log) \S4method{satellite}{list}(x, meta, log) } \arguments{ \item{x}{A vector of filenames, a (multi-layered) \code{Raster*} object or a \code{list} of single \code{RasterLayer} objects (see \code{\link{raster}}). In the latter case, be aware that bands must be arranged in ascending order (eg using \code{\link{sortFilesLandsat}}).} \item{meta}{Optional metadata object (e.g. returned from \code{\link{compMetaLandsat}}). If 'x' is a satellite dataset and recognised as "Landsat", then the metadata is automatically extracted from the respective meta information file if both the satellite data and the metadata file follow the USGS Earth Explorer's naming convention.} \item{log}{Optionally supply a log entry.} } \value{ A \code{Satellite} object. } \description{ Method to create a Satellite object. } \details{ A Satellite object consists of three data sections: (i) a raster data section which holds the actual data values of the respective sensor bands, (ii) a metadata grid which holds meta information for each sensor band (e.g. calibration coefficients, type of sensor band etc.) and (iii) a list of log information which records the processing history of the entire dataset. } \examples{ ## 'character' input (i.e. filenames) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) satellite(files) ## raster::RasterStack input satellite(l8) } \seealso{ (i) \code{\link{compMetaLandsat}} to get more information about the structure of the metadata component; (ii) \url{https://www.usgs.gov/faqs/what-naming-convention-landsat-collections-level-1-scenes?qt-news_science_products=0#qt-news_science_products} for detailed information about the naming conventions for Landsat scene identifiers; and (iii) \code{\link{sortFilesLandsat}} for automated rearrangement of Landsat band files. } satellite/man/convRad2Ref.Rd0000644000176200001440000000444213572464203015427 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/convRad2Ref.R \name{convRad2Ref} \alias{convRad2Ref} \alias{convRad2Ref,Satellite-method} \alias{convRad2Ref,RasterStack-method} \alias{convRad2Ref,RasterLayer-method} \title{Convert a band's scaled counts or radiance values to reflectance} \usage{ \S4method{convRad2Ref}{Satellite}(x, szen_correction = "TRUE") \S4method{convRad2Ref}{RasterStack}(x, mult, add, szen) \S4method{convRad2Ref}{RasterLayer}(x, mult, add, szen) } \arguments{ \item{x}{An object of class Satellite, raster::RasterStack or raster::RasterLayer providing radiance values.} \item{szen_correction}{Logical; if \code{TRUE}, sun zenith correction is being applied.} \item{mult}{Multiplicative coefficient for value transformation (i.e. slope).} \item{add}{Additive coefficient for value transformation (i.e. offset)} \item{szen}{Cosine of solar zenith angle.} } \value{ If x is a Satellite object, a Satellite object with added converted layers; \cr if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with converted layer(s). } \description{ Convert a band's scaled counts to reflectance using a simple linear conversion without any kind of atmospheric correction etc. } \details{ The conversion functions are taken from USGS' Landsat 8 Data Users Handbook which is available online at \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. If the sensor does not provide linear conversion coefficients for reflectance computation, the reflectance is calculated using the solar irradiance following the functions taken from USGS' Landsat 7 manual, chapter 11.3.2, which is available online at \url{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- convRad2Ref(sat) # If you use a raster layer, supply required meta information bcde <- "B002n" convRad2Ref(x = getSatDataLayer(sat, bcde), mult = getSatRADM(sat, bcde), add = getSatRADA(sat, bcde)) } \seealso{ \code{\link{calcAtmosCorr}} for conversions of scaled counts to physical units including a scene-based atmospheric correction. } satellite/man/calcTopoCorr.Rd0000644000176200001440000000473213572464203015710 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcTopoCorr.R \name{calcTopoCorr} \alias{calcTopoCorr} \alias{calcTopoCorr,Satellite-method} \alias{calcTopoCorr,RasterStackBrick-method} \alias{calcTopoCorr,RasterLayer-method} \title{Correct for topographic effects.} \usage{ \S4method{calcTopoCorr}{Satellite}(x, mask = TRUE) \S4method{calcTopoCorr}{RasterStackBrick}(x, hillsh, cloudmask = NULL, ...) \S4method{calcTopoCorr}{RasterLayer}(x, hillsh, cloudmask = NULL, ...) } \arguments{ \item{x}{\code{Satellite} or \code{Raster*} object.} \item{mask}{\code{logical}. If \code{TRUE}, the cloudmask from the \code{Satellite} object (if available) will be considered in the regression model.} \item{hillsh}{A \code{RasterLayer} created with \code{\link{hillShade}}.} \item{cloudmask}{A \code{RasterLayer} in which clouds are masked with NA values, passed to \code{\link[raster]{mask}}.} \item{...}{Additional arguments passed to \code{\link{writeRaster}}.} } \value{ If x is a Satellite object, a Satellite object with added, topographic corrected layers; if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with converted layer(s). } \description{ Correct for topographic effects. } \details{ The method of Civco (1989) is applied on atmospherically corrected bands (if not already available in the Satellite object, \code{\link{calcAtmosCorr}} is performed with its default settings.): First, an analytical hillshade image is created based on a DEM and sun elevation and sun zenith information from the metadata. A regression between the hillshade (independent variable) and each channel is then calculated with consideration of a cloudmask (if available). The regression coefficents are used to calibrate the hillshade raster (for each channel individually). Finally, the calibrated hillshade image is subtracted from the corresponding channel and the mean value of the channel is added. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## dem files_dem <- list.files(path, pattern = "DEM", full.names = TRUE) DEM <- raster(files_dem) sat <- addSatDataLayer(sat, data = DEM, info = NULL, bcde = "DEM", in_bcde="DEM") \dontrun{ sat <- calcTopoCorr(sat) } } \references{ CIVCO, D.L. (1989): Topographic normalization of Landsat Thematic Mapper digitalimagery. \emph{Photogrammetric Engineering & Remote Sensing}, 55, 1303-1309. } satellite/man/calcTOAIrradModel.Rd0000644000176200001440000000703313572467256016537 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcTOAIrradModel.R \name{calcTOAIrradModel} \alias{calcTOAIrradModel} \alias{calcTOAIrradModel,Satellite-method} \alias{calcTOAIrradModel,data.frame-method} \title{Compute top of atmosphere solar irradiance for sensor bands using LUTs} \usage{ \S4method{calcTOAIrradModel}{Satellite}(x, model = "MNewKur", normalize = TRUE, esd) \S4method{calcTOAIrradModel}{data.frame}(x, model = "MNewKur", normalize = TRUE, esd) } \arguments{ \item{x}{A Satellite object or the relative spectral response function for the respective band as \code{data.frame} (see details for structure).} \item{model}{Tabulated solar radiation model to be used (one of MCebKur_MChKur, MNewKur, MthKur, MoldKur, MODWherli_WMO, NN, see reference on tabulated solar irradiance below).} \item{normalize}{Logical; if \code{TRUE}, ESun is normalized to mean earth-sun distance.} \item{esd}{Earth-sun distance (AU, can be estimated using \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not supplied and necessary for normalization, it is tried to take it from the metadata, otherwise it is estimated by the day of the year using \code{\link{calcEarthSunDist}}.} } \value{ If x is a Satellite object, a Satellite object with ESun information added to the metadata; if x is a \code{data.frame}, a vector containing ESun for the respective band(s). } \description{ Compute mean extraterrestrial solar irradiance (ESun) using tabulated mean solar spectral data and the band specific relative spectral response (rsr) functions. } \details{ Computation of ESun is taken from Updike and Comp (2011). Tabulated values for mean earth-sun distance are taken from the data sources mentioned in the references. If results should not be normalized to a mean earth-sun distance, the actual earth-sun distance is approximated by the day of the year using \code{\link{calcEarthSunDist}}. Relative spectral response values have to be supplied as a \code{data.frame} which has at least the following three columns: (i) a column "Band" for the sensor band number (i.e. 1, 2, etc.), (ii) a column "WAVELENGTH" for the WAVELENGTH data in full nm steps, and (iii) a column "RSR" for the response information [0...1]. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- calcTOAIrradModel(sat) getSatESUN(sat) lut <- lutInfo() calcTOAIrradModel(lut$L8_RSR, model = "MNewKur", normalize = FALSE, esd = calcEarthSunDist("2015-01-01")) } \references{ Updike T, Comp C (2011) Radiometric use of WorldView-2 imagery. Technical Note, available online at \url{http://www.pancroma.com/downloads/Radiometric_Use_of_WorldView-2_Imagery.pdf}. Tabulated relative spectral response functions (nm-1) are taken from the \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} of the USGS Landsat FAQ. Tabulated solar irradiance (W m-2 nm-1) is taken from the \href{http://rredc.nrel.gov/solar/spectra/am0/modtran.html}{National Renewable Energy Laboratory}. } \seealso{ \code{\link{calcTOAIrradTable}} for tabulated solar irradiance values from the literature or \code{\link{calcTOAIrradRadRef}} for the computation of the solar irradiance based on maximum radiation and reflection values of the dataset. See \code{\link{calcEarthSunDist}} for calculating the earth-sun distance based on the day of the year which is called by this function if ESun should be corrected for actual earth-sun distance. } satellite/man/calcEarthSunDist.Rd0000644000176200001440000000451113572464203016511 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcEarthSunDist.R \name{calcEarthSunDist} \alias{calcEarthSunDist} \title{Compute earth-sun distance based on day of the year} \usage{ calcEarthSunDist(date, formula = c("Spencer", "Mather", "ESA", "Duffie")) } \arguments{ \item{date}{Date of the sensor overpass; either a character string in a native date format (e.g. "YYYY-MM-DD", see \code{\link{as.Date}}) or a POSIX* object (see \code{\link{as.POSIXct}}).} \item{formula}{Formula to be applied, specified through the name of the author, i.e. one of "Spencer", "Mather", "ESA" or "Duffie" (see 'Details').} } \value{ Numeric earth-sun distance (in AU) or, if \code{formula = "Duffie"}, the relative squared earth--sun distance on the given day. } \description{ The earth-sun distance for a particular day of the year is computed based on one of several empirical formulas. } \details{ Computation of earth-sun distance using formulas provided by Spencer (1971), Mather (2005) or ESA. If \code{formula = "Duffie"}, the inverse squared relative earth--sun distance is returned as proposed by Duffie and Beckman (1980). } \examples{ calcEarthSunDist(date = "2015-01-01", formula = "Spencer") # absolute calcEarthSunDist(date = "2015-01-01", formula = "Duffie") # relative } \references{ The formulas are taken from the following sources: \itemize{ \item Spencer: Spencer JW (1971) Fourier series representation of the position of the sun. Search 2/5. Taken from \url{https://goo.gl/lhi9UI}. \item Mather: Mather PM (2005) Computer Processing of Remotely-Sensed Images: An Introduction. Wiley: Chichester, ISBN: 978-0-470-02101-9, \url{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470021012.html}. \item ESA: ESA Earth Observation Quality Control: Landsat frequently asked questions. \item Duffie: Duffie JA, Beckman WA (2013) Solar Engineering of Thermal Processes. Wiley: Hoboken, New Jersey, ISBN: 978-0-470-87366-3, \url{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470873663.html}. } See also: Bird R, Riordan C (1984) Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at the Earth's surface for cloudless atmospheres. Task No. 3434.10, Solar Energy Research Institute: Golden, Colorado, \url{http://www.nrel.gov/docs/legosti/old/2436.pdf}. } satellite/man/plot.Rd0000644000176200001440000000227413572464203014273 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plot} \alias{plot} \alias{plot,Satellite,ANY-method} \alias{plot,Satellite-method} \title{Plot a Satellite object} \usage{ \S4method{plot}{Satellite,ANY}(x, bcde = NULL, col = grDevices::grey.colors(100), ...) } \arguments{ \item{x}{A 'Satellite' object, usually returned by \code{\link{satellite}}.} \item{bcde}{Band codes to be visualized, e.g. returned by \code{\link{getSatBCDE}}. If not supplied, the initial (up to) 16 layers are being visualized.} \item{col}{Color scheme.} \item{...}{Further arguments passed on to \code{\link{plot.default}}.} } \description{ This is the standard plotting routine for the 'Satellite' class. Layers are drawn either from the start (default; limited to a maximum of 16 sub-plots) or according to the speficied band codes. } \examples{ \dontrun{ ## sample data path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## display data without quality flag layer bds <- getSatBCDE(sat)[1:11] plot(sat, bcde = bds) } } \seealso{ \code{\link{plot.default}}, \code{\link{par}}. } satellite/man/convRef2RadLinear.Rd0000644000176200001440000000213213572464203016554 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/convRef2RadLinear.R \name{convRef2RadLinear} \alias{convRef2RadLinear} \title{Convert reflectance to radiance using linear function coefficients} \usage{ convRef2RadLinear(band, refm, refa, radm, rada, szen) } \arguments{ \item{band}{raster::RasterStack or raster::RasterLayer containing reflectance.} \item{refm}{Multiplication coefficient for reflectance conversion.} \item{refa}{Addtition coefficient for reflectance conversion.} \item{radm}{Multiplication coefficient for radiance conversion.} \item{rada}{Addition coefficient for radiance conversion.} \item{szen}{Sun zenith angle.} } \value{ \code{raster::Raster*} object with converted values. } \description{ The function converts the reflectance (ref) back to radiance (rad) given that linear conversion coefficients for both radiance and reflectance are available. } \details{ The conversion functions are taken from USGS' Landsat 8 Data Users Handbook which is available online at \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. } satellite/man/satInfo.Rd0000644000176200001440000002173013572464203014716 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/satInfo.R \name{satInfo} \alias{satInfo} \alias{getSatDataLayers} \alias{getSatDataLayer} \alias{getSatMeta} \alias{getSatMetaBCDETemplate} \alias{getSatLog} \alias{setSatBCDE} \alias{createSatBCDE} \alias{addSatMetaParam} \alias{addSatMetaEntry} \alias{addSatLog} \alias{addSatDataLayer} \alias{addRasterMeta2Sat} \alias{createRasterMetaData} \alias{updateRasterMetaData} \alias{countSatDataLayers} \alias{getSatParam} \alias{getSatBCDE} \alias{getSatBID} \alias{getSatSID} \alias{getSatSensor} \alias{getSatSensorGroup} \alias{getSatSensorInfo} \alias{getSatSpectrum} \alias{getSatBCDESolar} \alias{getSatBCDEThermal} \alias{getSatXRes} \alias{getSatYRes} \alias{getSatRes} \alias{getSatType} \alias{getSatCalib} \alias{getSatBCDEType} \alias{getSatBCDEFromType} \alias{getSatBCDEFromSpectrum} \alias{getSatBCDESres} \alias{getSatBCDECalib} \alias{getSatBCDESolarCalib} \alias{getSatBCDEThermalCalib} \alias{getSatBandInfo} \alias{getSatRadMax} \alias{getSatRadMin} \alias{getSatRefMax} \alias{getSatRefMin} \alias{getSatESD} \alias{getSatESUN} \alias{getSatSZEN} \alias{getSatSAZM} \alias{getSatSELV} \alias{getSatMetaLayer} \alias{getSatLayerfromData} \alias{getSatLNBR} \alias{getSatLMIN} \alias{getSatLMAX} \alias{getSatRADA} \alias{getSatRADM} \alias{getSatREFA} \alias{getSatREFM} \alias{getSatBTK1} \alias{getSatBTK2} \alias{getSatPRAD} \alias{getSatDATE} \alias{getSatProjection} \title{Get or access Satellite object information used by various functions} \usage{ getSatDataLayers(sat, bcde = NULL) getSatDataLayer(sat, bcde) getSatMeta(sat, bcde) getSatMetaBCDETemplate(sat, bcde) getSatLog(sat) setSatBCDE(sat, bcde) createSatBCDE(sat, width = 3, flag = 0, prefix = "B", postfix = "n") addSatMetaParam(sat, meta_param) addSatMetaEntry(sat, meta_param) addSatLog( sat, info = NA_character_, in_bcde = NA_character_, out_bcde = NA_character_ ) addSatDataLayer(sat, bcde, data, meta_param, info, in_bcde) addRasterMeta2Sat(sat) createRasterMetaData(rst) updateRasterMetaData(sat, bcde) countSatDataLayers(sat) getSatParam(sat, param, bcde, return_bcde = TRUE) getSatBCDE(sat, lnbr) getSatBID(sat, bcde) getSatSID(sat) getSatSensor(sat) getSatSensorGroup(sat) getSatSensorInfo(sat) getSatSpectrum(sat, bcde) getSatBCDESolar(sat) getSatBCDEThermal(sat) getSatXRes(sat, bcde) getSatYRes(sat, bcde) getSatRes(sat, bcde) getSatType(sat, bcde) getSatCalib(sat, bcde) getSatBCDEType(sat, bcde, type) getSatBCDEFromType(sat, type = "VIS") getSatBCDEFromSpectrum(sat, spectrum = "solar") getSatBCDESres(sat, bcde, type) getSatBCDECalib(sat, bcde, calib) getSatBCDESolarCalib(sat, bcde, calib) getSatBCDEThermalCalib(sat, bcde, calib) getSatBandInfo(sat, bcde, return_calib = TRUE) getSatRadMax(sat, bcde) getSatRadMin(sat, bcde) getSatRefMax(sat, bcde) getSatRefMin(sat, bcde) getSatESD(sat) getSatESUN(sat, bcde) getSatSZEN(sat, bcde) getSatSAZM(sat, bcde) getSatSELV(sat, bcde) getSatMetaLayer(sat, bcde) getSatLayerfromData(sat, bcde, nbr) getSatLNBR(sat, bcde) getSatLMIN(sat, bcde) getSatLMAX(sat, bcde) getSatRADA(sat, bcde) getSatRADM(sat, bcde) getSatREFA(sat, bcde) getSatREFM(sat, bcde) getSatBTK1(sat, bcde) getSatBTK2(sat, bcde) getSatPRAD(sat, bcde) getSatDATE(sat, bcde) getSatProjection(sat, bcde) } \arguments{ \item{sat}{Satellite object (see \code{\link{satellite}}).} \item{bcde}{Band code.} \item{width, flag}{Field width and format modifier for automated creation of BCDE information, defaults to '3' and '0', respectively. See \code{\link{formatC}} for further details.} \item{prefix, postfix}{Prefix and postfix to be added to the created BCDE information.} \item{meta_param}{Metadata parameters used to document new data layer} \item{info}{Log information added to metadata} \item{in_bcde}{BCDE of layer used as input dataset} \item{out_bcde}{BCDE of layer used as output dataset} \item{data}{Data layer of a Satellite object} \item{rst}{Input raster::Raster* object from which to extract metadata.} \item{param}{Parameter of the metadata set (i.e. colname)} \item{return_bcde}{Return bcde as attribute (TRUE/FALSE)} \item{lnbr}{Layer number} \item{type}{Type of the sensor band} \item{spectrum}{Spectral region, e.g. "solar" or "thermal".} \item{calib}{Calibration information.} \item{return_calib}{Return calibration information (TRUE/FALSE)} \item{nbr}{Return specific data layer selected by number} } \value{ Objects of respective type (see \code{\link{satellite}}). } \description{ Get information from class Satellite. } \details{ The functions are generally self-explaining in that sence that \code{get*} returns the respective information and \code{set*} sets the respective information from/in the Satellite object. \code{addSatLog} adds a log entry to the Satellite object. } \section{Functions}{ \itemize{ \item \code{getSatDataLayers}: Return Satellite data layers \item \code{getSatDataLayer}: Return Satellite data layer i \item \code{getSatMeta}: Return Satellite object metadata \item \code{getSatMetaBCDETemplate}: Return template for Satellite object metadata which is based on existing band \item \code{getSatLog}: Return Satellite object log info \item \code{setSatBCDE}: Set BCDE/data layer names of a Satellite object \item \code{createSatBCDE}: If not supplied, automatically create BCDE names of a Satellite object \item \code{addSatMetaParam}: Add additional or overwrite metainformation parameter to Satellite object \item \code{addSatMetaEntry}: Add metainformation for an additional layer to Satellite object \item \code{addSatLog}: Add new log entry to Satellite object \item \code{addSatDataLayer}: Add new Satellite data layer \item \code{addRasterMeta2Sat}: Add raster meta data to Satellite object meta data \item \code{createRasterMetaData}: Create raster meta data \item \code{updateRasterMetaData}: Create raster meta data \item \code{countSatDataLayers}: Return number of Satellite data layers \item \code{getSatParam}: Return parameter (general method implemented by the specific functions below) \item \code{getSatBCDE}: Return Band code \item \code{getSatBID}: Return Band IDs \item \code{getSatSID}: Return sensor ID \item \code{getSatSensor}: Return sensor \item \code{getSatSensorGroup}: Return sensor group \item \code{getSatSensorInfo}: Return sensor information \item \code{getSatSpectrum}: Return spectrum \item \code{getSatBCDESolar}: Return solar band codes \item \code{getSatBCDEThermal}: Return thermal band codes \item \code{getSatXRes}: Return sensor x resolution \item \code{getSatYRes}: Return sensor y resolution \item \code{getSatRes}: Return mean sensor resolution (mean of x and y res) \item \code{getSatType}: Return sensor type \item \code{getSatCalib}: Return calibration level \item \code{getSatBCDEType}: Return TYPE band codes \item \code{getSatBCDEFromType}: Return BCDE matching TYPE \item \code{getSatBCDEFromSpectrum}: Return BCDE matching TYPE \item \code{getSatBCDESres}: Return the mean of x and y resolution for band codes matching type \item \code{getSatBCDECalib}: Return calibration level for band codes matching type \item \code{getSatBCDESolarCalib}: Return calibration level for band codes machting type and are solar bands \item \code{getSatBCDEThermalCalib}: Return calibration level for band codes machting type and are thermal bands \item \code{getSatBandInfo}: Return band information \item \code{getSatRadMax}: Return maximum radiance for bcde \item \code{getSatRadMin}: Return minimum radiance for bcde \item \code{getSatRefMax}: Return maximum reflectance for bcde \item \code{getSatRefMin}: Return minimum reflectance for bcde \item \code{getSatESD}: Return earth-sun distance \item \code{getSatESUN}: Return actual solar TOA irradiance \item \code{getSatSZEN}: Return sun zenith angle \item \code{getSatSAZM}: Return sun azimuth angle \item \code{getSatSELV}: Return Sun elevation \item \code{getSatMetaLayer}: Return Layer name from metadata \item \code{getSatLayerfromData}: Return Layer name from data layer \item \code{getSatLNBR}: Return Layer number \item \code{getSatLMIN}: Return minimum wavelength of the sensor band \item \code{getSatLMAX}: Return maximum wavelength of the sensor band \item \code{getSatRADA}: Return addition coefficient for SC to radiance conversion \item \code{getSatRADM}: Return multiplicative coefficient for SC to radiance conversion \item \code{getSatREFA}: Return addition coefficient for SC to reflectance \item \code{getSatREFM}: Return multiplicative coefficient for SC to reflectance \item \code{getSatBTK1}: Return calibration coefficent to convert SC to brightness temperature \item \code{getSatBTK2}: Return calibration coefficent to convert SC to brightness temperature \item \code{getSatDATE}: Return DATE \item \code{getSatProjection}: Return projection }} \examples{ # List of input files path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) # Raster stack l8 sat <- satellite(l8) } satellite/man/calcAtmosCorr.Rd0000644000176200001440000001511113572467256016056 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/calcAtmosCorr.R \name{calcAtmosCorr} \alias{calcAtmosCorr} \alias{calcAtmosCorr,Satellite-method} \alias{calcAtmosCorr,RasterStack-method} \alias{calcAtmosCorr,RasterLayer-method} \title{Atmospheric correction of remote sensing data} \usage{ \S4method{calcAtmosCorr}{Satellite}(x, model = c("DOS2", "DOS4"), esun_method = "RadRef") \S4method{calcAtmosCorr}{RasterStack}(x, path_rad, esun, szen, model = c("DOS2", "DOS4")) \S4method{calcAtmosCorr}{RasterLayer}(x, path_rad, esun, szen, model = c("DOS2", "DOS4")) } \arguments{ \item{x}{Satellite or Raster* object providing the radiance at the sensor.} \item{model}{Model to be used to correct for 1\% scattering (DOS2, DOS4).} \item{esun_method}{If x is a Satellite object, name of the method to be used to compute \code{esun} using one of \code{\link{calcTOAIrradRadRef}} ("RadRef"), \code{\link{calcTOAIrradTable}} ("Table") or \code{\link{calcTOAIrradModel}} ("Model").} \item{path_rad}{Path radiance, e.g. returned from \code{\link{calcPathRadDOS}}.} \item{esun}{Actual (i.e. non-normalized) TOA solar irradiance, e.g. returned from \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} or \code{\link{calcTOAIrradModel}}.} \item{szen}{Sun zenith angle.} } \value{ Satellite object with added atmospheric corrected layers raster::RasterStack object with atmospheric corrected layers raster::RasterLayer object with atmospheric corrected layer } \description{ The function computes an atmospheric scattering correction and converts the sensors digital numbers to reflectances using \itemize{ \item absolute radiance correction \item DOS2: a dark object substraction model by Chavez (1996) \item DOS4: a dark object substratcion model by Moran et al. (1992) } } \details{ If a Satellite object is passed to the function, and if the required pre-processing has not been performed already, the path radiance is computed based on a dark object's scaled count value using \code{\link{calcPathRadDOS}} which will also take care of the TOA solar irradiance by calling \code{\link{calcTOAIrradModel}}, \code{\link{calcTOAIrradRadRef}} or \code{\link{calcTOAIrradTable}} (depending on \code{esun_method}) if necessary. The bands' scaled counts are converted to radiance using \code{\link{convSC2Rad}}. The radiometric correction is based on a dark object approach using either the DOS2 (Chavez 1996) or DOS4 (Moran et al. 1992) model. The minimum reflectance values for the dark object are identified using the approximation of Chavez (1988, see \code{\link{calcPathRadDOS}} for details). The estimated values of the solar irradiance required for the path radiance can be computed by one of \code{\link{calcTOAIrradTable}} which is used to get readily published values of ESun, \code{\link{calcTOAIrradRadRef}} which computes ESun based on the actual radiance and reflectance in the scene, or \code{\link{calcTOAIrradModel}} which computes ESun based on look-up tables for the sensor's relative spectral response and solar irradiation spectral data. The atmospheric transmittance towards the sensor (Tv) is approximated by 1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992). The atmospheric transmittance from the sun (Tz) is approximated by the cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh scattering (DOS4, Moran et al. 1992). The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996) or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992). Equations are taken from Song et al. (2001). } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat_atmos <- calcAtmosCorr(sat, model = "DOS2", esun_method = "RadRef") bcde <- "B002n" sat <- calcTOAIrradRadRef(sat, normalize = FALSE) path_rad <- calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bcde))), bnbr = getSatLNBR(sat, bcde), band_wls = data.frame(LMIN = getSatLMIN(sat, getSatBCDESolar(sat)), LMAX = getSatLMAX(sat, getSatBCDESolar(sat))), radm = getSatRADM(sat, getSatBCDESolar(sat)), rada = getSatRADA(sat, getSatBCDESolar(sat)), szen = getSatSZEN(sat, getSatBCDESolar(sat)), esun = getSatESUN(sat, getSatBCDESolar(sat)), model = "DOS2") sensor_rad <- convSC2Rad(x = getSatDataLayer(sat, bcde), mult = getSatRADM(sat, bcde), add = getSatRADA(sat, bcde), getSatSZEN(sat, bcde)) ref_atmos <- calcAtmosCorr(x = sensor_rad, path_rad = path_rad[names(path_rad) == bcde], esun = getSatESUN(sat, bcde), szen = getSatSZEN(sat, bcde), model = "DOS2") } \references{ Chavez Jr PS (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at \url{http://www.sciencedirect.com/science/article/pii/0034425788900193} Chavez Jr PS (1996) Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62/9, available online at \url{https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved} Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software,43/4, 1-25. URL \url{http://www.jstatsoft.org/v43/i04/}. Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output.Remote Sensing of Environment 41/2-3, 169-184, doi:10.1016/0034-4257(92)90076-V, URL \url{http://www.sciencedirect.com/science/article/pii/003442579290076V}. Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment 75/2, doi:10.1016/S0034-4257(00)00169-3, URL \url{http://www.sciencedirect.com/science/article/pii/S0034425700001693} } satellite/man/convRad2BT.Rd0000644000176200001440000000267513572464203015226 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/convRad2BT.R \name{convRad2BT} \alias{convRad2BT} \alias{convRad2BT,Satellite-method} \alias{convRad2BT,RasterStack-method} \alias{convRad2BT,RasterLayer-method} \title{Convert a band's scaled counts to brightness temperature} \usage{ \S4method{convRad2BT}{Satellite}(x) \S4method{convRad2BT}{RasterStack}(x, k1, k2) \S4method{convRad2BT}{RasterLayer}(x, k1, k2) } \arguments{ \item{x}{An object of class Satellite, raster::RasterStack or raster::RasterLayer providing radiance values.} \item{k1, k2}{Temperature correction parameters.} } \value{ If x is a Satellite object, a Satellite object with added converted layers; \cr if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with converted layer(s). } \description{ Convert a band's radiance values to brightness temperature without any kind of atmospheric correction etc. } \details{ The conversion functions are taken from USGS' Landsat 8 Data Users Handbook which is available online at \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. } \examples{ path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) sat <- convRad2BT(sat) } \seealso{ \code{\link{calcAtmosCorr}} for converions of scaled counts to physical units including a scene-based atmospheric correction. } satellite/man/l7.Rd0000644000176200001440000000117713572464203013640 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/satellite-package.R \docType{data} \name{l7} \alias{l7} \title{Landsat 7 sample data} \format{\code{RasterStack} with bands 1-8 (incl. QA) of 41 by 41 pixels.} \source{ \url{https://earthexplorer.usgs.gov/} } \description{ This dataset comes from the USGS. It contains part of the Landsat 7 scene LE07_L1TP_195025_20010730_20170204_01_T1 (Collection 1 Level-1) from 2001-07-30 over Maburg, Germany. } \details{ Use of this data requires your agreement to the USGS regulations on using Landsat data. } \examples{ plotRGB(l7, r = 3, b = 1, stretch = "hist") } satellite/DESCRIPTION0000644000176200001440000000267313573407552013771 0ustar liggesusersPackage: satellite Type: Package Title: Handling and Manipulating Remote Sensing Data Version: 1.0.2 Date: 2019-12-06 Author: Thomas Nauss, Hanna Meyer, Tim Appelhans, Florian Detsch Maintainer: Florian Detsch Description: Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers' metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set. Depends: R (>= 2.10), raster, methods, utils, stats, grDevices, graphics Imports: plyr, Rcpp (>= 0.10.3), tools, stats4 License: MIT + file LICENSE Suggests: devtools, knitr, rgdal, testthat, rmarkdown LazyData: true VignetteBuilder: knitr LinkingTo: Rcpp RoxygenNote: 7.0.2 Encoding: UTF-8 NeedsCompilation: yes Packaged: 2019-12-09 07:52:48 UTC; FlorianD Repository: CRAN Date/Publication: 2019-12-09 09:10:02 UTC satellite/build/0000755000176200001440000000000013573376520013352 5ustar liggesuserssatellite/build/vignette.rds0000644000176200001440000000040713573376520015712 0ustar liggesusersuPn0uHH T) ;KZVCaɉm u. ~={guzя*>`=JnJnpRw using namespace Rcpp; // [[Rcpp::export]] NumericMatrix insertMinReqRem(IntegerVector anHs, IntegerVector anHt) { // setup output matrix int nRows = anHs.size(), nCols = anHt.size(); NumericMatrix mnOut(nRows, nCols); // setup temporary vectors IntegerVector anValRows(nRows); int nSumRows; int nReq; IntegerVector anValCols(nCols); int nSumCols; int nRem; IntegerVector anReqRem(2); // loop over ht$counts for (int i = 0; i < nCols; i++) { for (int j = 0; j < nRows; j++) { // pixelsreq for (int k = 0; k <= j; k++) { anValRows[k] = mnOut(k, i); } nSumRows = sum(anValRows); nReq = anHt[i] - nSumRows; anReqRem[0] = nReq; // reset temporary vector for (int k = 0; k < nRows; k++) { anValRows[k] = 0; } // pixelrem for (int l = 0; l <= i; l++) { anValCols[l] = mnOut(j, l); } nSumCols = sum(anValCols); nRem = anHs[j] - nSumCols; anReqRem[1] = nRem; // reset temporary vector for (int l = 0; l < nCols; l++) { anValCols[l] = 0; } // insert minimum value into output matrix mnOut(j, i) = min(anReqRem); } } return mnOut; } //////////////////////////////////////////////////////////////////////////////// // functions required for computation of scattering model in calcPathRadDOS //// //////////////////////////////////////////////////////////////////////////////// struct add_multiple { int incr; int count; add_multiple(int incr) : incr(incr), count(0) {} inline int operator()(int d) { return d + incr * count++; } }; // [[Rcpp::export]] Rcpp::NumericVector seqC(double from_, double to_, double by_ = 1.0) { int adjust = std::pow(10, std::ceil(std::log10(10 / by_)) - 1); int from = adjust * from_; int to = adjust * to_; int by = adjust * by_; std::size_t n = ((to - from) / by) + 1; Rcpp::IntegerVector res = Rcpp::rep(from, n); add_multiple ftor(by); std::transform(res.begin(), res.end(), res.begin(), ftor); return Rcpp::NumericVector(res) / adjust; } // [[Rcpp::export]] NumericVector ScatteringModel(NumericMatrix mnBandWls, double dScatCoef) { int nRows = mnBandWls.nrow(); NumericVector anOut(nRows); double x; double y; double by = 0.001; List lsActBand(nRows); NumericVector anTmp; for (int i = 0; i < nRows; i++) { x = mnBandWls(i, 0); y = mnBandWls(i, 1); lsActBand[i] = seqC(x, y, by); anTmp = lsActBand[i]; for (int j = 0; j < anTmp.size(); j++) { anTmp[j] = pow(anTmp[j], dScatCoef); } anOut[i] = mean(anTmp); } return anOut; } satellite/src/RcppExports.cpp0000644000176200001440000000415713572463522016044 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; // insertMinReqRem NumericMatrix insertMinReqRem(IntegerVector anHs, IntegerVector anHt); RcppExport SEXP _satellite_insertMinReqRem(SEXP anHsSEXP, SEXP anHtSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< IntegerVector >::type anHs(anHsSEXP); Rcpp::traits::input_parameter< IntegerVector >::type anHt(anHtSEXP); rcpp_result_gen = Rcpp::wrap(insertMinReqRem(anHs, anHt)); return rcpp_result_gen; END_RCPP } // seqC Rcpp::NumericVector seqC(double from_, double to_, double by_); RcppExport SEXP _satellite_seqC(SEXP from_SEXP, SEXP to_SEXP, SEXP by_SEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type from_(from_SEXP); Rcpp::traits::input_parameter< double >::type to_(to_SEXP); Rcpp::traits::input_parameter< double >::type by_(by_SEXP); rcpp_result_gen = Rcpp::wrap(seqC(from_, to_, by_)); return rcpp_result_gen; END_RCPP } // ScatteringModel NumericVector ScatteringModel(NumericMatrix mnBandWls, double dScatCoef); RcppExport SEXP _satellite_ScatteringModel(SEXP mnBandWlsSEXP, SEXP dScatCoefSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type mnBandWls(mnBandWlsSEXP); Rcpp::traits::input_parameter< double >::type dScatCoef(dScatCoefSEXP); rcpp_result_gen = Rcpp::wrap(ScatteringModel(mnBandWls, dScatCoef)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_satellite_insertMinReqRem", (DL_FUNC) &_satellite_insertMinReqRem, 2}, {"_satellite_seqC", (DL_FUNC) &_satellite_seqC, 3}, {"_satellite_ScatteringModel", (DL_FUNC) &_satellite_ScatteringModel, 2}, {NULL, NULL, 0} }; RcppExport void R_init_satellite(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } satellite/vignettes/0000755000176200001440000000000013573376520014263 5ustar liggesuserssatellite/vignettes/satellite.Rmd0000644000176200001440000001377413572463522016727 0ustar liggesusers--- title: "satellite - Classes and Methods for Satellite Data" author: "Thomas Nauss, Florian Detsch" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{satellite - Classes and Methods for Satellite Data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ## Introduction We are happy to introduce `satellite`, an R package designed to facilitate satellite remote sensing analysis in a structured and user-friendly manner. The main purpose of `satellite` is to provide standard classes and methods to stream-line remote sensing analysis workflow in R. It provides its own `satellite-class` along with standard methods for basic image transformations such as atmospheric and topgraphic corrections, among others. The package is desinged with both flexibility and user-friendliness in mind. Think of it as the `sp-package` for remote sensing analysis. It provides core functionality and can be easily extended via packages to suit your own analysis needs. Furthermore, the fact that image bands are stored as `Raster*` objects means, that all functionality currently available for these classes will also work nicely with `satellite`. In the following, we would like to highlight some of the functionality provided by `satellite`. ## The satellite class To start a remote sensing alaysis workflow with `satellite` you simply use its workhorse function `satellite()` and point it to a folder where your satellite data is stored. ```{r} library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) # Landsat 8 example data files sat <- satellite(files) ``` This will create an object of class `satellite` with three slots: * @layers - a list of `RasterLayers` of all available bands * @meta - a data frame containing all the meta data needed to process the data layers * @log - a list containing information about the processing history of the object For supported satellite platforms all of this is done automatically. At the moment of this writing, supported platforms are Landsat generations 4 to 8. It is, however, very easy to expand this support to other platforms by providing suitable look-up-tables (LUT). Even if no suitable LUT is available, `satellite` will still import slots `@layers` and `@log`. ### The @layers slot As mentioned above, `@layers` contains a list of `RasterLayers` of all available bands. The reason for storing the individual bands as `RasterLayers` rather than a `RasterStack` is that most satellite platforms provide at least one layer of different spatial resolution that the rest. ```{r} str(sat@layers, 1) ``` It is, however, easy to create a `RasterStack` from selected layers as `stack-method` is defined for class `satellite`. By default this will take all layers with the same resolution as the first and stack them. A suitable warning is provided so that the user is informed which layers were not included in the `RasterStack`. Furthermore, one can simply provide a vector of layer IDs (either layer names or numbers) to be stacked. ```{r} ## default (all that are similar to layer 1; panchromatic 15-m band 8 is skipped here) sat_stack <- stack(sat) sat_stack ## or by layer names stack(sat, layer = c("B001n", "B002n", "B003n")) ## or by layer indices stack(sat, layer = 2:6) ``` ### The @meta slot The `@meta` slot provides meta information for each of the layers of the `satellite` object. Here's a non-exhaustive list of the most important entries: * BCDE - the band code (i.e. layer name) * DATE - the date the image was captured * SID - a short ID for the sensor (e.g. "LC8") * TYPE - type of the solar spectrum the band falls into (e.g. visible, near-infrared) * SPECTRUM - the broad spectrum classification according to shart-wave vs. long-wave radiation (i.e. solar vs thermal) * CALIB - the calibration level of the layer ("SC" means source calibration, i.e. as provided by the raw data) In addition to these, several calibration coefficients (such as the sun zenith and azimuth angles , sun elevation, earth-sun distance etc.), information on spatial resolution and projection as well as information about file names and paths is also stored. For the Landsat 8 example data shipped with the package the meta data looks like this: ```{r} str(sat@meta) ``` Everytime the user performs some calculation on some or all of the layers, this meta information will be updated accordingly. Here's an example: ```{r} ## add digital elevation model to existing 'Satellite' object dem <- raster(system.file("extdata/DEM.TIF", package = "satellite")) sat <- addSatDataLayer(sat, data = dem, info = NULL, bcde = "DEM", in_bcde = "DEM") ## perform topographic correction sat_tc <- calcTopoCorr(sat) tail(sat_tc@meta[, 1:6]) ``` As you can see, all bands have been topographically corrected and the meta data for the resulting layers is automatically appended to the original data frame. Note for example how `$DATE` is set to the date that layers were calculated. Note, that in order to avoid too long console output, we only show the first and last six columns and rows, respectively, of the meta data here. ### The @log slot Similar to the meta data, log data is also updated every time an analyis is carried out on the object. The default entries (i.e. the ones created on intial import) are as follows: ```{r} sat@log ``` And here's how this is modified after the topographic correction: ```{r} str(sat_tc@log[1:2]) ``` Note how, in addition to the info about the initial import, we now have additional logs entries for each band that was topographically corrected clearly showing which call was dispatched, when and on which layer. Even though we only show the first additional log entry here, entries are created for all processed layers. This ensures that we can easily trace what has been done so far and serves as a reference for the current state of processing. satellite/vignettes/landsat-calibration.Rmd0000644000176200001440000000261113572463522020640 0ustar liggesusers--- title: "Landsat-8 Calibration" author: "Thomas Nauss" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Landsat-8 Calibration} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- The following example shows how to read a Landsat-8 data set downloaded from the [USGS Earth Explorer](https://earthexplorer.usgs.gov/) and how to convert the bands' scaled counts to radiance, reflectance and/or brightness temperature. ## Reading Landsat-8 data We will use the test data set supplied with this package. In order to read the data set, just supply the filename (or list of filenames) to the `satellite()` function. ```{r} library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ``` The meta data will automatically be loaded since (i) Landsat-8 is a known platform to the `satellite` package and (ii) the name of the meta data file is in accordance with the USGS conventions. ## Converting Landsat-8 scaled counts to physical units In order to convert scaled counts of a Satellite object to radiance, reflectance and/or temperature, just supply it to the `convSC2Rad`, `convSC2Ref` or `convRad2BT` function. ```{r} sat <- convSC2Rad(sat) sat <- convSC2Ref(sat) sat <- convRad2BT(sat) ``` satellite/NEWS0000644000176200001440000000445413572463733012763 0ustar liggesuserssatellite 1.0.2 Changes: * Replaced all class() calls with inherits(). Starting with R version 4.0.0, "matrix" objects also inherit from class "array", which invalidates code assuming that length(class(obj)) == 1. * Fixed broken links. * Roxygen2 update. satellite 1.0.1 Bugfixes: * calcAtmosCorr() was not compatible with NA values, ie quantile() failed. * compFilePathLandsat() failed when specifying metadata files with new naming standard as input. * compMetaLandsat() assigned Landsat 7 metadata information, eg parameters required for radiometric rescaling, to wrong bands. satellite 1.0.0 New features: * Inverse squared relative earth-sun distance from calcEarthSunDist(). * calcTopoCorr,Raster*() methods accept additional arguments (via '...') passed to writeRaster(). * crop,Satellite-method takes a new argument 'snap' similar to raster::crop(). * extend,Satellite-method takes a new argument 'value' which is assigned to new cells similar to raster::extend(). * satellite is compatible with pre-collection Level-1 and Collection 1 Level-1 file naming. * sortFilesLandsat() to automatically sort Landsat band files in ascending order. Bugfixes: * plot,Satellite-method ignored band codes to be visualized if 'bcde = ' assignment was not explicitly included. * Solved 'Error in .local(.Object, ...) :' when trying to retrieve values from built-in data sets l7, l8. Changes: * calcHistMatch() and panSharp() moved to satelliteTools (https://github.com/environmentalinformatics-marburg/satelliteTools). * Mandatory use of Rcpp functionality in calcPathRadDOS(). * Added helper functions in R/pck_data.R and R/pck_lut.R to .Rbuildignore. * Revised sensor IDs for Landsat 4 (LT4 for 'Landsat Thematic Mapper') and 5 (LT5). * Updated ESun values (https://landsat.usgs.gov/esun) for Landsat 4, 5 and 7. * Updated spectral range of Landsat 7 band 7 (https://landsat.usgs.gov/landsat-7-history). * Moved Rcpp from Depends to Imports. * Built-in data sets l7, l8 now originate from Landsat Collection 1 Level-1 instead of previous Pre-Collection 1 Level-1. * Removed deprecated convDN2RU(), users must switch to convSC2Rad(), convSC2Ref() and convRad2BT() instead. satellite 0.2.0 * n/a satellite 0.1.0 * Initial release satellite/R/0000755000176200001440000000000013573376520012454 5ustar liggesuserssatellite/R/names.R0000644000176200001440000000267113572463522013706 0ustar liggesusersif ( !isGeneric("names") ) { setGeneric("names", function(x, ...) standardGeneric("names")) } #' Get/set Satellite data layer names #' #' @description #' Get/set Satellite data layer names, i.e. the BCDE id. #' #' @param x A Satellite object. #' @param value Band codes of the individual data layers. #' #' @name names #' @aliases names,Satellite-method #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' names(sat) #' new_names <- paste0(names(sat), "_test") #' names(sat) <- new_names NULL # Function using satellite object ---------------------------------------------- #' @return Satellite data layer names as character vector. #' @rdname names setMethod("names", signature(x = "Satellite"), function(x){ as.character(getSatBCDE(x)) } ) # Function using satellite object ---------------------------------------------- #' @rdname names setMethod('names<-', signature(x = "Satellite"), function(x, value){ nl <- countSatDataLayers(x) if (is.null(value)) { bcde <- rep('', nl) } else if (length(value) != nl) { stop('incorrect number of layer names') } x <- setSatBCDE(x, value) return(x) } )satellite/R/show.R0000644000176200001440000000021013572463522013546 0ustar liggesuserssetMethod("show", signature(object = "Satellite"), function(object) { print(object) } ) satellite/R/compFilePathLandsat.R0000644000176200001440000000745213572463522016467 0ustar liggesusers#' Get filename, bands and metadata file for Landsat 7 and 8 standard 1B/T format #' #' @description #' The function compiles the sensor, band, filename and metadata filename information #' for standard level 1B/T Landsat files. #' #' @param files Path and filename(s) of one or more Landsat band files or, #' alternatively, one or more Landsat metadata files. #' #' @return \code{data.frame} containing filepaths, band numbers and metadata #' filepaths. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' #' compFilePathLandsat(files) #' #' sortFilesLandsat(files) #' sortFilesLandsat(files, id = TRUE) # indices #' #' @export compFilePathLandsat #' @name compFilePathLandsat #' @rdname compFilePathLandsat #' @aliases compFilePathLandsat compFilePathLandsat <- function(files){ if((length(files) == 1 & grepl("MTL", files[1])) == FALSE){ info <- lapply(files, function(x){ layer <- tools::file_path_sans_ext(basename(x)) pos <- gregexpr(pattern ='_B', layer)[[1]][1] band_ids <- substr(basename(x), pos + 2, nchar(layer)) meta <- paste0(dirname(x), "/", substr(basename(x), 1, pos), "MTL.txt") sid <- substr(basename(x), 1, 3) ## alternative sensor pattern if (sid %in% c("LC0", "LE0", "LT0")) { pttrn <- substr(basename(x), 1, 4) rid <- apply(lut$SENSOR_ID_PATTERN, 1, FUN = function(x) pttrn %in% x) sid <- lut$SENSOR_ID_PATTERN$SID[rid] } sensor <- lutInfoSensorFromSID(sid) band_code <- lutInfoBCDEFromBID(sid = sid, bid = band_ids) data.frame(SID = sid, SENSOR = sensor, BID = band_ids, BCDE = band_code, LAYER = layer, FILE = x, CALIB = "SC", METAFILE = meta, stringsAsFactors = FALSE, row.names = NULL) }) result <- (do.call("rbind", info)) return(result) } else { info <- lapply(files, function(x){ sid <- substr(basename(x), 1, 3) ## alternative sensor pattern if (sid %in% c("LC0", "LE0", "LT0")) { pttrn <- substr(basename(x), 1, 4) rid <- apply(lut$SENSOR_ID_PATTERN, 1, FUN = function(x) pttrn %in% x) sid <- lut$SENSOR_ID_PATTERN$SID[rid] } sensor <- lutInfoSensorFromSID(sid) band_code <- lutInfoBCDEFromBID(sid = sid) ids = names(lut$BANDS) == sid bid = sapply(band_code, function(i) { tmp = grep(i, lut[[lut$BANDS[ids]]][, "BCDE"]) lut[[lut$BANDS[ids]]][tmp, "BID"] }) data.frame(BCDE = as.character(band_code),SID = sid, SENSOR = sensor, BID = bid, LAYER = NA, FILE = NA, CALIB = NA, METAFILE = x, stringsAsFactors = FALSE, row.names = NULL) }) result <- (do.call("rbind", info)) rownames(result) <- NULL return(result) } } # Sort Landsat band files ----- #' @describeIn compFilePathLandsat Sort Landsat band files in ascending order. #' @param id \code{logical}, defaults to \code{FALSE}. Determines whether to #' return sorted band files (ie default) or sorting order. #' @return If \code{id = FALSE} (default), sorted band files as #' \code{character}, else the corresponding sorting order as \code{integer}. #' @export sortFilesLandsat sortFilesLandsat <- function(files, id = FALSE) { cfp <- compFilePathLandsat(files) bid <- suppressWarnings(as.integer(cfp$BID)) if (id) { order(bid) } else { files[order(bid)] } } satellite/R/convRad2BT.R0000644000176200001440000000633013572463733014507 0ustar liggesusersif (!isGeneric("convRad2BT") ) { setGeneric("convRad2BT", function(x, ...) standardGeneric("convRad2BT")) } #' Convert a band's scaled counts to brightness temperature #' #' @description #' Convert a band's radiance values to brightness temperature without #' any kind of atmospheric correction etc. #' #' @param x An object of class Satellite, raster::RasterStack or #' raster::RasterLayer providing radiance values. #' @param k1,k2 Temperature correction parameters. #' #' @export convRad2BT #' #' @name convRad2BT #' #' @details #' The conversion functions are taken from USGS' Landsat 8 Data Users Handbook #' which is available online at #' \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. #' #' @seealso \code{\link{calcAtmosCorr}} for converions of scaled counts #' to physical units including a scene-based atmospheric correction. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- convRad2BT(sat) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added converted #' layers; \cr #' if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with #' converted layer(s). #' @rdname convRad2BT setMethod("convRad2BT", signature(x = "Satellite"), function(x){ band_codes <- getSatBCDEThermalCalib(x, calib = "RAD") for(bcde in band_codes){ if(!any(is.na(getSatRADM(x, bcde)), is.na(getSatBTK1(x, bcde)))){ sensor_ref <- convRad2BT(x = getSatDataLayer(x, bcde), k1 = getSatBTK1(x, bcde), k2 = getSatBTK2(x, bcde)) layer_bcde <- paste0(bcde, "_BT") meta_param <- getSatMetaBCDETemplate(x, bcde) meta_param$BCDE <- layer_bcde meta_param$CALIB <- "BT" info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = sensor_ref, meta_param = meta_param, info = info, in_bcde = bcde) } } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @rdname convRad2BT setMethod("convRad2BT", signature(x = "RasterStack"), function(x, k1, k2){ for(l in seq(nlayers(x))){ x[[l]] <- convRad2BT(x[[l]], k1, k2) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @rdname convRad2BT setMethod("convRad2BT", signature(x = "RasterLayer"), function(x, k1, k2){ x <- k2 / log(k1 / x + 1) return(x) })satellite/R/print.R0000644000176200001440000000125713572463522013736 0ustar liggesuserssetMethod ('print' , signature(x = "Satellite"), function(x) { cat(paste("Summary of the Satellite Object\n\n", sep = "")) print(getSatMeta(x)[1:11]) cat(paste("\n Layers are projected in:\n", getSatProjection(x, getSatBCDE(x, 1)), sep = "")) if (length(unique(as.character(lapply( seq(countSatDataLayers(x)), function(i) getSatProjection(x, getSatBCDE(x, i)))))) > 1) { cat("\n\nWarning: Not all layers have same projection") } } ) satellite/R/convSC2Rad.R0000644000176200001440000001035013572463733014504 0ustar liggesusersif (!isGeneric("convSC2Rad") ) { setGeneric("convSC2Rad", function(x, ...) standardGeneric("convSC2Rad")) } #' Convert a band's scaled counts to radiance #' #' @description #' Convert a band's scaled counts to radiance using a simple linear conversion #' without any kind of atmospheric correction etc. #' #' @param x An object of class Satellite, raster::RasterStack or #' raster::RasterLayer providing scaled counts (DNs). #' @param add Additive coefficient for value transformation (i.e. offset). #' @param mult Multiplicative coefficient for value transformation (i.e. slope). #' @param szen Cosine of solar zenith angle. #' @param szen_correction Logical; if \code{TRUE}, sun zenith correction is #' being applied. #' @param subset Logical; if \code{TRUE}, all layers but the cropped #' ones are being dropped; if \code{FALSE} (default), cropped layers are appended to the #' Satellite object. #' #' @export convSC2Rad #' #' @name convSC2Rad #' #' @details #' The conversion functions are taken from USGS' Landsat 8 Data Users Handbook #' which is available online at #' \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. #' #' @seealso \code{\link{calcAtmosCorr}} for conversions of scaled counts #' to physical units including a scene-based atmospheric correction. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- convSC2Rad(sat) #' #' # If you use a raster layer, supply required meta information #' bcde <- "B002n" #' convSC2Rad(x = getSatDataLayer(sat, bcde), #' mult = getSatRADM(sat, bcde), #' add = getSatRADA(sat, bcde)) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added converted #' layers; \cr #' if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with #' converted layer(s). #' @rdname convSC2Rad setMethod("convSC2Rad", signature(x = "Satellite"), function(x, szen_correction = "TRUE", subset = FALSE){ band_codes <- getSatBCDECalib(x, calib = "SC") for(bcde in band_codes){ if(!is.na(getSatRADM(x, bcde))){ sensor_rad <- convSC2Rad(x = getSatDataLayer(x, bcde), mult = getSatRADM(x, bcde), add = getSatRADA(x, bcde)) layer_bcde <- paste0(bcde, "_RAD") meta_param <- getSatMetaBCDETemplate(x, bcde) meta_param$BCDE <- layer_bcde meta_param$CALIB <- "RAD" info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = sensor_rad, meta_param = meta_param, info = info, in_bcde = bcde) } } if(subset == TRUE){ x <- subset(x, cid = "RAD") #reset LNBR (dirty hack) x@meta$LNBR <- rep(1:nrow(x@meta)) } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @rdname convSC2Rad setMethod("convSC2Rad", signature(x = "RasterStack"), function(x, mult, add, szen){ for(l in seq(nlayers(x))){ x[[l]] <- convSC2Rad(x[[l]], mult, add, szen) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @rdname convSC2Rad setMethod("convSC2Rad", signature(x = "RasterLayer"), function(x, mult, add, szen){ if(!missing(szen)){ x <- (mult * x + add) / cos(szen * pi / 180.0) } else { x <- mult * x + add } return(x) })satellite/R/sysdata.rda0000644000176200001440000234674013573376522014636 0ustar liggesusers7zXZi"6!X])TW"nRʟxq5(БPY9:ͭb! )չ6Yrkwej9 P X4F&'Lbhf Yvn(i}˷hm)')ʹc.!\%֗'VEsz)@NFWF'wN8{ FǕNpV*$5_ccwBdXZ:A:yL6hSjD7<:Ba,; 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if \code{TRUE}, sun zenith correction is #' being applied. #' #' #' @export convRad2Ref #' #' @name convRad2Ref #' #' @details #' The conversion functions are taken from USGS' Landsat 8 Data Users Handbook #' which is available online at #' \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. #' #' If the sensor does not provide linear conversion coefficients for reflectance #' computation, the reflectance is calculated using the solar irradiance #' following the functions taken from USGS' Landsat 7 manual, chapter 11.3.2, #' which is available online at #' \url{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}. #' #' @seealso \code{\link{calcAtmosCorr}} for conversions of scaled counts #' to physical units including a scene-based atmospheric correction. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- convRad2Ref(sat) #' #' # If you use a raster layer, supply required meta information #' bcde <- "B002n" #' convRad2Ref(x = getSatDataLayer(sat, bcde), #' mult = getSatRADM(sat, bcde), #' add = getSatRADA(sat, bcde)) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added converted #' layers; \cr #' if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with #' converted layer(s). #' @rdname convRad2Ref setMethod("convRad2Ref", signature(x = "Satellite"), function(x, szen_correction = "TRUE"){ band_codes <- getSatBCDESolarCalib(x, calib = "SC") for(bcde in band_codes){ if(!is.na(getSatREFM(x, bcde))){ if(szen_correction == TRUE){ szen <- getSatSZEN(x, bcde) sensor_ref <- convRad2Ref(x = getSatDataLayer(x, bcde), mult = getSatREFM(x, bcde), add = getSatREFA(x, bcde), szen = szen) calib = "REF" } else { sensor_ref <- convRad2Ref(x = getSatDataLayer(x, bcde), mult = getSatREFM(x, bcde), add = getSatREFA(x, bcde)) calib = "REF_NoSZEN" } layer_bcde <- paste0(bcde, "_", calib) meta_param <- getSatMetaBCDETemplate(x, bcde) meta_param$BCDE <- layer_bcde meta_param$CALIB <- calib info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = sensor_ref, meta_param = meta_param, info = info, in_bcde = bcde) } } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @rdname convRad2Ref setMethod("convRad2Ref", signature(x = "RasterStack"), function(x, mult, add, szen){ for(l in seq(nlayers(x))){ x[[l]] <- convRad2Ref(x[[l]], mult, add, szen) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @rdname convRad2Ref setMethod("convRad2Ref", signature(x = "RasterLayer"), function(x, mult, add, szen){ x <- mult * x + add if(!missing(szen)){ x <- x / cos(szen * pi / 180.0) } return(x) })satellite/R/satellite.R0000644000176200001440000001125413572466404014570 0ustar liggesusersif ( !isGeneric("satellite") ) { setGeneric("satellite", function(x, ...) standardGeneric("satellite")) } #' Create a Satellite object #' #' @description #' Method to create a Satellite object. #' #' @param x A vector of filenames, a (multi-layered) \code{Raster*} object or a #' \code{list} of single \code{RasterLayer} objects (see \code{\link{raster}}). #' In the latter case, be aware that bands must be arranged in ascending order #' (eg using \code{\link{sortFilesLandsat}}). #' @param meta Optional metadata object (e.g. returned from #' \code{\link{compMetaLandsat}}). If 'x' is a satellite dataset and recognised #' as "Landsat", then the metadata is automatically extracted from the #' respective meta information file if both the satellite data and the metadata #' file follow the USGS Earth Explorer's naming convention. #' @param log Optionally supply a log entry. #' #' @return A \code{Satellite} object. #' #' @export satellite #' #' @details A Satellite object consists of three data sections: #' (i) a raster data section which holds the actual data values of the #' respective sensor bands, (ii) a metadata grid which holds meta information #' for each sensor band (e.g. calibration coefficients, type of sensor band #' etc.) and (iii) a list of log information which records the processing #' history of the entire dataset. #' #' @seealso (i) \code{\link{compMetaLandsat}} to get more information about the #' structure of the metadata component; (ii) #' \url{https://www.usgs.gov/faqs/what-naming-convention-landsat-collections-level-1-scenes?qt-news_science_products=0#qt-news_science_products} #' for detailed information about the naming conventions for Landsat scene #' identifiers; and (iii) \code{\link{sortFilesLandsat}} for automated #' rearrangement of Landsat band files. #' #' @name satellite #' #' @examples #' ## 'character' input (i.e. filenames) #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' #' satellite(files) #' #' ## raster::RasterStack input #' satellite(l8) #' NULL # Function using vector of filenames ------------------------------------------- #' @rdname satellite setMethod("satellite", signature(x = "character"), function(x, meta, log){ if(missing(meta)){ if(lutInfoSGRPfromFilename(x) == "Landsat"){ meta <- compMetaLandsat(x) } else { meta <- data.frame(BCDE = paste0("Bxx", as.character(seq(length(x)))), LNBR = seq(length(x)), LAYER = tools::file_path_sans_ext(basename(x)), FILE = x) } } layers <- lapply(seq(nrow(meta)), function(y){ act_layer <- raster(as.character(meta$FILE)[y]) names(act_layer) <- meta$BCDE[y] return(act_layer) }) if(missing(log)){ ps <- list(time = Sys.time(), info = "Initial import", layers = "all", output = "all") log <- list(ps0001 = ps) } #return(new("Satellite", layers = layers, meta = meta, log = log)) tmp <- new("Satellite", layers = layers, meta = meta, log = log) return(addRasterMeta2Sat(tmp)) }) # Function using readily existing raster layers -------------------------------- #' @rdname satellite setMethod("satellite", signature(x = "Raster"), function(x, meta, log){ if(missing(meta)){ meta <- data.frame(DATE = as.POSIXlt(Sys.Date(), tz = "UTC"), FILE = names(x)) } layers <- if (raster::nlayers(x) > 1) raster::unstack(x) else x if (missing(log)) return(satellite(layers, meta)) else return(satellite(layers, meta, log)) }) # Function using list of raster layers ----------------------------------------- #' @rdname satellite setMethod("satellite", signature(x = "list"), function(x, meta, log){ if(missing(meta)){ meta <- data.frame(DATE = as.POSIXlt(Sys.Date(), tz = "UTC"), FILE = sapply(x, names)) } if(missing(log)){ ps <- list(time = Sys.time(), info = "Initial import", layers = "all", output = "all") log <- list(ps0001 = ps) } tmp <- new("Satellite", layers = x, meta = meta, log = log) return(addRasterMeta2Sat(tmp)) }) satellite/R/subset.R0000644000176200001440000000700713572463522014106 0ustar liggesusersif ( !isGeneric("subset") ) { setGeneric("subset", function(x, ...) standardGeneric("subset")) } #' Subset of Satellite object data layers #' #' @description #' Create a subset of data layers from a \code{Satellite} object and return it #' as a standalone \code{Satellite} object. #' #' @param x Satellite object providing the source band(s) to be adjusted. #' @param sid Band numbers or bcde which should be extracted #' @param cid Calibration information used for subsetting (only works if sid is #' not supplied to the function) #' @param i Layer index(es) for subsetting. #' #' @name subset #' @export subset #' @aliases subset,Satellite-method #' #' @return A Satellite object #' #' @examples #' ## sample data #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' sat[[2:5]] #' subset(sat, cid = "SC") NULL # Function using satellite object ---------------------------------------------- #' @return A Satellite object #' @rdname subset setMethod('subset', signature(x = 'Satellite'), function(x, sid, cid) { if(!missing(sid)){ if (!is.character(sid)) { sid <- getSatBCDE(x, sid) } data <- getSatDataLayers(x, sid) meta <- getSatMeta(x, sid) log <- getSatLog(x) x <- satellite(data, meta = meta, log = log) } else { if (is.character(cid)) { i <- subset(x@meta, x@meta$CALIB == cid) if (length(i)==0) { stop('invalid layer names') } else if (length(i) < length(cid)) { warning('invalid layer names omitted') } meta_cid <- i rownames(meta_cid) <- seq(1:nrow(meta_cid)) meta_cid$LNBR <-seq(1:nrow(meta_cid)) data_cid <- x@layers[as.integer(row.names(i))] } else { # cidting by row/list numbers makes only sense for multiples of # channel numbers in case all channels are submitted to the # Satellite object in the first place. Therefore maybe checking # for correct selection would need to be implemented for user # friendlyness. Maybe defining sat object as list of obejcts, # where each object is an instance of the sat object as it is # now defined would make handling complete instances of sat # objects on which some computation was applied easier to handle # (instead of appending them directly to the list and the meta # data frame)? cid <- as.integer(cid) if (! all(cid %in% 1:length(x@layers))) { stop('not a valid cid') } meta_cid <- x@meta[cid,] rownames(meta_cid) <- seq(1:nrow(meta_cid)) meta_cid$LNBR <-seq(1:nrow(meta_cid)) data_cid <- x@layers[cid] } x <- new("Satellite", layers = data_cid, meta = meta_cid) } return(x) }) # Function using satellite object ---------------------------------------------- #' @return A Satellite object #' @rdname subset setMethod("[[", signature(x = "Satellite"), function(x, i) { subset(x, i) } ) satellite/R/compMetaLandsat.R0000644000176200001440000001771013572463522015657 0ustar liggesusers#' Get calibration information from Landsat 8 standard level 1B/T filename #' #' @description #' The function scans a Lansat metadata file for various calibration #' and orbit coefficients as well as some sensor specific data. #' #' @param files Path and filename of the Landsat metadata file. #' #' @return \code{data.frame} containing the following information for each #' band/layer: #' \itemize{ #' \item DATE date (e.g. 2013-07-07) #' \item SID sensor id (e.g. LC8) #' \item SENSOR sensor name (e.g. Landsat 8) #' \item SGRP sensor group (e.g. Landsat) #' \item BID band id (e.g. 7) #' \item BCDE band code (5 digit standard name, e.g B001n) #' \item SRES spatial resolution of the sensor band (e.g. 30 for 30 m x 30m) #' \item TYPE type of the sensor band regarding wavelength (e.g. VIS) #' \item SPECTRUM spectral range regarding radiation source (e.g. solar) #' \item CALIB type of applied calibration (e.g. SC for scaled counts) #' \item RID region id (e.g. R00001) for multi region Satellite objects #' \item RADA addtition coefficient for radiance conversion #' \item RADM multiplication coefficient for radiance conversion #' \item REFA addtition coefficient for reflectance conversion #' \item REFM multiplication coefficient for reflectance conversion #' \item BTK1 brightness temperature correction parameter #' \item BTK2 brightness temperature correction parameter #' \item SZEN sun zenith angle #' \item SAZM sun azimuth angle #' \item SELV sun elevation angle #' \item ESD earth-sun distance (AU) #' \item LMIN Minimum wavelength of the band (micrometer) #' \item LMAX Maximum wavelength of the band (micrometer) #' \item RADMIN Minimum radiance recorded by the band #' \item RADMAX Maximum radiance recorded by the band #' \item REFMIN Minimum reflectance recorded by the band #' \item REFMAX Maximum reflectance recorded by the band #' \item LNBR Layer number from 1 to n layers #' \item LAYER Layer name #' \item FILE Filepath of the data file #' \item METAFILE Filepath of the metadata file #' } #' #' @export compMetaLandsat #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' compMetaLandsat(files) #' compMetaLandsat <- function(files){ datafiles <- compFilePathLandsat(files) bandinfo_all <- lutInfoBandsFromSID(datafiles$SID[1]) bandinfo_all <- merge(bandinfo_all, datafiles, by = c("BCDE", "SID", "BID")) metainformation <- lapply(seq(length(sort(unique(bandinfo_all$META)))), function(scn){ bandinfo <- bandinfo_all[bandinfo_all$META == sort(unique(bandinfo_all$META))[scn], ] metadata <- utils::read.table(as.character(bandinfo$METAFILE[1]), header = FALSE, sep = "=", fill = TRUE) search_term_date <- "DATE_ACQUIRED" search_term_esd <- "EARTH_SUN_DISTANCE" ids = names(lut$BANDS) == unique(bandinfo$SID) bds = sapply(bandinfo$BCDE, function(i) { tmp = grep(i, lut[[lut$BANDS[ids]]][, "BCDE"]) row.names(lut[[lut$BANDS[ids]]])[tmp] }); bds = toupper(bds) metainformation <- lapply(seq(bds), function(x){ bd = bds[x] search_term_rad_max <- paste0("RADIANCE_MAXIMUM_", bd) search_term_rad_min <- paste0("RADIANCE_MINIMUM_", bd) search_term_ref_max <- paste0("REFLECTANCE_MAXIMUM_", bd) search_term_ref_min <- paste0("REFLECTANCE_MINIMUM_", bd) search_term_add_rad <- paste0("RADIANCE_ADD_", bd) search_term_mult_rad <- paste0("RADIANCE_MULT_", bd) search_term_add_ref <- paste0("REFLECTANCE_ADD_", bd) search_term_mult_ref <- paste0("REFLECTANCE_MULT_", bd) search_term_BTK1 <- paste0("K1_CONSTANT_", bd) search_term_BTK2 <- paste0("K2_CONSTANT_", bd) cal_rad_max <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_rad_max)))) cal_rad_min <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_rad_min)))) cal_ref_max <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_ref_max)))) cal_ref_min <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_ref_min)))) cal_add_rad <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_add_rad)))) cal_mult_rad <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_mult_rad)))) cal_add_ref <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_add_ref)))) cal_mult_ref <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_mult_ref)))) cal_BTK1 <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_BTK1)))) cal_BTK2 <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_BTK2)))) date <- as.POSIXlt(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_date))), tz = "UTC") esd <- as.numeric(as.character( (subset(metadata$V2, gsub("\\s","", metadata$V1) == search_term_esd)))) selv <- as.numeric(as.character( subset(metadata$V2, gsub("\\s","", metadata$V1) == "SUN_ELEVATION"))) sazm <- as.numeric(as.character( subset(metadata$V2, gsub("\\s","", metadata$V1) == "SUN_AZIMUTH"))) szen <- 90.0 - selv if(length(cal_rad_max) == 0){cal_rad_max = NA} if(length(cal_rad_min) == 0){cal_rad_min = NA} if(length(cal_ref_max) == 0){cal_ref_max = NA} if(length(cal_ref_min) == 0){cal_ref_min = NA} if(length(cal_add_rad) == 0){cal_add_rad = NA} if(length(cal_mult_rad) == 0){cal_mult_rad = NA} if(length(cal_add_ref) == 0){cal_add_ref = NA} if(length(cal_mult_ref) == 0){cal_mult_ref = NA} if(length(cal_BTK1) == 0){cal_BTK1 = NA} if(length(cal_BTK2) == 0){cal_BTK2 = NA} if(length(esd) == 0){esd = NA} result <- data.frame(SCENE = scn, DATE = date, SID = bandinfo$SID[x], SENSOR = bandinfo$SENSOR[x], SGRP = bandinfo$SGRP[x], BID = bandinfo$BID[x], BCDE = bandinfo$BCDE[x], #SRES = bandinfo$SRES[x], TYPE = bandinfo$TYPE[x], SPECTRUM = bandinfo$SPECTRUM[x], CALIB = bandinfo$CALIB[x], RID = "R00001", RADA = cal_add_rad, RADM = cal_mult_rad, REFA = cal_add_ref, REFM = cal_mult_ref, BTK1 = cal_BTK1, BTK2 = cal_BTK2, SZEN = szen, SAZM = sazm, SELV = selv, ESD = esd, LMIN = bandinfo$LMIN[x], LMAX = bandinfo$LMAX[x], RADMAX = cal_rad_max, RADMIN = cal_rad_min, REFMAX = cal_ref_max, REFMIN = cal_ref_min, LNBR = x, LAYER = bandinfo$LAYER[x], FILE = bandinfo$FILE[x], METAFILE = bandinfo$METAFILE[x], stringsAsFactors = FALSE) }) metainformation <- do.call("rbind", metainformation) return(metainformation) }) return(do.call("rbind", metainformation)) }satellite/R/calcTopoCorr.R0000644000176200001440000001332613572463522015174 0ustar liggesusersif ( !isGeneric("calcTopoCorr") ) { setGeneric("calcTopoCorr", function(x, ...) standardGeneric("calcTopoCorr")) } #' Correct for topographic effects. #' #' @param x \code{Satellite} or \code{Raster*} object. #' @param mask \code{logical}. If \code{TRUE}, the cloudmask from the #' \code{Satellite} object (if available) will be considered in the regression #' model. #' #' @details #' The method of Civco (1989) is applied on atmospherically corrected bands #' (if not already available in the Satellite object, #' \code{\link{calcAtmosCorr}} is performed with its default settings.): #' First, an analytical hillshade image is created based on a DEM and sun #' elevation and sun zenith information from the metadata. A regression between #' the hillshade (independent variable) and each channel is then calculated #' with consideration of a cloudmask (if available). #' The regression coefficents are used to calibrate the hillshade raster #' (for each channel individually). #' Finally, the calibrated hillshade image is subtracted from the corresponding #' channel and the mean value of the channel is added. #' #' @return If x is a Satellite object, a Satellite object with added, #' topographic corrected layers; if x is a \code{raster::Raster*} object, a #' \code{raster::Raster*} object with converted layer(s). #' #' @export calcTopoCorr #' #' @name calcTopoCorr #' #' @references CIVCO, D.L. (1989): Topographic normalization of Landsat Thematic #' Mapper digitalimagery. \emph{Photogrammetric Engineering & Remote Sensing}, #' 55, 1303-1309. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' ## dem #' #' files_dem <- list.files(path, pattern = "DEM", full.names = TRUE) #' DEM <- raster(files_dem) #' #' sat <- addSatDataLayer(sat, data = DEM, info = NULL, bcde = "DEM", in_bcde="DEM") #' #' \dontrun{ #' sat <- calcTopoCorr(sat) #' } NULL # Function using satellite object ---------------------------------------------- #' @rdname calcTopoCorr setMethod("calcTopoCorr", signature(x = "Satellite"), function(x, mask=TRUE){ if (is.null(getSatDataLayer(x, "hillShade"))){ if (is.null(getSatDataLayer(x, "DEM"))){stop( "Please provide a DEM in the Satellite object")} x <- demTools(x, method = "hillShade") } if(any(is.na(getSatBCDESolarCalib(x, calib = "AtmosCorr")))){ x <- calcAtmosCorr(x) } # atmoscbands <- getSatDataLayers(x)[grepl("_REF_AtmosCorr$", # getSatBCDE(x))] atmos_bands <- getSatBCDESolarCalib(x, calib = "AtmosCorr") for(i in 1:length(atmos_bands)){ hillsh <- getSatDataLayer(x, "hillShade") #hillsh <- raster::resample (hillsh, atmoscbands[[i]]) hillsh <- raster::resample (hillsh, getSatDataLayer(x, atmos_bands[i])) if (mask){ cloudmask <- getSatDataLayer(x, "cloudmask")[[1]] } layer_bcde <- gsub("AtmosCorr","TopoCorr",getSatBCDE(x)[ grepl("_REF_AtmosCorr$", getSatBCDE(x))][i]) meta_param <- data.frame(getSatSensorInfo(x), getSatBandInfo(x, atmos_bands[i], return_calib = FALSE), CALIB = "REF_TopoCorr") tmp <- calcTopoCorr(getSatDataLayer(x, atmos_bands[[i]]), hillsh, cloudmask) x <- addSatDataLayer(x, bcde = layer_bcde, data = tmp, info="Add layer from calcTopoCorr(x)", meta_param = meta_param, in_bcde=getSatBCDE(x)[ grepl("_REF_AtmosCorr$", getSatBCDE(x))][i]) } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @rdname calcTopoCorr setMethod("calcTopoCorr", signature(x = "RasterStackBrick"), function(x, hillsh, cloudmask = NULL, ...){ for(l in seq(raster::nlayers(x))){ x[[l]] <- calcTopoCorr(x[[l]], hillsh, cloudmask, ...) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @param hillsh A \code{RasterLayer} created with \code{\link{hillShade}}. #' @param cloudmask A \code{RasterLayer} in which clouds are masked with #' NA values, passed to \code{\link[raster]{mask}}. #' @param ... Additional arguments passed to \code{\link{writeRaster}}. #' @rdname calcTopoCorr setMethod("calcTopoCorr", signature(x = "RasterLayer"), function(x, hillsh, cloudmask = NULL, ...){ if (!is.null(cloudmask)){ x <- raster::mask(x, cloudmask) } model <- summary(stats::lm(raster::values(x) ~ raster::values(hillsh))) calib <- raster::calc(hillsh, fun = function(x) { x * stats::coef(model)[2] + stats::coef(model)[1] }) raster::overlay(x, calib, fun = function(y, z) { y - z + mean(y, na.rm = TRUE) }, ...) }) satellite/R/satInfo.R0000644000176200001440000006176313572463733014221 0ustar liggesusers#' Get or access Satellite object information used by various functions #' #' @description #' Get information from class Satellite. #' #' @param data Data layer of a Satellite object #' @param type Type of the sensor band #' @param lnbr Layer number #' @param in_bcde BCDE of layer used as input dataset #' @param info Log information added to metadata #' @param meta_param Metadata parameters used to document new data layer #' @param nbr Return specific data layer selected by number #' @param out_bcde BCDE of layer used as output dataset #' @param param Parameter of the metadata set (i.e. colname) #' @param return_bcde Return bcde as attribute (TRUE/FALSE) #' @param return_calib Return calibration information (TRUE/FALSE) #' @param sat Satellite object (see \code{\link{satellite}}). #' @param rst Input raster::Raster* object from which to extract metadata. #' @param spectrum Spectral region, e.g. "solar" or "thermal". #' @param width,flag Field width and format modifier for automated creation of #' BCDE information, defaults to '3' and '0', respectively. See #' \code{\link{formatC}} for further details. #' @param prefix,postfix Prefix and postfix to be added to the created BCDE #' information. #' @param calib Calibration information. #' #' #' @return Objects of respective type (see \code{\link{satellite}}). #' #' @details The functions are generally self-explaining in that sence that #' \code{get*} returns the respective information and \code{set*} sets the #' respective information from/in the Satellite object. #' #' \code{addSatLog} adds a log entry to the Satellite object. #' #' @name satInfo #' #' @examples #' # List of input files #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' # Raster stack l8 #' sat <- satellite(l8) #' NULL ################################################################################ # Return (almost) complete sections of the Satellite object ################################################################################ #' @describeIn satInfo Return Satellite data layers #' @export getSatDataLayers getSatDataLayers <- function(sat, bcde = NULL){ if (is.null(bcde)) { return(sat@layers) } else { ls_lyr <- sat@layers ch_lyr_nms <- sapply(ls_lyr, names) return(ls_lyr[ch_lyr_nms %in% bcde]) } } # Return Satellite data layer i ------------------------------------------------ #' @export getSatDataLayer #' @describeIn satInfo Return Satellite data layer i getSatDataLayer <- function(sat, bcde){ return(sat@layers[[getSatLNBR(sat, bcde)]]) } # Return Satellite object metadata --------------------------------------------- #' @export getSatMeta #' @describeIn satInfo Return Satellite object metadata getSatMeta <- function(sat, bcde){ if(missing(bcde)){ return(sat@meta) } else { return(sat@meta[sat@meta$BCDE %in% bcde, ]) } } # Return template for Satellite object metadata which is based on existing band- #' @export getSatMetaBCDETemplate #' @describeIn satInfo Return template for Satellite object metadata which is based on existing band getSatMetaBCDETemplate <- function(sat, bcde){ meta_template <- getSatMeta(sat, bcde) meta_template$DATE <- NULL meta_template$LAYER <- NULL meta_template$FILE <- NULL meta_template$METAFILE <- NULL return(meta_template) } # Return Satellite object log info --------------------------------------------- #' @export getSatLog #' @describeIn satInfo Return Satellite object log info getSatLog <- function(sat){ return(sat@log) } ################################################################################ # Set entries to the Satellite object ################################################################################ # Set BCDE/data layer names of a Satellite object ------------------------------ #' @export setSatBCDE #' @describeIn satInfo Set BCDE/data layer names of a Satellite object setSatBCDE <- function(sat, bcde){ ## if not supplied, BCDE is being created automatically if (missing(bcde)) bcde <- createSatBCDE(sat) sat@meta$BCDE <- bcde for(i in seq(countSatDataLayers(sat))){ names(sat@layers[[i]]) <- bcde[i] } return(sat) } # If not supplied, automatically create BCDE names of a Satellite object ------- #' @export createSatBCDE #' @describeIn satInfo If not supplied, automatically create BCDE names of a Satellite object createSatBCDE <- function(sat, width = 3, flag = 0, prefix = "B", postfix = "n") { int_nrow <- nrow(sat@meta) int_seq <- 1:int_nrow ch_seq <- formatC(int_seq, width = width, flag = flag) ch_bcde <- paste0(prefix, ch_seq, postfix) return(ch_bcde) } ################################################################################ # Add entries to the Satellite object ################################################################################ # Add additional or overwrite metainformation parameter to Satellite object ---- #' @export addSatMetaParam #' @describeIn satInfo Add additional or overwrite metainformation parameter to Satellite object addSatMetaParam <- function(sat, meta_param){ if("SCENE" %in% colnames(meta_param)){ id <- c("SCENE", "BCDE") } else { id <- "BCDE" } names <- colnames(meta_param) names <- names[-which(names %in% id)] # Parameter already exists: overwrite, otherwise add sat@meta <- sat@meta[, !colnames(sat@meta) %in% colnames(names)] sat@meta <- merge(sat@meta, meta_param, by = id, all.x = TRUE) sat@meta <- if (is.null(sat@meta$LNBR)) { sat@meta[order(sat@meta$SCENE, sat@meta$BCDE), ] } else { sat@meta[order(sat@meta$SCENE, sat@meta$LNBR), ] } return(sat) } # Add metainformation for an additional layer to Satellite object -------------- #' @export addSatMetaEntry #' @describeIn satInfo Add metainformation for an additional layer to Satellite object addSatMetaEntry <- function(sat, meta_param){ if(missing(meta_param)){ meta_param <- data.frame(DATE = as.POSIXlt(Sys.Date(), tz = "UTC")) } lnbr_new <- nrow(getSatMeta(sat)) + 1 meta_param$LNBR <- lnbr_new if("DATE" %in% colnames(meta_param) == FALSE){ meta_param$DATE <- as.POSIXlt(Sys.Date(), tz = "UTC") } if("LAYER" %in% colnames(meta_param) == FALSE){ if(length(getSatDataLayers(sat)) == lnbr_new){ meta_param$LAYER <- getSatLayerfromData(sat, nbr = length(getSatDataLayers(sat))) } else { meta_param$LAYER <- paste0("Layer number ", lnbr_new) } } sat@meta <- plyr::rbind.fill(getSatMeta(sat), meta_param) return(sat) } # Add new log entry to Satellite object ---------------------------------------- #' @export addSatLog #' @describeIn satInfo Add new log entry to Satellite object addSatLog <- function(sat, info = NA_character_, in_bcde = NA_character_, out_bcde = NA_character_){ new_length <- length(getSatLog(sat)) + 1 ps <- sprintf("ps%04d", new_length) sat@log <- append(sat@log, list(list(time = Sys.time(), info = info, in_bcde = in_bcde, out_bcde = out_bcde))) names(sat@log)[new_length] <- ps return(sat) } # Add new Satellite data layer -------------------------------------------------- #' @export addSatDataLayer #' @describeIn satInfo Add new Satellite data layer addSatDataLayer <- function(sat, bcde, data, meta_param, info, in_bcde){ names(data) <- bcde if (inherits(data, "RasterLayer")) { sat@layers[[length(sat@layers) + 1]] <- data } else { entry <- length(sat@layers) for(i in seq(nlayers(data))){ entry <- entry + 1 sat@layers[[entry]] <- data[[i]] } } if(missing(meta_param)){ meta_param = data.frame(BCDE = bcde) } else { meta_param$BCDE = bcde } sat <- addSatMetaEntry(sat, meta_param = meta_param) sat <- addSatLog(sat, info = info, in_bcde = in_bcde, out_bcde = bcde) return(sat) } # Add raster meta data to Satellite object meta data ---- #' @export addRasterMeta2Sat #' @describeIn satInfo Add raster meta data to Satellite object meta data addRasterMeta2Sat <- function(sat){ ## if BCDE is not available, it is automatically created if (is.null(sat@meta$BCDE)) { sat <- setSatBCDE(sat) } if(is.null(sat@meta$SCENE)){ sat@meta$SCENE <- 1 } rst_meta <- data.frame(SCENE = sat@meta$SCENE, BCDE = sat@meta$BCDE, RID = "R00001", XRES = sapply(sat@layers, function(x) raster::xres(x)), YRES = sapply(sat@layers, function(x) raster::yres(x)), NROW = sapply(sat@layers, function(x) raster::nrow(x)), NCOL = sapply(sat@layers, function(x) raster::ncol(x)), NCELL = sapply(sat@layers, function(x) raster::ncell(x)), XMIN = sapply(sat@layers, function(x) raster::xmin(x)), XMAX = sapply(sat@layers, function(x) raster::xmax(x)), YMIN = sapply(sat@layers, function(x) raster::ymin(x)), YMAX = sapply(sat@layers, function(x) raster::ymax(x)), PROJ = sapply(sat@layers, function(x) raster::projection(x)), stringsAsFactors = FALSE) sat <- addSatMetaParam(sat, meta_param = rst_meta) return(sat) } # Create raster meta data ------------------------------------------------------ #' @export createRasterMetaData #' @describeIn satInfo Create raster meta data createRasterMetaData <- function(rst){ rst_meta <- data.frame(XRES = raster::xres(rst), YRES = raster::yres(rst), NROW = raster::nrow(rst), NCOL = raster::ncol(rst), NCELL = raster::ncell(rst), XMIN = raster::xmin(rst), XMAX = raster::xmax(rst), YMIN = raster::ymin(rst), YMAX = raster::ymax(rst), PROJ = raster::projection(rst), stringsAsFactors = FALSE) return(rst_meta) } # Update raster meta data ------------------------------------------------------ #' @export updateRasterMetaData #' @describeIn satInfo Create raster meta data updateRasterMetaData <- function(sat, bcde) { rst_meta <- createRasterMetaData(getSatDataLayer(sat, bcde)) for (i in colnames(rst_meta)) { sat@meta[sat@meta$BCDE == bcde, i] <- rst_meta[, i] } return(sat) } ################################################################################ # Return individual entries of the Satellite object ################################################################################ # Return number of Satellite data layers --------------------------------------- #' @export countSatDataLayers #' @describeIn satInfo Return number of Satellite data layers countSatDataLayers <- function(sat){ return(length(sat@layers)) } # Return parameter (general method implemented by the specific functions below)- #' @param bcde Band code. #' @export getSatParam #' @describeIn satInfo Return parameter (general method implemented by the specific functions below) getSatParam <- function(sat, param, bcde, return_bcde = TRUE){ if(length(which(param == colnames(getSatMeta(sat)))) > 0){ if(param == "BCDE"){ return(getSatMeta(sat)[, which(param == colnames(getSatMeta(sat)))]) } else { if(missing(bcde)){ param <- getSatMeta(sat)[, which(param == colnames(getSatMeta(sat)))] bcde <- as.character(getSatBCDE(sat)) } else { param <- getSatMeta(sat)[, which(param == colnames(getSatMeta(sat)))][match( bcde, getSatMeta(sat)$BCDE)] bcde <- as.character(bcde) } if(return_bcde == TRUE){ attr(param, "names") <- bcde } return(param) } } else { return(NA_character_) } } # Return Band code ------------------------------------------------------------- #' @export getSatBCDE #' @describeIn satInfo Return Band code getSatBCDE <- function(sat, lnbr){ if(missing(lnbr)){ getSatParam(sat, "BCDE", return_bcde = FALSE) } else { getSatParam(sat, "BCDE", return_bcde = FALSE)[lnbr] } } # Return Band IDs -------------------------------------------------------------- #' @export getSatBID #' @describeIn satInfo Return Band IDs getSatBID <- function(sat, bcde){ getSatParam(sat, "BID", bcde, return_bcde = FALSE) } # Return sensor ID ------------------------------------------------------------- #' @export getSatSID #' @describeIn satInfo Return sensor ID getSatSID <- function(sat){ getSatParam(sat, "SID", return_bcde = FALSE)[1] } # Return sensor ---------------------------------------------------------------- #' @export getSatSensor #' @describeIn satInfo Return sensor getSatSensor <- function(sat){ getSatParam(sat, "SENSOR", return_bcde = FALSE)[1] } # Return sensor group ---------------------------------------------------------- #' @export getSatSensorGroup #' @describeIn satInfo Return sensor group getSatSensorGroup <- function(sat){ getSatParam(sat, "SGRP", return_bcde = FALSE)[1] } # Return sensor information ---------------------------------------------------- #' @export getSatSensorInfo #' @describeIn satInfo Return sensor information getSatSensorInfo <- function(sat){ data.frame(SID = getSatSID(sat), SENSOR = getSatSensor(sat), SGRP = getSatSensorGroup(sat)) } # Return spectrum -------------------------------------------------------------- #' @export getSatSpectrum #' @describeIn satInfo Return spectrum getSatSpectrum <- function(sat, bcde){ getSatParam(sat, "SPECTRUM", bcde) } # Return solar band codes ------------------------------------------------------ #' @export getSatBCDESolar #' @describeIn satInfo Return solar band codes getSatBCDESolar <- function(sat){ spectrum <- getSatSpectrum(sat) return(getSatBCDE(sat)[grep("solar", spectrum)]) } # Return thermal band codes ------------------------------------------------------ #' @export getSatBCDEThermal #' @describeIn satInfo Return thermal band codes getSatBCDEThermal <- function(sat){ spectrum <- getSatSpectrum(sat) return(getSatBCDE(sat)[grep("thermal", spectrum)]) } # Return sensor x resolution ----------------------------------------------------- #' @export getSatXRes #' @describeIn satInfo Return sensor x resolution getSatXRes <- function(sat, bcde){ getSatParam(sat, "XRES", bcde) } # Return sensor y resolution ----------------------------------------------------- #' @export getSatYRes #' @describeIn satInfo Return sensor y resolution getSatYRes <- function(sat, bcde){ getSatParam(sat, "YRES", bcde) } # Return mean sensor resolution (mean of x and y res) -------------------------- #' @export getSatRes #' @describeIn satInfo Return mean sensor resolution (mean of x and y res) getSatRes <- function(sat, bcde){ mean(getSatXRes(sat, bcde), getSatYRes(sat, bcde), na.rm = TRUE) } # Return sensor type ----------------------------------------------------------- #' @export getSatType #' @describeIn satInfo Return sensor type getSatType <- function(sat, bcde){ getSatParam(sat, "TYPE", bcde) } # Return CALIB ----------------------------------------------------------------- #' @export getSatCalib #' @describeIn satInfo Return calibration level getSatCalib <- function(sat, bcde){ getSatParam(sat, "CALIB", bcde) } # Return TYPE band codes matching id ------------------------------------------ #' @export getSatBCDEType #' @describeIn satInfo Return TYPE band codes getSatBCDEType <- function(sat, bcde, type){ type <- getSatType(sat, bcde) result <- getSatBCDE(sat)[grep(type, type)] if(length(result) == 0){ result = NA_character_ } return(result) } # Return BCDE matching TYPE ---------------------------------------------------- #' @export getSatBCDEFromType #' @describeIn satInfo Return BCDE matching TYPE getSatBCDEFromType <- function(sat, type = "VIS"){ as.character(stats::na.exclude(sat@meta$BCDE[sat@meta$TYPE == type])) } # Return BCDE matching TYPE ---------------------------------------------------- #' @export getSatBCDEFromSpectrum #' @describeIn satInfo Return BCDE matching TYPE getSatBCDEFromSpectrum <- function(sat, spectrum = "solar"){ as.character(stats::na.exclude(sat@meta$BCDE[sat@meta$SPECTRUM == spectrum])) } # Return SRES band codes matching type ------------------------------------------ #' @export getSatBCDESres #' @describeIn satInfo Return the mean of x and y resolution for band codes matching type getSatBCDESres <- function(sat, bcde, type){ sres <- getSatRes(sat, bcde) result <- getSatBCDE(sat)[grep(type, sres)] if(length(result) == 0){ result = NA_character_ } return(result) } # Return CALIB band codes matching type ------------------------------------------ #' @export getSatBCDECalib #' @describeIn satInfo Return calibration level for band codes matching type getSatBCDECalib <- function(sat, bcde, calib){ calib_val <- getSatCalib(sat, bcde) result <- getSatBCDE(sat)[grep(calib, calib_val)] if(length(result) == 0){ result = NA_character_ } return(result) } # Return CALIB band codes machting type and are solare bands --------------------- #' @export getSatBCDESolarCalib #' @describeIn satInfo Return calibration level for band codes machting type and are solar bands getSatBCDESolarCalib <- function(sat, bcde, calib){ calib_val <- getSatBCDECalib(sat, bcde, calib) result <- getSatBCDESolar(sat)[getSatBCDESolar(sat) %in% calib_val] if(length(result) == 0){ result = NA_character_ } return(result) } # Return CALIB band codes machting type and are thermal bands -------------------- #' @export getSatBCDEThermalCalib #' @describeIn satInfo Return calibration level for band codes machting type and are thermal bands getSatBCDEThermalCalib <- function(sat, bcde, calib){ calib_val <- getSatBCDECalib(sat, bcde, calib) return(getSatBCDEThermal(sat)[getSatBCDEThermal(sat) %in% calib_val]) } # Return band information ------------------------------------------------------ #' @export getSatBandInfo #' @describeIn satInfo Return band information getSatBandInfo <- function(sat, bcde, return_calib = TRUE){ if(return_calib){ result <- data.frame(BID = getSatBID(sat, bcde), #SRES = getSatRes(sat, bcde), TYPE = getSatType(sat, bcde), SPECTRUM = getSatSpectrum(sat, bcde), CALIB = getSatCalib(sat, bcde)) } else { result <- data.frame(BID = getSatBID(sat, bcde), #SRES = getSatRes(sat, bcde), TYPE = getSatType(sat, bcde), SPECTRUM = getSatSpectrum(sat, bcde)) } return(result) } # Return RAD_MAX --------------------------------------------------------------- #' @export getSatRadMax #' @describeIn satInfo Return maximum radiance for bcde getSatRadMax <- function(sat, bcde){ getSatParam(sat, "RADMAX", bcde) } # Return RAD_MIN --------------------------------------------------------------- #' @export getSatRadMin #' @describeIn satInfo Return minimum radiance for bcde getSatRadMin <- function(sat, bcde){ getSatParam(sat, "RADMIN", bcde) } # Return REF_MAX --------------------------------------------------------------- #' @export getSatRefMax #' @describeIn satInfo Return maximum reflectance for bcde getSatRefMax <- function(sat, bcde){ getSatParam(sat, "REFMAX", bcde) } # Return REF_MIN --------------------------------------------------------------- #' @export getSatRefMin #' @describeIn satInfo Return minimum reflectance for bcde getSatRefMin <- function(sat, bcde){ getSatParam(sat, "REFMIN", bcde) } # Return ESD ------------------------------------------------------------------- #' @export getSatESD #' @describeIn satInfo Return earth-sun distance getSatESD <- function(sat){ getSatParam(sat, "ESD")[1] } # Return ESun ------------------------------------------------------------------ #' @export getSatESUN #' @describeIn satInfo Return actual solar TOA irradiance getSatESUN <- function(sat, bcde) { getSatParam(sat, "ESUN", bcde) } # Return SZEN ------------------------------------------------------------------ #' @export getSatSZEN #' @describeIn satInfo Return sun zenith angle getSatSZEN <- function(sat, bcde){ getSatParam(sat, "SZEN", bcde) } # Return SAZM ------------------------------------------------------------------ #' @export getSatSAZM #' @describeIn satInfo Return sun azimuth angle getSatSAZM <- function(sat, bcde){ getSatParam(sat, "SAZM", bcde) } # Return SELV ------------------------------------------------------------------ #' @export getSatSELV #' @describeIn satInfo Return Sun elevation getSatSELV <- function(sat, bcde){ getSatParam(sat, "SELV", bcde) } # Return Layer name from metadata ---------------------------------------------- #' @export getSatMetaLayer #' @describeIn satInfo Return Layer name from metadata getSatMetaLayer <- function(sat, bcde){ getSatParam(sat, "LAYER", bcde) } # Return Layer name from data layer -------------------------------------------- #' @export getSatLayerfromData #' @describeIn satInfo Return Layer name from data layer getSatLayerfromData <- function(sat, bcde, nbr){ if(missing(bcde)){ layers <- sapply(getSatDataLayers(sat), function(x){ names(x) }) if(missing(nbr)){ return(layers) } else { return(layers[nbr]) } } else { names(getSatDataLayer(sat, bcde)) } } # Return LNBR ------------------------------------------------------------------ #' @export getSatLNBR #' @describeIn satInfo Return Layer number getSatLNBR <- function(sat, bcde){ getSatParam(sat, "LNBR", bcde) } # Return LMin ------------------------------------------------------------------ #' @export getSatLMIN #' @describeIn satInfo Return minimum wavelength of the sensor band getSatLMIN <- function(sat, bcde){ getSatParam(sat, "LMIN", bcde) } # Return LMAX ------------------------------------------------------------------ #' @export getSatLMAX #' @describeIn satInfo Return maximum wavelength of the sensor band getSatLMAX <- function(sat, bcde){ getSatParam(sat, "LMAX", bcde) } # Return RADA ------------------------------------------------------------------ #' @export getSatRADA #' @describeIn satInfo Return addition coefficient for SC to radiance conversion getSatRADA <- function(sat, bcde){ getSatParam(sat, "RADA", bcde) } # Return RADM ------------------------------------------------------------------ #' @export getSatRADM #' @describeIn satInfo Return multiplicative coefficient for SC to radiance conversion getSatRADM <- function(sat, bcde){ getSatParam(sat, "RADM", bcde) } # Return REFA ------------------------------------------------------------------ #' @export getSatREFA #' @describeIn satInfo Return addition coefficient for SC to reflectance getSatREFA <- function(sat, bcde){ getSatParam(sat, "REFA", bcde) } # Return REFM ------------------------------------------------------------------ #' @export getSatREFM #' @describeIn satInfo Return multiplicative coefficient for SC to reflectance getSatREFM <- function(sat, bcde){ getSatParam(sat, "REFM", bcde) } # Return BTK1 ------------------------------------------------------------------ #' @export getSatBTK1 #' @describeIn satInfo Return calibration coefficent to convert SC to brightness temperature getSatBTK1 <- function(sat, bcde){ getSatParam(sat, "BTK1", bcde) } # Return BTK2 ------------------------------------------------------------------ #' @export getSatBTK2 #' @describeIn satInfo Return calibration coefficent to convert SC to brightness temperature getSatBTK2 <- function(sat, bcde){ getSatParam(sat, "BTK2", bcde) } # Return PRAD ------------------------------------------------------------------ #' @export getSatPRAD #' @rdname satInfo getSatPRAD <- function(sat, bcde){ getSatParam(sat, "PRAD", bcde) } # Return DATE ------------------------------------------------------------------ #' @export getSatDATE #' @describeIn satInfo Return DATE getSatDATE <- function(sat, bcde){ getSatParam(sat, "DATE", bcde) } # Return projection ------------------------------------------------------------------ #' @export getSatProjection #' @describeIn satInfo Return projection getSatProjection <- function(sat, bcde){ prj <- getSatParam(sat, "PROJ", bcde) if (is.na(prj)) prj <- projection(sat@layers[[1]]) return(prj) } satellite/R/calcTOAIrradTable.R0000644000176200001440000001223513572466603016003 0ustar liggesusersif ( !isGeneric("calcTOAIrradTable") ) { setGeneric("calcTOAIrradTable", function(x, ...) standardGeneric("calcTOAIrradTable")) } #' Get top of atmosphere solar irradiance using readily tabulated values #' #' @description #' Get mean extraterrestrial solar irradiance (ESun) using published values. #' #' @param x A Satellite object or sensor id ("LT4, LT5, LE7") as character. #' @param normalize Logical; if \code{TRUE}, ESun is normalized to mean #' earth-sun distance. #' @param esd Earth-sun distance (AU, can be estimated using #' \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not #' supplied and necessary for normalization, it is tried to take it from the #' metadata, otherwise it is estimated by the day of the year using #' \code{\link{calcEarthSunDist}}. #' #' @export calcTOAIrradTable #' #' @name calcTOAIrradTable #' #' @details Currently implemented sensors are Landsat 4, 5 and 7. #' #' If results should not be normalized to a mean earth-sun distance, the #' actual earth-sun distance is approximated by the day of the year using #' \code{\link{calcEarthSunDist}}. #' #' Please note that ESun values are not required for converting Landsat 8 data #' to reflectance as the corresponding metadata files provide coefficients #' necessary to convert digital numbers to radiance and reflectance (taken from #' \url{http://www.gisagmaps.com/landsat-8-atco/}. #' #' @references Tabulated values of the solar irradiance for all Landsat sensors #' are taken from \url{https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product}. #' #' @seealso \code{\link{calcTOAIrradRadRef}} for the computation of the solar #' irradiance based on maximum radiation and reflection values of the dataset or #' \code{\link{calcTOAIrradModel}} for the computation of the solar irradiance #' based on look-up tables for the sensor's relative spectral response and solar #' irradiation spectral data. #' #' See \code{\link{calcEarthSunDist}} for calculating the earth-sun #' distance based on the day of the year which is called by this function if #' ESun should be corrected for actual earth-sun distance. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LE07*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' calcTOAIrradTable(sat) #' #' calcTOAIrradTable(x = "LE7", normalize = FALSE, #' calcEarthSunDist("2015-01-01")) #' NULL # Function using satellite object ---------------------------------------------- #' @return Satellite object with ESun information added to the metadata #' @rdname calcTOAIrradTable setMethod("calcTOAIrradTable", signature(x = "Satellite"), function(x, normalize = TRUE, esd){ if(normalize == FALSE & missing(esd)){ esd <- getSatESD(x) if(is.na(esd)){ esd <- calcEarthSunDist(date) } } if(normalize == TRUE){ esun <- calcTOAIrradTable(x = getSatSID(x), normalize = normalize) } else { esun <- calcTOAIrradTable(x = getSatSID(x), normalize = normalize, esd = esd) } bcde <- names(esun) x <- addSatMetaParam(x, meta_param = data.frame( BCDE = bcde, ESUN = as.numeric(esun))) return(x) }) # Function using factor -------------------------------------------------------- #' @return Vector object containing ESun for the respective band(s) #' @rdname calcTOAIrradTable setMethod("calcTOAIrradTable", signature(x = "factor"), function(x, normalize = TRUE, esd){ x <- as.character(x) if(missing(esd)){ eSun <- calcTOAIrradTable(x, normalize) } else { eSun <- calcTOAIrradTable(x, normalize, esd) } return(eSun) }) # Function using character ----------------------------------------------------- #' @return Vector object containing ESun for the respective band(s) #' @rdname calcTOAIrradTable setMethod("calcTOAIrradTable", signature(x = "character"), function(x, normalize = TRUE, esd){ if(x %in% c("LE7", "LE07")) { eSun <- lut$L7_ESUN } else if(x %in% c("LT5", "LT05")) { eSun <- lut$L5_ESUN } else if(x %in% c("LT4", "LT04")) { eSun <- lut$L4_ESUN # } else if(x == "GLS5") { # eSun <- lut$GLS5_ESUN } else if(x %in% c("LC8", "LC08")) { stop("ESun values are not provided for Landsat 8, see ?calcTOAIrradTable.") } else { stop(paste0("Satellite ID ", x, " is not supported, yet.")) } if(normalize == FALSE){ if(missing(esd)){ stop("Variable esd is missing.") } eSun <- eSun / esd**2 } return(eSun) })satellite/R/alignGeometry.R0000644000176200001440000001076013572463733015413 0ustar liggesusersif ( !isGeneric("alignGeometry") ) { setGeneric("alignGeometry", function(x, ...) standardGeneric("alignGeometry")) } #' Align raster geometry between two data sets #' #' @description #' Align raster data by bringing it in the same geometry and extent. #' If the data set is not in the same projection as the template, the alignment #' will be computed by reprojection. If the data has already the same #' projection, the data set will be cropped and aggregated prior to resampling #' in order to reduce computation time. #' #' @param x Satellite or Raster* object to be resampled. #' @param template Raster* or spatial data set from which geometry can be #' extracted. #' @param type Type of bands (e.g. VIS, NIR) which should be considered. If not #' supplied, all types will be processed depending and bands to be processed can #' be defined by band_codes. #' @param band_codes Band ID(s) to be resampled. If not supplied and type is #' not given, too, all bands will be considered for resampling. #' @param method Method for resampling; "bilinear" for bilinear interpolation #' (default) or "ngb" for nearest neighbor interpolation. See e.g. #' \code{\link{resample}}, \code{\link{projectRaster}}. #' #' @export alignGeometry #' #' @name alignGeometry #' @aliases alignGeometry,Satellite-method #' #' @examples #' path <- system.file("testdata/LC8", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC8*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' alignGeometry(sat, template = getSatDataLayer(sat, "B008n"), #' band_codes = "B001n") NULL # Function using satellite object ---------------------------------------------- #' @return Satellite object with aligned geometries. #' @rdname alignGeometry setMethod("alignGeometry", signature(x = "Satellite"), function(x, template, band_codes, type, method = c("bilinear", "ngb")){ method <- method[1] if(!missing(type)){ band_codes <- getSatBCDE(x)[which(getSatType(x) == type)] } else if(missing(band_codes)){ band_codes <- getSatBCDE(x) } for(bcde in band_codes){ ag <- alignGeometry(x = getSatDataLayer(x, bcde), template = template, method = method) layer_bcde <- paste0(bcde, "_AG") meta_param <- getSatMetaBCDETemplate(x, bcde) meta_param$BCDE <- layer_bcde meta_param$XRES <- xres(template) meta_param$YRES <- yres(template) info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = ag, meta_param = meta_param, info = info, in_bcde = bcde) } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @return raster::RasterStack object with aligned layers #' @rdname alignGeometry setMethod("alignGeometry", signature(x = "RasterStack"), function(x, template, method = c("bilinear", "ngb")){ method <- method[1] for(l in seq(nlayers(x))){ x[[l]] <- alignGeometry(x[[l]], template, method) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @return raster::RasterLayer object with aligned layer #' @rdname alignGeometry setMethod("alignGeometry", signature(x = "RasterLayer"), function(x, template, method = c("bilinear", "ngb")){ method <- method[1] if(raster::projection(x) == raster::projection(template)){ x <- raster::crop(x, template, snap = "out") if (inherits(template, "RasterLayer")) { if(x@ncols / template@ncols >= 2){ factor <- floor(x@ncols/template@ncols) x <- raster::aggregate(x, fact = factor, fun = mean, expand = TRUE) } x <- raster::resample(x, template, method = method) } } else { x <- raster::projectRaster(x, template, method = method) } return(x) }) satellite/R/satellite-package.R0000644000176200001440000000422513572463522016157 0ustar liggesusers#' Smorgasbord for remote sensing functions #' #' The package provides a variety of functions which are useful for #' handling, manipulating and visualizing remote sensing data. #' #' @name satellite-package #' @aliases satellitepackage #' @docType package #' @title Smorgasboard for remote sensing functions. #' @author Thomas Nauss, Hanna Meyer, Florian Detsch, Tim Appelhans \cr #' \cr #' \emph{Maintainer:} Florian Detsch \email{fdetsch@@web.de} #' #' @import methods raster Rcpp #' @importFrom stats mvfft #' @importFrom stats4 plot #' #' @rawNamespace useDynLib(satellite, .registration = TRUE) #' #' @references Some functions are taken and/or adopted from Sarah C. Goslee #' (2011). Analyzing Remote Sensing Data in R: The landsat Package. Journal of #' Statistical Software, 43(4), 1-25. Online available at #' \url{http://www.jstatsoft.org/v43/i04/}. #' #' @keywords package #' NULL #' #' @docType data #' @name l7 #' @title Landsat 7 sample data #' @description This dataset comes from the USGS. It contains part of the #' Landsat 7 scene LE07_L1TP_195025_20010730_20170204_01_T1 (Collection 1 #' Level-1) from 2001-07-30 over Maburg, Germany. #' @details Use of this data requires your agreement to the USGS regulations on #' using Landsat data. #' @format \code{RasterStack} with bands 1-8 (incl. QA) of 41 by 41 pixels. #' @source #' \url{https://earthexplorer.usgs.gov/} #' @examples #' plotRGB(l7, r = 3, b = 1, stretch = "hist") NULL #' #' @docType data #' @name l8 #' @title Landsat 8 sample data #' @description This dataset comes from the USGS. It contains part of the #' Landsat 8 scene LC08_L1TP_195025_20130707_20170503_01_T1 (Collection 1 #' Level-1) from 2013-07-07 over Maburg, Germany. #' @details Use of this data requires your agreement to the USGS regulations on #' using Landsat data. #' @format \code{RasterStack} with bands 1-7, 9-11 (incl. QA) of 41 by 41 pixels. #' @source #' \url{https://earthexplorer.usgs.gov/} #' @examples #' plotRGB(l8, r = 4, g = 3, b = 2, stretch = "hist") # true-color composite #' plotRGB(l8, r = 5, g = 4, b = 3, stretch = "hist") # false-color composite NULL satellite/R/AAAsatelliteClasses.R0000644000176200001440000000307013572463522016404 0ustar liggesusers#' An S4 class to represent a satellite data file #' #' @slot name name of the data file without extension #' @slot filepath full path and file of the data file #' @slot path path to the data file #' @slot file filename incl. extension of the data file #' @slot extension extension of the data file #' #' @exportClass SatelliteInfo #' setClass("SatelliteInfo", representation( name ="character", filepath = "character", path = "character", file = "character", extension = "character" ) ) #' An S4 class to represent satellite metadata #' #' @slot meta a data frame object containing the data #' #' @exportClass SatelliteMetaData #' setClass("SatelliteMetaData", representation( meta = "data.frame" ) ) #' An S4 class to represent satellite data #' #' @slot layers a list object containing individual RasterLayer objects #' #' @exportClass SatelliteLayers #' setClass("SatelliteLayers", representation( layers = "list" ) ) #' An S4 class to represent satellite log data #' #' @slot log a list object containing information on individual processing steps #' #' @exportClass SatelliteLog #' setClass("SatelliteLog", representation( log = "list" ) ) #' An S4 class to represent a complete satellite dataset #' #' @exportClass Satellite #' setClass("Satellite", contains = c("SatelliteLayers", "SatelliteMetaData", "SatelliteLog") ) satellite/R/calcEarthSunDist.R0000644000176200001440000000607113572463522016001 0ustar liggesusers#' Compute earth-sun distance based on day of the year #' #' @description #' The earth-sun distance for a particular day of the year is computed based on #' one of several empirical formulas. #' #' @param date Date of the sensor overpass; either a character string in a #' native date format (e.g. "YYYY-MM-DD", see \code{\link{as.Date}}) or a POSIX* #' object (see \code{\link{as.POSIXct}}). #' @param formula Formula to be applied, specified through the name of the #' author, i.e. one of "Spencer", "Mather", "ESA" or "Duffie" (see 'Details'). #' #' @return Numeric earth-sun distance (in AU) or, if \code{formula = "Duffie"}, #' the relative squared earth--sun distance on the given day. #' #' @export calcEarthSunDist #' #' @details Computation of earth-sun distance using formulas provided by #' Spencer (1971), Mather (2005) or ESA. If \code{formula = "Duffie"}, the #' inverse squared relative earth--sun distance is returned as proposed by #' Duffie and Beckman (1980). #' #' @references The formulas are taken from the following sources: #' \itemize{ #' \item Spencer: Spencer JW (1971) Fourier series representation of the position of #' the sun. Search 2/5. Taken from #' \url{https://goo.gl/lhi9UI}. #' #' \item Mather: Mather PM (2005) Computer Processing of Remotely-Sensed #' Images: An Introduction. Wiley: Chichester, ISBN: 978-0-470-02101-9, #' \url{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470021012.html}. #' #' \item ESA: ESA Earth Observation Quality Control: Landsat frequently asked questions. #' #' \item Duffie: Duffie JA, Beckman WA (2013) Solar Engineering of Thermal #' Processes. Wiley: Hoboken, New Jersey, ISBN: 978-0-470-87366-3, #' \url{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470873663.html}. #' } #' See also: Bird R, Riordan C (1984) Simple solar spectral model for direct and #' diffuse irradiance on horizontal and tilted planes at the Earth's surface for #' cloudless atmospheres. Task No. 3434.10, Solar Energy Research Institute: #' Golden, Colorado, \url{http://www.nrel.gov/docs/legosti/old/2436.pdf}. #' #' @examples #' calcEarthSunDist(date = "2015-01-01", formula = "Spencer") # absolute #' calcEarthSunDist(date = "2015-01-01", formula = "Duffie") # relative #' calcEarthSunDist <- function(date, formula = c("Spencer", "Mather", "ESA", "Duffie")){ ## if not supplied, formula defaults to "Spencer" formula <- formula[1] day <- as.numeric(strftime(date, format = "%j")) if(formula == "Spencer"){ pos <- 2 * pi * (day - 1) / 365 (1/(1.000110 + 0.034221 * cos(pos) + 0.001280 * sin(pos) + 0.000719 * cos(2 * pos) + 0.000077 * sin(2 * pos)))**0.5 } else if(formula == "Mather"){ 1/(1 - 0.016729 * cos(0.9856 * (day - 4))) } else if(formula == "ESA"){ 1 - 0.016729 * cos((2 * pi) * (0.9856 * (day - 4) / 360)) } else if (formula == "Duffie") { 1 + 0.033 * cos(day * 2 * pi / 365) } else { stop("Please specify a valid 'formula' (see ?calcEarthSunDist).\n") } } satellite/R/lutInfo.R0000644000176200001440000001223113572467453014223 0ustar liggesusers#' Get or access internal LUT values used by various functions #' #' @description #' Get internal look-up table (LUT) values from sysdata.rda which have been #' compiled using data-raw/lut_data.R. Metadata is stored in \code{lut$meta}. #' #' @param bcde Band code as returned e.g. from \code{\link{lutInfoBCDEFromBID}}. #' @param bid Band id as returned e.g. from \code{\link{lutInfoBIDFromBCDE}}. #' @param sid Sensor id as returned e.g. from \code{\link{lutInfoSensorFromSID}}. #' @param file Filename of a remote sensing data file #' @param files Filename (or filepath) of one or more remote sensing data #' filenames #' #' @return List containing several \code{data.frame} objects with LUT values. #' #' @export lutInfo #' #' @details The functions above return the following information: #' \itemize{ #' \item \code{lutInfoBandsFromSID} returns the band info block. #' \item \code{lutInfoBCDEFromBID} returns the band code. #' \item \code{lutInfoBIDFromBCDE} returns the band ids. #' \item \code{lutInfoRSRromSID} returns the relative spectral response (rsr) #' for the sensor. #' \item \code{lutInfoSensorFromSID} returns the sensor name. #' } #' #' The LUT contains the following band information taken, if not specified #' otherwise, from the #' \href{https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products}{USGS Landsat FAQ}: #' \describe{ #' \item{l4_band_wl}{Minimum/maximum wavelength for Landsat 4 bands.} #' \item{l5_band_wl}{Minimum/maximum wavelength for Landsat 5 bands.} #' \item{l7_band_wl}{Minimum/maximum wavelength for Landsat 7 bands.} #' \item{l8_band_wl}{Minimum/maximum wavelength for Landsat 8 bands.} #' \item{l7_rsr}{Landat 7 rsr (nm-1) taken from the #' \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} #' of the USGS Landsat FAQ.} #' \item{l8_rsr}{Landat 8 rsr (nm-1) taken from the #' \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} #' of the USGS Landsat FAQ.} #' \item{solar}{Solar irradiance (W m-2 nm-1) taken from the #' \href{http://rredc.nrel.gov/solar/spectra/am0/modtran.html}{National Renewable #' Energy Laboratory}.} #' \item{l7_esun}{Tabulated ESun values from #' \href{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}{tab 11.3 (Thuillier spectrum)} #' of the Landsat7 handbook.} #' \item{l5_esun, l4_esun}{Tabulated ESun values from #' \href{ftp://ftp.vt.tpu.ru/study/Tokareva/public/English_prof/GIS/L5TMLUTIEEE2003.pdf}{Chander #' G, Markham B (2003) Revised Landsat-5 TM radiometric calibration procedures #' and postcalibration dynamic ranges. IEEE Transaction on Geoscience and #' Remote Sensing 41/11, doi:10.1109/LGRS.2007.898285}.} #' } #' #' @examples #' ls_li <- lutInfo() #' # str(ls_li) #' lutInfo <- function(){ return(lut) } # Return band information block ------------------------------------------------ #' @export lutInfoBandsFromSID #' #' @describeIn lutInfo #' lutInfoBandsFromSID <- function(sid){ return( lut[which(names(lut) == (lut$BANDS[which(names(lut$BANDS) == sid)]))][[1]]) } # Return sensor ---------------------------------------------------------------- #' @export lutInfoSensorFromSID #' #' @describeIn lutInfo #' lutInfoSensorFromSID <- function(sid){ return(lut$SENSORS[which(names(lut$SENSORS) == sid)]) } # Return band code ------------------------------------------------------------- #' @export lutInfoBCDEFromBID #' #' @describeIn lutInfo #' lutInfoBCDEFromBID <- function(sid, bid){ act_bands <- lutInfoBandsFromSID(sid) if(missing(bid)){ return(act_bands$BCDE) } else { return(act_bands$BCDE[act_bands$BID == bid]) } } # Return band ids -------------------------------------------------------------- #' @export lutInfoBIDFromBCDE #' #' @describeIn lutInfo #' lutInfoBIDFromBCDE <- function(bcde, sid){ act_bands <- lutInfoBandsFromSID(sid) return(act_bands$BID[act_bands$BCDE == bcde]) } # Return rsr ------------------------------------------------------------------- #' @export lutInfoRSRromSID #' #' @describeIn lutInfo #' lutInfoRSRromSID <- function(sid){ return(lut[names(lut) == lut$RSR[which(names(lut$RSR) == sid)]][[1]]) } # Return sensor id from filename -------------------------------------------------- #' @export lutInfoSIDfromFilename #' #' @describeIn lutInfo #' lutInfoSIDfromFilename <- function(files){ ## original sensor pattern test <- sapply(lut$SENSOR_ID_PATTERN$PATTERN, function(x) { grepl(x, basename(files)) }) ## alternative sensor pattern if (!any(test)) { test <- sapply(lut$SENSOR_ID_PATTERN$PATTERN2, function(x) { grepl(x, basename(files)) }) } if (inherits(test, "matrix")) { return(colnames(test)[test[1, ]]) } else { return(names(test)[test]) } } # Return group id from filename -------------------------------------------------- #' @export lutInfoSGRPfromFilename #' #' @describeIn lutInfo #' lutInfoSGRPfromFilename <- function(file){ sid <- lutInfoSIDfromFilename(file) if(length(sid) == 0){ return(FALSE) } else { rid <- apply(lut$SENSOR_ID_PATTERN == sid, 1, FUN = any) return(lut$SENSOR_ID_PATTERN$SGRP[rid]) } } satellite/R/extend.R0000644000176200001440000000530413572463522014066 0ustar liggesusersif ( !isGeneric("extend") ) { setGeneric("extend", function(x, ...) standardGeneric("extend")) } #' Extend a Satellite object #' #' @description #' The function is a wrapper around \code{\link[raster]{extend}} to easily #' extend a Satellite object to a larger spatial extent. #' #' @param x Satellite object. #' @param y Target \code{Extent}, see \code{\link[raster]{extent}}. #' @param subset Logical. If \code{TRUE} (default), all layers but the extended #' ones are being dropped, else the extended layers are appended to the initial #' Satellite object. #' @param value Fill value assigned to new cells passed to #' \code{\link[raster]{extend}}, defaults to \code{NA}. #' #' @return A Satellite object consisting of extended layers only or, if #' \code{subset = FALSE}, a Satellite object with the extended layers appended. #' #' @export extend #' #' @name extend #' @aliases extend,Satellite-method #' #' @seealso This function is a wrapper around \code{\link[raster]{extend}}. #' #' @examples #' \dontrun{ #' ## sample data #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' ## geographic extent of georg-gassmann-stadium (utm 32-n) #' ext_ggs <- raster::extent(482606.4, 482781.4, 5627239, 5627489) #' #' ## extend satellite object by specified extent #' sat_ggs <- extend(sat, ext_ggs) #' #' plot(sat) #' plot(sat_ggs) #' } setMethod("extend", signature(x = "Satellite"), function(x, y, subset = TRUE, value = NA) { rad_bands <- getSatBCDE(x) for (bcde_rad in rad_bands) { ref <- raster::extend(getSatDataLayer(x, bcde_rad), y, value) # keep all metadata except for file path since extended # layers are in memory and set calib column flag. meta_param <- getSatMeta(x, bcde_rad) meta_param$CALIB <- "extended" meta_param$FILE <- NULL info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = bcde_rad, data = ref, meta_param = meta_param, info = info, in_bcde = bcde_rad) } if(subset == TRUE){ x <- subset(x, cid = "extended") #reset LNBR (dirty hack) x@meta$LNBR <- rep(1:nrow(x@meta)) x@meta$CALIB <- "SC" } return(x) } ) satellite/R/demTools.R0000644000176200001440000000716713572463522014376 0ustar liggesusersif ( !isGeneric("demTools") ) { setGeneric("demTools", function(x, ...) standardGeneric("demTools")) } #' Compute terrain characteristics from digital elevation models #' #' @description #' Compute terrain characteristics from digital elevation models (DEM) using #' \code{raster::terrain} or \code{raster::hillShade}. #' @param x A DEM provided as an object of class Satellite or RasterLayer. #' @param method Currently "slope", "aspect" and "hillshade" are implemented. #' @param bcde The name of the DEM layer in the Satellite object. #' #' @seealso #' \code{raster::terrain}, \code{raster::hillShade}. #' #' @export demTools #' @name demTools #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' ## dem #' files_dem <- list.files(path, pattern = "DEM", full.names = TRUE) #' DEM <- raster(files_dem) #' #' sat <- addSatDataLayer(sat, data = DEM, info = NULL, bcde = "DEM", in_bcde="DEM") #' sat <- demTools(sat) NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added layer containing calculated #' terrain information; if x is a \code{raster::RasterLayer} object, a #' \code{raster::RasterLayer} object with calculated terrain information. #' @rdname demTools setMethod("demTools", signature(x = "Satellite"), function(x,method = "hillShade",bcde = "DEM"){ sunElev <- NULL sunAzim <- NULL if (method == "hillShade"){ if (sum(stats::complete.cases(unique(getSatSELV(x)))) > 1 || sum(stats::complete.cases(unique(getSatSAZM(x))))>1){ print("Warning: Satellite data have different Sun elevation or Sun azimuth values. Only the first element is used")} sunElev <- as.numeric(getSatSELV(x)[1]) sunAzim <- as.numeric(getSatSAZM(x)[1]) } result <- demTools(getSatDataLayer(x, bcde), sunElev = sunElev, sunAzim = sunAzim, method = method) x <- addSatDataLayer(x, bcde = method, data = result, info = paste0( "Add layer ", method), in_bcde = bcde) } ) # Function using raster::RasterLayer object ------------------------------------ #' @param sunElev If \code{method = "hillShade"}, the elevation angle of the #' sun in degrees. See parameter \code{angle} in \code{\link{hillShade}}. #' @param sunAzim If \code{method = "hillShade"}, the sun azimuth angle in #' degree. See parameter \code{direction} in \code{\link{hillShade}}. #' @rdname demTools setMethod("demTools", signature(x = "RasterLayer"), function(x, sunElev, sunAzim, method = "hillShade"){ if(missing(sunElev) | missing(sunAzim)) stop("Parameters 'sunElev' and/or 'sunAzim' missing.\n") if (method == "slope"){ result <- raster::terrain(x, opt = "slope") } if (method == "aspect"){ result <- raster::terrain(x, opt = "aspect") } if(method == "hillShade"){ slope <- raster::terrain(x, opt = "slope") aspect <- raster::terrain(x, opt = "aspect") result <- raster::hillShade(slope = slope, aspect = aspect, angle = sunElev, direction = sunAzim) } return(result) })satellite/R/calcPathRadDOS.R0000644000176200001440000002721013572467542015321 0ustar liggesusersif ( !isGeneric("calcPathRadDOS") ) { setGeneric("calcPathRadDOS", function(x, ...) standardGeneric("calcPathRadDOS")) } #' Compute path radiance based on the dark object method #' #' @description #' Compute an estimated path radiance for all sensor bands, which can then be #' used to roughly correct the radiance values for atmospheric scattering. Path #' radiance estimation is based on a dark object method. #' #' @param x A Satellite object or the value (scaled count) of a dark object in #' \code{bnbr} (e.g. minimum raw count of selected raster \code{bnbr}). If x is #' a Satellite object, the value is computed using \code{\link{calcDODN}}. #' @param bnbr Band number for which DNmin is valid. #' @param band_wls Band wavelengths to be corrected; \code{data.frame} with min #' (max) in first (second) column, see details. #' @param radm Multiplicative coefficient for radiance transformation (i.e. #' slope). #' @param rada Additive coefficient for radiance transformation (i.e. offset). #' @param szen Sun zenith angle. #' @param esun Actual (i.e. non-normalized) TOA solar irradiance, e.g. returned #' by \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} or #' \code{\link{calcTOAIrradModel}}. #' @param model Model to be used to correct for 1\% scattering (DOS2, DOS4; must #' be the same as used by \code{\link{calcAtmosCorr}}). #' @param esun_method If x is a Satellite object, name of the method to be used #' to compute esun using one of \code{\link{calcTOAIrradRadRef}} ("RadRef"), #' \code{\link{calcTOAIrradTable}} ("Table") or \code{\link{calcTOAIrradModel}} #' ("Model") #' @param dos_adjust Assumed reflection for dark object adjustment; defaults to 0.01. #' @param scat_coef Scattering coefficient; defaults to -4.0. #' #' @export calcPathRadDOS #' #' @name calcPathRadDOS #' #' @details #' If x is a Satellite object, the minimum raw count value (x) is computed using #' \code{\link{calcDODN}}. If the TOA solar irradiance is not part of the #' Satellite object's metadata, it is computed using #' \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} or #' \code{\link{calcTOAIrradModel}}. #' #' The dark object subtraction approach is based on an approximation #' of the atmospheric path radiance (i.e. upwelling radiation which is #' scattered into the sensors field of view, aka haze) using the reflectance of a #' dark object (i.e. reflectance ~1\%). #' #' Chavez (1988) proposed a method which uses the dark object reflectance #' in one band to predict the corresponding path radiances in all other #' \code{band_wls}. This is done using a relative radiance model which depends on #' the wavelength and overall atmospheric optical thickness (which is estimated #' based on the dark object's DN value). This has the advantage that the path #' radiance is actually correlated across different sensor \code{band_wls} and #' not computed individually for each band using independent dark objects. He #' proposed a relative radiance model which follows a wavelength dependent #' scattering that ranges from a power of -4 over -2, -1, -0.7 to -0.5 for very #' clear over clear, moderate, hazy to very hazy conditions. The relative #' factors are computed individually for each 1/1000 wavelength within each band #' range and subsequently averaged over the band as proposed by Goslee (2011). #' #' The atmospheric transmittance towards the sensor (Tv) is approximated by #' 1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992) #' #' The atmospheric transmittance from the sun (Tz) is approximated by the #' cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh #' scattering (DOS4, Moran et al. 1992). #' #' The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996) #' or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992). #' #' Equations for the path radiance are taken from Song et al. (2001). #' #' For some sensors, the band wavelengths are already included. See #' \code{lutInfo()[grepl("_BANDS", names(lutInfo()$META))]} if your sensor is #' included. To retrieve a sensor, use \code{lutInfo()$_BANDS}. #' #' @references Chavez Jr PS (1988) An improved dark-object subtraction technique #' for atmospheric scattering correction of multispectral data. Remote Sensing #' of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at #' \url{http://www.sciencedirect.com/science/article/pii/0034425788900193}. #' #' Chavez Jr PS (1996) Image-based atmospheric corrections revisited and #' improved. Photogrammetric Engineering and Remote Sensing 62/9, #' available online at #' \url{https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved}. #' #' Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat #' Package. Journal of Statistical Software, 43/4, 1-25, available online at #' \url{http://www.jstatsoft.org/v43/i04/}. #' #' Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evlauation of simplified #' procedures for rretrieval of land surface reflectane factors from satellite #' sensor output.Remote Sensing of Environment 41/2-3, 169-184, #' doi:10.1016/0034-4257(92)90076-V, available online at #' \url{http://www.sciencedirect.com/science/article/pii/003442579290076V}. #' #' Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification #' and Change Detection Using Landsat TM Data: When and How to Correct #' Atmospheric Effects? Remote Sensing of Environment 75/2, #' doi:10.1016/S0034-4257(00)00169-3, available online at #' \url{http://www.sciencedirect.com/science/article/pii/S0034425700001693} #' #' If you refer to Sawyer and Stephen 2014, please note that eq. 5 is wrong. #' #' @seealso This function is used by \code{\link{calcAtmosCorr}} to #' compute the path radiance. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- calcTOAIrradModel(sat) #' #' bds <- "B002n" #' val <- calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bds))), #' bnbr = getSatLNBR(sat, bds), #' band_wls = data.frame(LMIN = getSatLMIN(sat, getSatBCDESolar(sat)), #' LMAX = getSatLMAX(sat, getSatBCDESolar(sat))), #' radm = getSatRADM(sat, getSatBCDESolar(sat)), #' rada = getSatRADA(sat, getSatBCDESolar(sat)), #' szen = getSatSZEN(sat, getSatBCDESolar(sat)), #' esun = getSatESUN(sat, getSatBCDESolar(sat)), #' model = "DOS2", #' scat_coef = -4) #' val NULL # Function using satellite object ---------------------------------------------- #' @return Satellite object with path radiance for each band in the metadata #' (W m-2 micrometer-1) #' @rdname calcPathRadDOS setMethod("calcPathRadDOS", signature(x = "Satellite"), function(x, model = c("DOS2", "DOS4"), esun_method = "RadRef") { # if not supplied, model defaults to DOS2 model <- model[1] # Compute TOA solar irradiance information if necessary if(any(is.na(getSatESUN(x, getSatBCDESolar(x))))){ # Compute toa irradiance for all solar band layers if(esun_method == "Table"){ x <- calcTOAIrradTable(x, normalize = TRUE) } else if(esun_method == "Model"){ x <- calcTOAIrradModel(x, model = "MNewKur", normalize = TRUE) } else if(esun_method == "RadRef"){ x <- calcTOAIrradRadRef(x, normalize = "TRUE") } } # Get solar bands with calibration information equals scaled counts sc_bands <- getSatBCDESolarCalib(x, calib = "SC") # Take care of dark object values bcde <- "B002n" dn_min <- calcDODN(getSatDataLayer(x, bcde)) # Take care of path radiance path_rad <- calcPathRadDOS(x = dn_min, bnbr = getSatLNBR(x, bcde), band_wls = data.frame( LMIN = getSatLMIN(x, sc_bands), LMAX = getSatLMAX(x, sc_bands)), radm = getSatRADM(x, sc_bands), rada = getSatRADA(x, sc_bands), szen = getSatSZEN(x, sc_bands), esun = getSatESUN(x, sc_bands), model = model) x <- addSatMetaParam(x, meta_param = data.frame( BCDE = names(path_rad), PRAD = as.numeric(path_rad))) return(x) }) # Function using numeric ------------------------------------------------------- #' @return Vector object with path radiance values for each band #' (W m-2 micrometer-1) #' @rdname calcPathRadDOS setMethod("calcPathRadDOS", signature(x = "numeric"), function(x, bnbr, band_wls, radm, rada, szen, esun, model = c("DOS2", "DOS4"), scat_coef = c(-4.0, -2.0, -1.0, -0.7, -0.5), dos_adjust = 0.01) { # if not supplied, model defaults to DOS2 model <- model[1] # if not supplied, scat_coef defaults to -4.0 scat_coef <- scat_coef[1] # Define relative scattering model based on wavelength dependent # scattering processes and different atmospheric optical thiknesses. # Resulting coefficients are the band wavelength to the power of # the respective scattering coefficients defined for the model. band_wls_mat <- as.matrix(band_wls) scattering_model <- ScatteringModel(band_wls_mat, scat_coef) # Compute multiplication factors which relate the scattering model # coefficents between the individual band_wls and the one band which # has been used for the eytraction of the dark object radiation. basline_factor <- scattering_model[bnbr] scattering_factors <- scattering_model / basline_factor # Compute radiation multiplication factors for radiance conversion # (i.e.RADM) and normalize the factors to the multiplication factor # of the band that has been used for the dark object eytraction. normalized_radm <- radm / radm[bnbr] # Compute first estimate of path radiance for all band_wls based on # the values the band used to derive the dark object properties lp_min <- radm[bnbr] * x + rada[bnbr] lp_min_band_wls <- lp_min * scattering_factors # Compute a correction term for the path radiance values to consider # a minimum surface reflection wich one can always eypect. esun cos_szen <- cos(szen * pi / 180.0) if(model == "DOS2"){ tv <- 1.0 tz <- cos_szen edown <- 0.0 } else if(model == "DOS4"){ tv <- NA tz <- NA edown <- NA } lp_adj <- dos_adjust * (esun * cos_szen * tz + edown) * tv / pi # Compute final path radiance estimate for all band_wls lp <- lp_min_band_wls - lp_adj return(lp) }) satellite/R/calcDODN.R0000644000176200001440000000515213572463733014153 0ustar liggesusersif ( !isGeneric("calcDODN") ) { setGeneric("calcDODN", function(x, ...) standardGeneric("calcDODN")) } #' Compile dark object DN for given sensor band #' #' @description #' The function estimates the DN value of a "dark object" which is used for #' atmospheric correction using the DOS2 and DOS4 model. Therefore, the #' frequency distribution of the smallest 1\% of the data values is analyzed #' and the value for which the first derivate has the absolute maximum is #' taken as the DN for a dark object. #' #' @param x Satellite object or RasterLayer with sensor band data, e.g. returned #' by \code{\link{getSatDataLayer}}. #' @param bcde If 'x' is a Satellite object, a band code as character. #' #' @return Numeric value of the DN for the dark object. #' #' @details The DN for a dark object is extracted from a histogram similar to #' Chavez (1988). #' #' @references Chavez Jr PS (1988) An improved dark-object subtraction technique #' for atmospheric scattering correction of multispectral data. Remote Sensing #' of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at #' \url{http://www.sciencedirect.com/science/article/pii/0034425788900193} #' #' @seealso The DN is used by \code{\link{calcPathRadDOS}} for computing the #' path radiance based on the dark object method. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' calcDODN(sat, bcde = "B002n") #' calcDODN(getSatDataLayer(sat, bcde = "B002n")) #' #' @export calcDODN #' @name calcDODN NULL # Function using Satellite object ---------------------------------------------- #' @rdname calcDODN setMethod("calcDODN", signature(x = "Satellite"), function(x, bcde) { calcDODN(getSatDataLayer(x, bcde)) }) # Function using Satellite object ---------------------------------------------- #' @rdname calcDODN setMethod("calcDODN", signature(x = "RasterLayer"), function(x) { ## stop if a multi-layer raster is supplied if (!inherits(x, "RasterLayer")) stop("Please supply a single-layer object.") vals <- raster::getValues(x) #use only values > 0 (if using whole satellite images e.g. landsat no data values #may be zero or negative, which will lead to a value of zero in calcDODN function) vals <- vals[vals > 0] freq <- plyr::count(vals) q01 <- quantile(vals, probs = 0.01, na.rm = TRUE) freq_q01 <- freq[freq$x <= q01, ] return(freq_q01$x[which.max(diff(freq_q01$freq))]) }) satellite/R/convRef2RadLinear.R0000644000176200001440000000225613572463733016054 0ustar liggesusers#' Convert reflectance to radiance using linear function coefficients #' #' @description #' The function converts the reflectance (ref) back to radiance (rad) given that #' linear conversion coefficients for both radiance and reflectance are #' available. #' #' @param band raster::RasterStack or raster::RasterLayer containing reflectance. #' @param refm Multiplication coefficient for reflectance conversion. #' @param refa Addtition coefficient for reflectance conversion. #' @param radm Multiplication coefficient for radiance conversion. #' @param rada Addition coefficient for radiance conversion. #' @param szen Sun zenith angle. #' #' @return \code{raster::Raster*} object with converted values. #' #' @export convRef2RadLinear #' #' @details The conversion functions are taken from USGS' Landsat 8 Data Users #' Handbook which is available online at #' \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. #' convRef2RadLinear <- function(band, refm, refa, radm, rada, szen){ if(!missing(szen)){ band <- band * cos(szen * pi / 180.0) } dn <- (band - refa) / refm result <- radm * dn + rada return(result) }satellite/R/RcppExports.R0000644000176200001440000000067713573376465015112 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 insertMinReqRem <- function(anHs, anHt) { .Call(`_satellite_insertMinReqRem`, anHs, anHt) } seqC <- function(from_, to_, by_ = 1.0) { .Call(`_satellite_seqC`, from_, to_, by_) } ScatteringModel <- function(mnBandWls, dScatCoef) { .Call(`_satellite_ScatteringModel`, mnBandWls, dScatCoef) } satellite/R/convSC2Ref.R0000644000176200001440000001212313572463733014512 0ustar liggesusersif (!isGeneric("convSC2Ref") ) { setGeneric("convSC2Ref", function(x, ...) standardGeneric("convSC2Ref")) } #' Convert a band's scaled counts or radiance values to reflectance #' #' @description #' Convert a band's scaled counts to reflectance using a simple linear #' conversion without any kind of atmospheric correction etc. #' #' @param x An object of class Satellite, raster::RasterStack or #' raster::RasterLayer providing scaled counts (DNs). #' @param add Additive coefficient for value transformation (i.e. offset). #' @param mult Multiplicative coefficient for value transformation (i.e. slope). #' @param szen Cosine of solar zenith angle. #' @param szen_correction Logical; if \code{TRUE}, sun zenith correction is #' being applied. #' @param subset Logical; if \code{TRUE}, all layers but the cropped #' ones are being dropped; if \code{FALSE} (default), cropped layers are appended to the #' Satellite object. #' #' @export convSC2Ref #' #' @name convSC2Ref #' #' @details #' The conversion functions are taken from USGS' Landsat 8 Data Users Handbook #' which is available online at #' \url{https://www.usgs.gov/land-resources/nli/landsat/landsat-8-data-users-handbook}. #' #' If the sensor does not provide linear conversion coefficients for reflectance #' computation, the reflectance is calculated using the solar irradiance #' following the functions taken from USGS' Landsat 7 manual, chapter 11.3.2, #' which is available online at #' \url{https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf}. #' #' @seealso \code{\link{calcAtmosCorr}} for conversions of scaled counts #' to physical units including a scene-based atmospheric correction. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- convSC2Ref(sat) #' #' # If you use a raster layer, supply required meta information #' bcde <- "B002n" #' convSC2Ref(x = getSatDataLayer(sat, bcde), #' mult = getSatRADM(sat, bcde), #' add = getSatRADA(sat, bcde)) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added converted #' layers; \cr #' if x is a \code{raster::Raster*} object, a \code{raster::Raster*} object with #' converted layer(s). #' @rdname convSC2Ref setMethod("convSC2Ref", signature(x = "Satellite"), function(x, szen_correction = "TRUE", subset = FALSE){ band_codes <- getSatBCDESolarCalib(x, calib = "SC") for(bcde in band_codes){ if(!is.na(getSatREFM(x, bcde))){ if(szen_correction == TRUE){ szen <- getSatSZEN(x, bcde) sensor_ref <- convSC2Ref(x = getSatDataLayer(x, bcde), mult = getSatREFM(x, bcde), add = getSatREFA(x, bcde), szen = szen) calib = "REF" } else { sensor_ref <- convSC2Ref(x = getSatDataLayer(x, bcde), mult = getSatREFM(x, bcde), add = getSatREFA(x, bcde)) calib = "REF_NoSZEN" } layer_bcde <- paste0(bcde, "_", calib) meta_param <- getSatMetaBCDETemplate(x, bcde) meta_param$BCDE <- layer_bcde meta_param$CALIB <- calib info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = sensor_ref, meta_param = meta_param, info = info, in_bcde = bcde) } } if(subset == TRUE){ x <- subset(x, cid = "REF") #reset LNBR (dirty hack) x@meta$LNBR <- rep(1:nrow(x@meta)) } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @rdname convSC2Ref setMethod("convSC2Ref", signature(x = "RasterStack"), function(x, mult, add, szen){ for(l in seq(nlayers(x))){ x[[l]] <- convSC2Ref(x[[l]], mult, add, szen) } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @rdname convSC2Ref setMethod("convSC2Ref", signature(x = "RasterLayer"), function(x, mult, add, szen){ if(!missing(szen)){ x <- (mult * x + add) / cos(szen * pi / 180.0) } else { x <- mult * x + add } return(x) })satellite/R/crop.R0000644000176200001440000000562313572463522013546 0ustar liggesusersif ( !isGeneric("crop") ) { setGeneric("crop", function(x, ...) standardGeneric("crop")) } #' Crop Satellite object #' #' @description #' The function is a wrapper around the \code{\link{crop}} function to #' easily crop a Satellite object by an \code{\link{extent}} object. #' #' @param x Satellite object. #' @param y \code{\link{extent}} object. #' @param subset Logical; if \code{TRUE} (default), all layers but the cropped #' ones are being dropped; if \code{FALSE}, cropped layers are appended to the #' Satellite object. #' @param snap Direction towards which to align the extent as \code{character}. #' Available options are \code{"near"} (default), \code{"in"} and \code{"out"} #' (see \code{\link[raster]{alignExtent}}). #' #' @return A Satellite object consisting of cropped layers only. If #' \code{subset = FALSE}, a Satellite object with the cropped layers appended. #' #' @export crop #' #' @name crop #' @aliases crop,Satellite-method #' #' @details Crop layers of a Satellite object to the size of a given #' \code{raster::extent} object. #' #' @references Please refer to the respective functions for references. #' #' @seealso This function is a wrapper for \code{raster::crop}. #' #' @examples #' \dontrun{ #' ## sample data #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' ## geographic extent of georg-gassmann-stadium (utm 32-n) #' ext_ggs <- raster::extent(484015, 484143, 5627835, 5628020) #' #' ## crop satellite object by specified extent #' sat_ggs <- crop(sat, ext_ggs) #' #' plot(sat) #' plot(sat_ggs) #' } setMethod("crop", signature(x = "Satellite"), function(x, y, subset = TRUE, snap = "near") { rad_bands <- getSatBCDE(x) for (bcde_rad in rad_bands) { ref <- raster::crop(getSatDataLayer(x, bcde_rad), y, snap = snap[1]) # keep all metadata except for file path since cropped # layers are in memory and set calib column flag. meta_param <- getSatMeta(x, bcde_rad) meta_param$CALIB <- "cropped" meta_param$FILE <- NULL info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = bcde_rad, data = ref, meta_param = meta_param, info = info, in_bcde = bcde_rad) } if(subset == TRUE){ x <- subset(x, cid = "cropped") #reset LNBR (dirty hack) x@meta$LNBR <- rep(1:nrow(x@meta)) x@meta$CALIB <- "SC" } return(x) } ) satellite/R/plot.R0000644000176200001440000000571613572463522013564 0ustar liggesusersif ( !isGeneric('plot') ) { setGeneric('plot', function(x, ...) standardGeneric('plot')) } #' Plot a Satellite object #' #' @description #' This is the standard plotting routine for the 'Satellite' class. Layers are #' drawn either from the start (default; limited to a maximum of 16 sub-plots) #' or according to the speficied band codes. #' #' @param x A 'Satellite' object, usually returned by \code{\link{satellite}}. #' @param bcde Band codes to be visualized, e.g. returned by #' \code{\link{getSatBCDE}}. If not supplied, the initial (up to) 16 layers are #' being visualized. #' @param col Color scheme. #' @param ... Further arguments passed on to \code{\link{plot.default}}. #' #' @seealso \code{\link{plot.default}}, \code{\link{par}}. #' #' @examples #' \dontrun{ #' ## sample data #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' ## display data without quality flag layer #' bds <- getSatBCDE(sat)[1:11] #' plot(sat, bcde = bds) #' } #' #' @export #' @name plot NULL # set methods ------------------------------------------------------------- #' @aliases plot,Satellite-method #' @rdname plot setMethod('plot', signature(x = 'Satellite'), function(x, bcde = NULL, col = grDevices::grey.colors(100), ...) { ## if 'bcde' is not supplied, visualize the initial (up to 16) ## layers, else visualize the specified bands ls_lyr <- getSatDataLayers(x, bcde = bcde) ch_bcde <- sapply(ls_lyr, names) # par settings int_lyr_len <- length(ls_lyr) int_nrow <- int_ncol <- 1L if (int_lyr_len == 2) { int_ncol <- 2L } else if (int_lyr_len > 2 & int_lyr_len <= 4) { int_nrow <- int_ncol <- 2L } else if (int_lyr_len > 4 & int_lyr_len <= 6) { int_nrow <- 2L; int_ncol <- 3L } else if (int_lyr_len > 6 & int_lyr_len <= 9) { int_nrow <- int_ncol <- 3L } else if (int_lyr_len > 9 & int_lyr_len <= 12) { int_nrow <- 3L; int_ncol <- 4L } else if (int_lyr_len > 12) { int_nrow <- int_ncol <- 4L if (int_lyr_len > 16) { ls_lyr <- ls_lyr[1:16] int_lyr_len <- length(ls_lyr) } } # visualize graphics::par(mfrow = c(int_nrow, int_ncol)) for (i in 1:int_lyr_len) { stats4::plot(ls_lyr[[i]], col = col, ...) graphics::title(ch_bcde[i], line = .2) } ## reset par settings graphics::par(mfrow = c(1, 1)) return(invisible()) }) satellite/R/brick.R0000644000176200001440000000307513572463522013674 0ustar liggesusersif ( !isGeneric('brick') ) { setGeneric('brick', function(x, ...) standardGeneric('brick')) } #' Convert selected layers of a Satellite object to a RasterBrick #' #' @description #' Convert selected layers of a Satellite object to a RasterBrick #' #' @param x an object of class 'Satellite' #' @param layer character vector (bcde codes) or integer vector (index) of #' the layers to be stacked #' @param ... additional arguments passed on to \code{\link{brick}} #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' brck <- brick(sat, c("B001n", "B002n", "B003n")) #' brck #' #' @export #' @name brick #' @docType methods #' @rdname brick #' @aliases brick,Satellite-method setMethod('brick', signature(x = 'Satellite'), function(x, layer = names(x), ...) { if (is.numeric(layer)) layer <- names(x)[layer] lyrs <- getSatDataLayers(x, bcde = layer) xres <- getSatXRes(x, bcde = layer) if (length(unique(xres)) > 1) { skip <- which(xres == unique(xres)[2]) warning("\nlayer ", names(skip), " has different resolution", "\nnot stacking this layer") } ind <- duplicated(xres) ind[1] <- TRUE brck <- raster::brick(lyrs[ind], ...) return(brck) } ) satellite/R/calcTOAIrradRadRef.R0000644000176200001440000001131013572463522016105 0ustar liggesusersif ( !isGeneric("calcTOAIrradRadRef") ) { setGeneric("calcTOAIrradRadRef", function(x, ...) standardGeneric("calcTOAIrradRadRef")) } #' Compute top of atmosphere solar irradiance using radiation vs. reflection #' #' @description #' Compute extraterrestrial solar irradiance (ESun) using the actual #' maximum radiation and reflection values within each band. #' #' @param x A Satellite object or the maximum radiance of satellite #' band(s) as numeric object. #' @param ref_max Maximum reflextance of satellite band(s). #' @param normalize Logical; if \code{TRUE}, ESun is normalized to mean #' earth-sun distance. #' @param esd Earth-sun distance (AU, can be estimated using #' \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not #' supplied and necessary for normalization, it is tried to take it from the #' metadata, otherwise it is estimated by the day of the year using #' \code{\link{calcEarthSunDist}}. #' #' @export calcTOAIrradRadRef #' #' @name calcTOAIrradRadRef #' #' @details The actual solar irradiance is computed using the following formula #' taken from the GRASS GIS #' \href{https://grass.osgeo.org/grass64/manuals/i.landsat.toar.html}{i.landsat.toar} module #' \deqn{ESun = (pi d^2) RADIANCE_MAXIMUM / REFLECTANCE_MAXIMUM} #' where d is the earth-sun distance (in AU) and RADIANCE_MAXIMUM #' and REFLECTANCE_MAXIMUM are the maximum radiance and reflection values of the #' respective band. All these parameters are taken from the scene's metadata #' file if a Satellite object is passed to the function. #' #' By default, the resulting actual ESun will be normalized to a mean earth-sun #' distance to be compatible with other default results from #' \code{\link{calcTOAIrradTable}} or \code{\link{calcTOAIrradModel}}. #' #' @seealso \code{\link{calcTOAIrradTable}} for tabulated solar irradiance #' values from the literature or \code{\link{calcTOAIrradModel}} for the #' computation of the solar irradiance based on look-up tables for the sensor's #' relative spectral response and solar irradiation spectral data. #' #' See \code{\link{calcEarthSunDist}} for calculating the earth-sun #' distance based on the day of the year which is called by this function if #' ESun should be corrected for actual earth-sun distance. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- calcTOAIrradModel(sat) #' getSatESUN(sat) #' #' calcTOAIrradRadRef(x = getSatRadMax(sat, getSatBCDESolar(sat)), #' ref_max = getSatRefMax(sat, getSatBCDESolar(sat)), #' normalize = FALSE, #' esd = calcEarthSunDist("2015-01-01")) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with ESun information #' added to the metadata; if x is numeric, a vector containing ESun for the #' respective band(s). #' @rdname calcTOAIrradRadRef setMethod("calcTOAIrradRadRef", signature(x = "Satellite"), function(x, normalize = TRUE, esd){ if(normalize == TRUE & missing(esd)){ esd = getSatESD(x) if(is.na(esd)){ esd = calcEarthSunDist(getSatDATE(x)[1]) } } if(normalize == TRUE){ esun <- calcTOAIrradRadRef( x = getSatRadMax(x, getSatBCDESolar(x)), ref_max = getSatRefMax(x, getSatBCDESolar(x)), esd = esd, normalize = normalize) } else { esun <- calcTOAIrradRadRef( x = getSatRadMax(x, getSatBCDESolar(x)), ref_max = getSatRefMax(x, getSatBCDESolar(x)), normalize = normalize) } bcde = getSatBCDESolar(x) x <- addSatMetaParam(x, meta_param = data.frame( BCDE = bcde, ESUN = as.numeric(esun))) return(x) }) # Function using numeric ------------------------------------------------------- #' @rdname calcTOAIrradRadRef setMethod("calcTOAIrradRadRef", signature(x = "numeric"), function(x, ref_max, normalize = TRUE, esd){ eSun <- pi * x / ref_max if(normalize == TRUE){ if(missing(esd)){ stop("Variable esd is missing.") } eSun <- eSun * esd**2 } return(eSun) })satellite/R/calcAtmosCorr.R0000644000176200001440000002411113572465025015330 0ustar liggesusersif ( !isGeneric("calcAtmosCorr") ) { setGeneric("calcAtmosCorr", function(x, ...) standardGeneric("calcAtmosCorr")) } #' Atmospheric correction of remote sensing data #' #' @description #' The function computes an atmospheric scattering correction and converts #' the sensors digital numbers to reflectances using #' \itemize{ #' \item absolute radiance correction #' \item DOS2: a dark object substraction model by Chavez (1996) #' \item DOS4: a dark object substratcion model by Moran et al. (1992) #' } #' #' @param x Satellite or Raster* object providing the radiance at the sensor. #' @param model Model to be used to correct for 1\% scattering (DOS2, DOS4). #' @param esun_method If x is a Satellite object, name of the method to be used #' to compute \code{esun} using one of \code{\link{calcTOAIrradRadRef}} #' ("RadRef"), \code{\link{calcTOAIrradTable}} ("Table") or #' \code{\link{calcTOAIrradModel}} ("Model"). #' @param path_rad Path radiance, e.g. returned from #' \code{\link{calcPathRadDOS}}. #' @param esun Actual (i.e. non-normalized) TOA solar irradiance, e.g. returned #' from \code{\link{calcTOAIrradRadRef}}, \code{\link{calcTOAIrradTable}} #' or \code{\link{calcTOAIrradModel}}. #' @param szen Sun zenith angle. #' #' @export calcAtmosCorr #' #' @name calcAtmosCorr #' #' @details #' If a Satellite object is passed to the function, and if the required #' pre-processing has not been performed already, the path radiance is computed #' based on a dark object's scaled count value using #' \code{\link{calcPathRadDOS}} which will also take care of the TOA solar #' irradiance by calling \code{\link{calcTOAIrradModel}}, #' \code{\link{calcTOAIrradRadRef}} or \code{\link{calcTOAIrradTable}} #' (depending on \code{esun_method}) if necessary. The bands' scaled counts are #' converted to radiance using \code{\link{convSC2Rad}}. #' #' The radiometric correction is based on a dark object approach using #' either the DOS2 (Chavez 1996) or DOS4 (Moran et al. 1992) model. #' #' The minimum reflectance values for the dark object are identified using the #' approximation of Chavez (1988, see \code{\link{calcPathRadDOS}} for details). #' #' The estimated values of the solar irradiance required for the path radiance #' can be computed by one of \code{\link{calcTOAIrradTable}} which is used to #' get readily published values of ESun, \code{\link{calcTOAIrradRadRef}} which #' computes ESun based on the actual radiance and reflectance in the scene, or #' \code{\link{calcTOAIrradModel}} which computes ESun based on look-up tables #' for the sensor's relative spectral response and solar irradiation spectral data. #' #' The atmospheric transmittance towards the sensor (Tv) is approximated by #' 1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992). #' #' The atmospheric transmittance from the sun (Tz) is approximated by the #' cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh #' scattering (DOS4, Moran et al. 1992). #' #' The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996) #' or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992). #' #' Equations are taken from Song et al. (2001). #' #' @references Chavez Jr PS (1988) An improved dark-object subtraction technique #' for atmospheric scattering correction of multispectral data. Remote Sensing #' of Environment 24/3, doi:10.1016/0034-4257(88)90019-3, available online at #' \url{http://www.sciencedirect.com/science/article/pii/0034425788900193} #' #' Chavez Jr PS (1996) Image-based atmospheric corrections revisited and #' improved. Photogrammetric Engineering and Remote Sensing 62/9, #' available online at #' \url{https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved} #' #' Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat #' Package. Journal of Statistical Software,43/4, 1-25. URL #' \url{http://www.jstatsoft.org/v43/i04/}. #' #' Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evaluation of simplified #' procedures for retrieval of land surface reflectance factors from satellite #' sensor output.Remote Sensing of Environment 41/2-3, 169-184, #' doi:10.1016/0034-4257(92)90076-V, #' URL \url{http://www.sciencedirect.com/science/article/pii/003442579290076V}. #' #' Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification #' and Change Detection Using Landsat TM Data: When and How to Correct #' Atmospheric Effects? Remote Sensing of Environment 75/2, #' doi:10.1016/S0034-4257(00)00169-3, URL #' \url{http://www.sciencedirect.com/science/article/pii/S0034425700001693} #' #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat_atmos <- calcAtmosCorr(sat, model = "DOS2", esun_method = "RadRef") #' #' bcde <- "B002n" #' #' sat <- calcTOAIrradRadRef(sat, normalize = FALSE) #' #' path_rad <- calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bcde))), #' bnbr = getSatLNBR(sat, bcde), #' band_wls = #' data.frame(LMIN = #' getSatLMIN(sat, #' getSatBCDESolar(sat)), #' LMAX = #' getSatLMAX(sat, #' getSatBCDESolar(sat))), #' radm = getSatRADM(sat, getSatBCDESolar(sat)), #' rada = getSatRADA(sat, getSatBCDESolar(sat)), #' szen = getSatSZEN(sat, getSatBCDESolar(sat)), #' esun = getSatESUN(sat, getSatBCDESolar(sat)), #' model = "DOS2") #' #' sensor_rad <- convSC2Rad(x = getSatDataLayer(sat, bcde), #' mult = getSatRADM(sat, bcde), #' add = getSatRADA(sat, bcde), getSatSZEN(sat, bcde)) #' #' ref_atmos <- calcAtmosCorr(x = sensor_rad, #' path_rad = path_rad[names(path_rad) == bcde], #' esun = getSatESUN(sat, bcde), #' szen = getSatSZEN(sat, bcde), #' model = "DOS2") #' NULL # Function using satellite object ---------------------------------------------- #' @return Satellite object with added atmospheric corrected layers #' @rdname calcAtmosCorr setMethod("calcAtmosCorr", signature(x = "Satellite"), function(x, model = c("DOS2", "DOS4"), esun_method = "RadRef"){ # If not supplied, 'model' defaults to DOS2 model <- model[1] # Get solar bands with calibration information equals scaled counts sc_bands <- getSatBCDESolarCalib(x, calib = "SC") # Path radiance computation if necessary (TOA irradiance will be # computed, too, if necessary) if(any(is.na(getSatPRAD(x, sc_bands)))){ x <- calcPathRadDOS(x, model = model, esun_method = esun_method) } # Radiance conversion if necessary if(any(is.na(getSatBCDESolarCalib(x, calib = "RAD")))){ x <- convSC2Rad(x, szen_correction = "TRUE") } # Compute atmospheric correction (reflectance) rad_bands <- getSatBCDESolarCalib(x, calib = "RAD") for(bcde_rad in rad_bands){ ref <-calcAtmosCorr(getSatDataLayer(x, bcde_rad), path_rad = getSatPRAD(x, bcde_rad), esun = getSatESUN(x, bcde_rad), szen = getSatSZEN(x, bcde_rad), model = "DOS2") layer_bcde <- paste0(substr(bcde_rad, 1, nchar(bcde_rad) - 4), "_REF_AtmosCorr") meta_param <- data.frame(getSatSensorInfo(x), getSatBandInfo(x, bcde_rad, return_calib = FALSE), CALIB = "REF_AtmosCorr") info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = ref, meta_param = meta_param, info = info, in_bcde = bcde_rad) } return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @return raster::RasterStack object with atmospheric corrected layers #' @rdname calcAtmosCorr setMethod("calcAtmosCorr", signature(x = "RasterStack"), function(x, path_rad, esun, szen, model = c("DOS2", "DOS4")){ # If not supplied, 'model' defaults to DOS2 model <- model[1] for(l in seq(nlayers(x))){ x[[l]] <- calcAtmosCorr(x, path_rad, esun, szen, model = "DOS2") } return(x) }) # Function using raster::RasterLayer object ------------------------------------ #' @return raster::RasterLayer object with atmospheric corrected layer #' @rdname calcAtmosCorr setMethod("calcAtmosCorr", signature(x = "RasterLayer"), function(x, path_rad, esun, szen, model = c("DOS2", "DOS4")){ # If not supplied, 'model' defaults to DOS2 model <- model[1] cos_szen <- cos(szen * pi / 180.0) if(model == "DOS2"){ tv <- 1.0 tz <- cos_szen edown <- 0.0 } else if(model == "DOS4"){ tv <- NA tz <- NA edown <- NA } ref <- pi * (x - path_rad) / ( tv * (esun * cos_szen * tz + edown) ) return(ref) }) satellite/R/maskInvarFeatures.R0000644000176200001440000001264613572463522016240 0ustar liggesusersif ( !isGeneric("maskInvarFeatures") ) { setGeneric("maskInvarFeatures", function(x, ...) standardGeneric("maskInvarFeatures")) } #' Identify pseudo-invariant features from a satellite scene #' #' @description #' Identify pseudo-invariant features from a satellite scene based on a #' vis, near infravis and short-wave infravis band. #' #' @param x A Satellite object or a \code{raster::RasterLayer} providing the #' sensor's vis band. #' @param nir A \code{raster::RasterLayer} containing the sensor's nir band. #' @param swir A \code{raster::RasterLayer} containing the sensor's swir band. #' @param quant A value v = [0...1] which is used to define the percentage #' threshold values (thv) for invariant features (nir/vis ratio < thv, #' swir band values > 1-thv). #' #' @export maskInvarFeatures #' #' @name maskInvarFeatures #' #' @details Invariant features are identified as pixels which belong to the #' group of (i) the n lowest VIS/NIR ratios and of (ii) the highest n #' SWIR values. The value of n is given by the parameter quant = [0...1]. #' #' @references This function is taken and only slightly modified from the PIF #' function by Sarah C. Goslee (2011). Analyzing Remote Sensing Data in R: The #' landsat Package. Journal of Statistical Software,43(4), 1-25. URL #' \url{http://www.jstatsoft.org/v43/i04/}. #' #' The underlying theory has been published by Schott RJ, Salvaggio C and #' Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant #' features. Remote Sensing of Environment 26/1, #' doi:10.1016/0034-4257(88)90116-2, available online at #' \url{http://www.sciencedirect.com/science/article/pii/0034425788901162}. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- maskInvarFeatures(sat) #' #' maskInvarFeatures(x = getSatDataLayer(sat, "B004n"), #' nir = getSatDataLayer(sat, "B005n"), #' swir = getSatDataLayer(sat, "B007n")) #' #' ## when dealing with a 'RasterStack' #' rst <- stack(files[c(6, 7, 9)]) #' maskInvarFeatures(rst) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with added layer; \cr #' if x is a \code{raster::RasterLayer} object, a a \code{raster::RasterLayer} #' object with added layers (1 indicates invariant pixels, 0 otherwise). #' @rdname maskInvarFeatures setMethod("maskInvarFeatures", signature(x = "Satellite"), function(x){ bcde_vis <- "B004n" bcde_nir <- "B005n" bcde_swir <- "B007n" mask <- maskInvarFeatures(x = getSatDataLayer(x, bcde_vis), nir = getSatDataLayer(x, bcde_nir), swir = getSatDataLayer(x, bcde_swir)) layer_bcde <- "M0000_InvarFeatures" meta_param <- getSatSensorInfo(x) meta_param$BCDE <- layer_bcde meta_param$CALIB <- "BINARY" info <- sys.calls()[[1]] info <- paste0("Add layer from ", info[1], "(", toString(info[2:length(info)]), ")") x <- addSatDataLayer(x, bcde = layer_bcde, data = mask, meta_param = meta_param, info = info, in_bcde = paste(bcde_vis, bcde_nir, bcde_swir, sep = ", ")) return(x) }) # Function using raster::RasterStack object ------------------------------------ #' @param id_vis Index of the visible band. #' @param id_nir Index of the near infravis band. #' @param id_swir Index of the short-wave infravis band. #' @rdname maskInvarFeatures setMethod("maskInvarFeatures", signature(x = "RasterStack"), function(x, quant = 0.01, id_vis = 1L, id_nir = 2L, id_swir = 3L) { ## split 'RasterStack' into single layers vis <- x[[id_vis]] nir <- x[[id_nir]] swir <- x[[id_swir]] ratio_nir_vis <- nir/vis ratio_nir_vis_quant <- quantile(ratio_nir_vis, probs = quant, na.rm = TRUE) swir_quant <- quantile(swir, probs = 1-quant, na.rm = TRUE) invar_feats <- ratio_nir_vis < ratio_nir_vis_quant & swir > swir_quant invar_feats[invar_feats == 0] <- NA return(invar_feats) }) # Function using raster::RasterLayer object ------------------------------------ #' @rdname maskInvarFeatures setMethod("maskInvarFeatures", signature(x = "RasterLayer"), function(x, nir, swir, quant=0.01) { ratio_nir_vis <- nir/x ratio_nir_vis_quant <- quantile(ratio_nir_vis, probs = quant, na.rm=TRUE) swir_quant <- quantile(swir, probs = 1-quant, na.rm=TRUE) invar_feats <- ratio_nir_vis < ratio_nir_vis_quant & swir > swir_quant invar_feats[invar_feats == 0] <- NA return(invar_feats) }) satellite/R/calcTOAIrradModel.R0000644000176200001440000001444413572467017016020 0ustar liggesusersif ( !isGeneric("calcTOAIrradModel") ) { setGeneric("calcTOAIrradModel", function(x, ...) standardGeneric("calcTOAIrradModel")) } #' Compute top of atmosphere solar irradiance for sensor bands using LUTs #' #' @description #' Compute mean extraterrestrial solar irradiance (ESun) using tabulated mean #' solar spectral data and the band specific relative spectral response (rsr) #' functions. #' #' @param x A Satellite object or the relative spectral response function for #' the respective band as \code{data.frame} (see details for structure). #' @param model Tabulated solar radiation model to be used (one of #' MCebKur_MChKur, MNewKur, MthKur, MoldKur, MODWherli_WMO, NN, see reference #' on tabulated solar irradiance below). #' @param normalize Logical; if \code{TRUE}, ESun is normalized to mean #' earth-sun distance. #' @param esd Earth-sun distance (AU, can be estimated using #' \code{\link{calcEarthSunDist}}). If x is a Satellite object and esd is not #' supplied and necessary for normalization, it is tried to take it from the #' metadata, otherwise it is estimated by the day of the year using #' \code{\link{calcEarthSunDist}}. #' #' @export calcTOAIrradModel #' #' @name calcTOAIrradModel #' #' @details Computation of ESun is taken from Updike and Comp (2011). #' #' Tabulated values for mean earth-sun distance are taken from the #' data sources mentioned in the references. #' #' If results should not be normalized to a mean earth-sun distance, the #' actual earth-sun distance is approximated by the day of the year using #' \code{\link{calcEarthSunDist}}. #' #' Relative spectral response values have to be supplied as a \code{data.frame} #' which has at least the following three columns: (i) a column "Band" for the #' sensor band number (i.e. 1, 2, etc.), (ii) a column "WAVELENGTH" for the #' WAVELENGTH data in full nm steps, and (iii) a column "RSR" for the response #' information [0...1]. #' #' @references #' Updike T, Comp C (2011) Radiometric use of WorldView-2 imagery. #' Technical Note, available online at \url{http://www.pancroma.com/downloads/Radiometric_Use_of_WorldView-2_Imagery.pdf}. #' #' Tabulated relative spectral response functions (nm-1) are taken from the #' \href{https://landsat.usgs.gov/spectral-characteristics-viewer}{spectral viewer} #' of the USGS Landsat FAQ. #' #' Tabulated solar irradiance (W m-2 nm-1) is taken from the #' \href{http://rredc.nrel.gov/solar/spectra/am0/modtran.html}{National Renewable #' Energy Laboratory}. #' #' @seealso \code{\link{calcTOAIrradTable}} for tabulated solar irradiance #' values from the literature or \code{\link{calcTOAIrradRadRef}} for the #' computation of the solar irradiance based on maximum radiation and reflection #' values of the dataset. #' #' See \code{\link{calcEarthSunDist}} for calculating the earth-sun #' distance based on the day of the year which is called by this function if #' ESun should be corrected for actual earth-sun distance. #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' sat <- calcTOAIrradModel(sat) #' getSatESUN(sat) #' #' lut <- lutInfo() #' calcTOAIrradModel(lut$L8_RSR, model = "MNewKur", normalize = FALSE, #' esd = calcEarthSunDist("2015-01-01")) #' NULL # Function using satellite object ---------------------------------------------- #' @return If x is a Satellite object, a Satellite object with ESun information #' added to the metadata; if x is a \code{data.frame}, a vector containing ESun #' for the respective band(s). #' @rdname calcTOAIrradModel setMethod("calcTOAIrradModel", signature(x = "Satellite"), function(x, model = "MNewKur", normalize = TRUE, esd){ if(normalize == FALSE & missing(esd)){ esd <- getSatESD(x) if(is.na(esd)){ doy <- getSatDATE(x) esd <- calcEarthSunDist(doy) } } rsr <- lutInfoRSRromSID(sid = getSatSID(x)) if(normalize == TRUE){ esun <- calcTOAIrradModel(x = rsr, model = model, normalize = normalize) } else { esun <- calcTOAIrradModel(x = rsr, model = model, normalize = normalize, esd = esd) } bcde <- names(esun) x <- addSatMetaParam(x, meta_param = data.frame( BCDE = bcde, ESUN = as.numeric(esun))) return(x) }) # Function using data frame ---------------------------------------------------- #' @rdname calcTOAIrradModel setMethod("calcTOAIrradModel", signature(x = "data.frame"), function(x, model = "MNewKur", normalize = TRUE, esd){ toa <- lut$SOLAR toa$WAVELENGTH <- round(toa$WAVELENGTH, 0) toa_aggregated <- aggregate(toa, by = list(toa$WAVELENGTH), FUN = "mean") rsr <- x[, c(grep("BCDE", colnames(x)), grep("WAVELENGTH", colnames(x)), grep("RSR", colnames(x)))] eSun <- lapply(unique(rsr$BCDE), function(y){ rsr_solar <- merge(rsr[rsr$BCDE == y,], toa_aggregated, by = "WAVELENGTH") if(nrow(rsr_solar) > 0){ act_eSun <- aggregate( rsr_solar$RSR * rsr_solar[,grep(model, names(rsr_solar))], by = list(rsr_solar$BCDE), FUN = "sum")[2] / aggregate(rsr_solar$RSR, by = list(rsr_solar$BCDE), FUN = "sum")[2] * 1000 act_eSun <- unlist(act_eSun) } else { act_eSun <- c(NA) } names(act_eSun) <- paste0(y) return(act_eSun) }) eSun <- unlist(eSun) if(normalize == FALSE){ if(missing(esd)){ stop("Variable esd is missing.") } eSun <- eSun / esd**2 } return(eSun) })satellite/R/stack.R0000644000176200001440000000321713572463522013705 0ustar liggesusersif ( !isGeneric('stack') ) { setGeneric('stack', function(x, ...) standardGeneric('stack')) } #' Convert selected layers of a Satellite object to a RasterStack #' #' @description #' Convert selected layers of a Satellite object to a RasterStack #' #' @param x an object of class 'Satellite' #' @param layer character vector (bcde codes) or integer vector (index) of #' the layers to be stacked #' @param ... additional arguments passed on to \code{\link{stack}} #' #' @examples #' path <- system.file("extdata", package = "satellite") #' files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) #' sat <- satellite(files) #' #' stck <- stack(sat, c("B001n", "B002n", "B003n")) #' stck #' #' @export #' @name stack #' @docType methods #' @rdname stack #' @aliases stack,Satellite-method #' ### RasterStack ----------------------------------------------------------- setMethod('stack', signature(x = 'Satellite'), function(x, layer = names(x), ...) { if (is.numeric(layer)) layer <- names(x)[layer] lyrs <- getSatDataLayers(x, bcde = layer) xres <- getSatXRes(x, bcde = layer) if (length(unique(xres)) > 1) { skip <- which(xres == unique(xres)[2]) warning("\nlayer ", names(skip), " has different resolution", "\nnot stacking this layer") } ind <- duplicated(xres) ind[1] <- TRUE stck <- raster::stack(lyrs[ind], ...) return(stck) } ) satellite/NEWS.md0000644000176200001440000000444113572464432013353 0ustar liggesusers## satellite 1.0.2 Changes: * Replaced all class() calls with inherits(). Starting with R version 4.0.0, "matrix" objects also inherit from class "array", which invalidates code assuming that length(class(obj)) == 1. * Fixed broken links. * Switched to MIT license. * Roxygen2 update. ## satellite 1.0.1 Bugfixes: * calcAtmosCorr() was not compatible with NA values, ie quantile() failed. * compFilePathLandsat() failed when specifying metadata files with new naming standard as input. * compMetaLandsat() assigned Landsat 7 metadata information, eg parameters required for radiometric rescaling, to wrong bands. ## satellite 1.0.0 New features: * Inverse squared relative earth-sun distance from calcEarthSunDist(). * calcTopoCorr,Raster*() methods accept additional arguments (via '...') passed to writeRaster(). * crop,Satellite-method takes a new argument 'snap' similar to raster::crop(). * extend,Satellite-method takes a new argument 'value' which is assigned to new cells similar to raster::extend(). * satellite is compatible with pre-collection Level-1 and Collection 1 Level-1 file naming. * sortFilesLandsat() to automatically sort Landsat band files in ascending order. Bugfixes: * plot,Satellite-method ignored band codes to be visualized if 'bcde = ' assignment was not explicitly included. * Solved 'Error in .local(.Object, ...) :' when trying to retrieve values from built-in data sets l7, l8. Changes: * calcHistMatch() and panSharp() moved to satelliteTools (https://github.com/environmentalinformatics-marburg/satelliteTools). * Mandatory use of Rcpp functionality in calcPathRadDOS(). * Added helper functions in R/pck_data.R and R/pck_lut.R to .Rbuildignore. * Revised sensor IDs for Landsat 4 (LT4 for 'Landsat Thematic Mapper') and 5 (LT5). * Updated ESun values (https://landsat.usgs.gov/esun) for Landsat 4, 5 and 7. * Updated spectral range of Landsat 7 band 7 (https://landsat.usgs.gov/landsat-7-history). * Moved Rcpp from Depends to Imports. * Built-in data sets l7, l8 now originate from Landsat Collection 1 Level-1 instead of previous Pre-Collection 1 Level-1. * Removed deprecated convDN2RU(), users must switch to convSC2Rad(), convSC2Ref() and convRad2BT() instead. ## satellite 0.2.0 * n/a ## satellite 0.1.0 * Initial release satellite/MD50000644000176200001440000002654313573407552012575 0ustar liggesusersb1e32d22868fbe9ceaaedf4bbca87834 *DESCRIPTION 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liggesuserssatellite/inst/doc/satellite.R0000644000176200001440000000262113573376520016107 0ustar liggesusers## ----------------------------------------------------------------------------- library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) # Landsat 8 example data files sat <- satellite(files) ## ----------------------------------------------------------------------------- str(sat@layers, 1) ## ----------------------------------------------------------------------------- ## default (all that are similar to layer 1; panchromatic 15-m band 8 is skipped here) sat_stack <- stack(sat) sat_stack ## or by layer names stack(sat, layer = c("B001n", "B002n", "B003n")) ## or by layer indices stack(sat, layer = 2:6) ## ----------------------------------------------------------------------------- str(sat@meta) ## ----------------------------------------------------------------------------- ## add digital elevation model to existing 'Satellite' object dem <- raster(system.file("extdata/DEM.TIF", package = "satellite")) sat <- addSatDataLayer(sat, data = dem, info = NULL, bcde = "DEM", in_bcde = "DEM") ## perform topographic correction sat_tc <- calcTopoCorr(sat) tail(sat_tc@meta[, 1:6]) ## ----------------------------------------------------------------------------- sat@log ## ----------------------------------------------------------------------------- str(sat_tc@log[1:2]) satellite/inst/doc/satellite.html0000644000176200001440000006522713573376520016665 0ustar liggesusers satellite - Classes and Methods for Satellite Data

satellite - Classes and Methods for Satellite Data

Thomas Nauss, Florian Detsch

2019-12-09

Introduction

We are happy to introduce satellite, an R package designed to facilitate satellite remote sensing analysis in a structured and user-friendly manner.

The main purpose of satellite is to provide standard classes and methods to stream-line remote sensing analysis workflow in R. It provides its own satellite-class along with standard methods for basic image transformations such as atmospheric and topgraphic corrections, among others.

The package is desinged with both flexibility and user-friendliness in mind. Think of it as the sp-package for remote sensing analysis. It provides core functionality and can be easily extended via packages to suit your own analysis needs. Furthermore, the fact that image bands are stored as Raster* objects means, that all functionality currently available for these classes will also work nicely with satellite.

In the following, we would like to highlight some of the functionality provided by satellite.

 satellite/inst/doc/satellite.Rmd0000644000176200001440000001377413572463522016441 0ustar liggesusers--- title: "satellite - Classes and Methods for Satellite Data" author: "Thomas Nauss, Florian Detsch" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{satellite - Classes and Methods for Satellite Data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ## Introduction We are happy to introduce `satellite`, an R package designed to facilitate satellite remote sensing analysis in a structured and user-friendly manner. The main purpose of `satellite` is to provide standard classes and methods to stream-line remote sensing analysis workflow in R. It provides its own `satellite-class` along with standard methods for basic image transformations such as atmospheric and topgraphic corrections, among others. The package is desinged with both flexibility and user-friendliness in mind. Think of it as the `sp-package` for remote sensing analysis. It provides core functionality and can be easily extended via packages to suit your own analysis needs. Furthermore, the fact that image bands are stored as `Raster*` objects means, that all functionality currently available for these classes will also work nicely with `satellite`. In the following, we would like to highlight some of the functionality provided by `satellite`. ## The satellite class To start a remote sensing alaysis workflow with `satellite` you simply use its workhorse function `satellite()` and point it to a folder where your satellite data is stored. ```{r} library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) # Landsat 8 example data files sat <- satellite(files) ``` This will create an object of class `satellite` with three slots: * @layers - a list of `RasterLayers` of all available bands * @meta - a data frame containing all the meta data needed to process the data layers * @log - a list containing information about the processing history of the object For supported satellite platforms all of this is done automatically. At the moment of this writing, supported platforms are Landsat generations 4 to 8. It is, however, very easy to expand this support to other platforms by providing suitable look-up-tables (LUT). Even if no suitable LUT is available, `satellite` will still import slots `@layers` and `@log`. ### The @layers slot As mentioned above, `@layers` contains a list of `RasterLayers` of all available bands. The reason for storing the individual bands as `RasterLayers` rather than a `RasterStack` is that most satellite platforms provide at least one layer of different spatial resolution that the rest. ```{r} str(sat@layers, 1) ``` It is, however, easy to create a `RasterStack` from selected layers as `stack-method` is defined for class `satellite`. By default this will take all layers with the same resolution as the first and stack them. A suitable warning is provided so that the user is informed which layers were not included in the `RasterStack`. Furthermore, one can simply provide a vector of layer IDs (either layer names or numbers) to be stacked. ```{r} ## default (all that are similar to layer 1; panchromatic 15-m band 8 is skipped here) sat_stack <- stack(sat) sat_stack ## or by layer names stack(sat, layer = c("B001n", "B002n", "B003n")) ## or by layer indices stack(sat, layer = 2:6) ``` ### The @meta slot The `@meta` slot provides meta information for each of the layers of the `satellite` object. Here's a non-exhaustive list of the most important entries: * BCDE - the band code (i.e. layer name) * DATE - the date the image was captured * SID - a short ID for the sensor (e.g. "LC8") * TYPE - type of the solar spectrum the band falls into (e.g. visible, near-infrared) * SPECTRUM - the broad spectrum classification according to shart-wave vs. long-wave radiation (i.e. solar vs thermal) * CALIB - the calibration level of the layer ("SC" means source calibration, i.e. as provided by the raw data) In addition to these, several calibration coefficients (such as the sun zenith and azimuth angles , sun elevation, earth-sun distance etc.), information on spatial resolution and projection as well as information about file names and paths is also stored. For the Landsat 8 example data shipped with the package the meta data looks like this: ```{r} str(sat@meta) ``` Everytime the user performs some calculation on some or all of the layers, this meta information will be updated accordingly. Here's an example: ```{r} ## add digital elevation model to existing 'Satellite' object dem <- raster(system.file("extdata/DEM.TIF", package = "satellite")) sat <- addSatDataLayer(sat, data = dem, info = NULL, bcde = "DEM", in_bcde = "DEM") ## perform topographic correction sat_tc <- calcTopoCorr(sat) tail(sat_tc@meta[, 1:6]) ``` As you can see, all bands have been topographically corrected and the meta data for the resulting layers is automatically appended to the original data frame. Note for example how `$DATE` is set to the date that layers were calculated. Note, that in order to avoid too long console output, we only show the first and last six columns and rows, respectively, of the meta data here. ### The @log slot Similar to the meta data, log data is also updated every time an analyis is carried out on the object. The default entries (i.e. the ones created on intial import) are as follows: ```{r} sat@log ``` And here's how this is modified after the topographic correction: ```{r} str(sat_tc@log[1:2]) ``` Note how, in addition to the info about the initial import, we now have additional logs entries for each band that was topographically corrected clearly showing which call was dispatched, when and on which layer. Even though we only show the first additional log entry here, entries are created for all processed layers. This ensures that we can easily trace what has been done so far and serves as a reference for the current state of processing. satellite/inst/doc/landsat-calibration.Rmd0000644000176200001440000000261113572463522020352 0ustar liggesusers--- title: "Landsat-8 Calibration" author: "Thomas Nauss" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Landsat-8 Calibration} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- The following example shows how to read a Landsat-8 data set downloaded from the [USGS Earth Explorer](https://earthexplorer.usgs.gov/) and how to convert the bands' scaled counts to radiance, reflectance and/or brightness temperature. ## Reading Landsat-8 data We will use the test data set supplied with this package. In order to read the data set, just supply the filename (or list of filenames) to the `satellite()` function. ```{r} library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ``` The meta data will automatically be loaded since (i) Landsat-8 is a known platform to the `satellite` package and (ii) the name of the meta data file is in accordance with the USGS conventions. ## Converting Landsat-8 scaled counts to physical units In order to convert scaled counts of a Satellite object to radiance, reflectance and/or temperature, just supply it to the `convSC2Rad`, `convSC2Ref` or `convRad2BT` function. ```{r} sat <- convSC2Rad(sat) sat <- convSC2Ref(sat) sat <- convRad2BT(sat) ``` satellite/inst/doc/landsat-calibration.R0000644000176200001440000000064213573376512020036 0ustar liggesusers## ----------------------------------------------------------------------------- library(satellite) path <- system.file("extdata", package = "satellite") files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) sat <- satellite(files) ## ----------------------------------------------------------------------------- sat <- convSC2Rad(sat) sat <- convSC2Ref(sat) sat <- convRad2BT(sat) satellite/inst/doc/landsat-calibration.html0000644000176200001440000002534213573376512020605 0ustar liggesusers Landsat-8 Calibration

Landsat-8 Calibration

Thomas Nauss

2019-12-09

The following example shows how to read a Landsat-8 data set downloaded from the USGS Earth Explorer and how to convert the bands’ scaled counts to radiance, reflectance and/or brightness temperature.

Reading Landsat-8 data

We will use the test data set supplied with this package. In order to read the data set, just supply the filename (or list of filenames) to the satellite() function.

library(satellite)
## Loading required package: raster
## Loading required package: sp
path <- system.file("extdata", package = "satellite")
files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE)
sat <- satellite(files)

The meta data will automatically be loaded since (i) Landsat-8 is a known platform to the satellite package and (ii) the name of the meta data file is in accordance with the USGS conventions.

Converting Landsat-8 scaled counts to physical units

In order to convert scaled counts of a Satellite object to radiance, reflectance and/or temperature, just supply it to the convSC2Rad, convSC2Ref or convRad2BT function.

sat <- convSC2Rad(sat)
sat <- convSC2Ref(sat)
sat <- convRad2BT(sat)
satellite/inst/testdata/0000755000176200001440000000000013572463522015037 5ustar liggesuserssatellite/inst/testdata/LE7/0000755000176200001440000000000013572463522015426 5ustar liggesuserssatellite/inst/testdata/LE7/LE71950252001211EDC00_MTL.txt0000644000176200001440000001553313572463522021172 0ustar liggesusersGROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0101411263324_00001" LANDSAT_SCENE_ID = "LE71950252001211EDC00" FILE_DATE = 2014-11-28T15:34:43Z STATION_ID = "EDC" PROCESSING_SOFTWARE_VERSION = "LPGS_12.5.0" DATA_CATEGORY = "NOMINAL" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1T" ELEVATION_SOURCE = "GLS2000" OUTPUT_FORMAT = "GEOTIFF" EPHEMERIS_TYPE = "DEFINITIVE" SPACECRAFT_ID = "LANDSAT_7" SENSOR_ID = "ETM" SENSOR_MODE = "SAM" WRS_PATH = 195 WRS_ROW = 025 DATE_ACQUIRED = 2001-07-30 SCENE_CENTER_TIME = 10:04:52.9157671Z CORNER_UL_LAT_PRODUCT = 51.26868 CORNER_UL_LON_PRODUCT = 7.28558 CORNER_UR_LAT_PRODUCT = 51.26807 CORNER_UR_LON_PRODUCT = 10.75597 CORNER_LL_LAT_PRODUCT = 49.27295 CORNER_LL_LON_PRODUCT = 7.35580 CORNER_LR_LAT_PRODUCT = 49.27238 CORNER_LR_LON_PRODUCT = 10.68405 CORNER_UL_PROJECTION_X_PRODUCT = 380400.000 CORNER_UL_PROJECTION_Y_PRODUCT = 5681100.000 CORNER_UR_PROJECTION_X_PRODUCT = 622500.000 CORNER_UR_PROJECTION_Y_PRODUCT = 5681100.000 CORNER_LL_PROJECTION_X_PRODUCT = 380400.000 CORNER_LL_PROJECTION_Y_PRODUCT = 5459100.000 CORNER_LR_PROJECTION_X_PRODUCT = 622500.000 CORNER_LR_PROJECTION_Y_PRODUCT = 5459100.000 PANCHROMATIC_LINES = 14801 PANCHROMATIC_SAMPLES = 16141 REFLECTIVE_LINES = 7401 REFLECTIVE_SAMPLES = 8071 THERMAL_LINES = 7401 THERMAL_SAMPLES = 8071 FILE_NAME_BAND_1 = "LE71950252001211EDC00_B1.TIF" FILE_NAME_BAND_2 = "LE71950252001211EDC00_B2.TIF" FILE_NAME_BAND_3 = "LE71950252001211EDC00_B3.TIF" FILE_NAME_BAND_4 = "LE71950252001211EDC00_B4.TIF" FILE_NAME_BAND_5 = "LE71950252001211EDC00_B5.TIF" FILE_NAME_BAND_6_VCID_1 = "LE71950252001211EDC00_B6_VCID_1.TIF" FILE_NAME_BAND_6_VCID_2 = "LE71950252001211EDC00_B6_VCID_2.TIF" FILE_NAME_BAND_7 = "LE71950252001211EDC00_B7.TIF" FILE_NAME_BAND_8 = "LE71950252001211EDC00_B8.TIF" GROUND_CONTROL_POINT_FILE_NAME = "LE71950252001211EDC00_GCP.txt" METADATA_FILE_NAME = "LE71950252001211EDC00_MTL.txt" CPF_NAME = "L7CPF20010701_20010930.11" END_GROUP = PRODUCT_METADATA GROUP = IMAGE_ATTRIBUTES CLOUD_COVER = 0.00 IMAGE_QUALITY = 9 SUN_AZIMUTH = 144.05820926 SUN_ELEVATION = 53.87765310 GROUND_CONTROL_POINTS_VERSION = 1 GROUND_CONTROL_POINTS_MODEL = 224 GEOMETRIC_RMSE_MODEL = 3.284 GEOMETRIC_RMSE_MODEL_Y = 2.723 GEOMETRIC_RMSE_MODEL_X = 1.835 END_GROUP = IMAGE_ATTRIBUTES GROUP = MIN_MAX_RADIANCE RADIANCE_MAXIMUM_BAND_1 = 191.600 RADIANCE_MINIMUM_BAND_1 = -6.200 RADIANCE_MAXIMUM_BAND_2 = 196.500 RADIANCE_MINIMUM_BAND_2 = -6.400 RADIANCE_MAXIMUM_BAND_3 = 152.900 RADIANCE_MINIMUM_BAND_3 = -5.000 RADIANCE_MAXIMUM_BAND_4 = 241.100 RADIANCE_MINIMUM_BAND_4 = -5.100 RADIANCE_MAXIMUM_BAND_5 = 31.060 RADIANCE_MINIMUM_BAND_5 = -1.000 RADIANCE_MAXIMUM_BAND_6_VCID_1 = 17.040 RADIANCE_MINIMUM_BAND_6_VCID_1 = 0.000 RADIANCE_MAXIMUM_BAND_6_VCID_2 = 12.650 RADIANCE_MINIMUM_BAND_6_VCID_2 = 3.200 RADIANCE_MAXIMUM_BAND_7 = 10.800 RADIANCE_MINIMUM_BAND_7 = -0.350 RADIANCE_MAXIMUM_BAND_8 = 243.100 RADIANCE_MINIMUM_BAND_8 = -4.700 END_GROUP = MIN_MAX_RADIANCE GROUP = MIN_MAX_PIXEL_VALUE QUANTIZE_CAL_MAX_BAND_1 = 255 QUANTIZE_CAL_MIN_BAND_1 = 1 QUANTIZE_CAL_MAX_BAND_2 = 255 QUANTIZE_CAL_MIN_BAND_2 = 1 QUANTIZE_CAL_MAX_BAND_3 = 255 QUANTIZE_CAL_MIN_BAND_3 = 1 QUANTIZE_CAL_MAX_BAND_4 = 255 QUANTIZE_CAL_MIN_BAND_4 = 1 QUANTIZE_CAL_MAX_BAND_5 = 255 QUANTIZE_CAL_MIN_BAND_5 = 1 QUANTIZE_CAL_MAX_BAND_6_VCID_1 = 255 QUANTIZE_CAL_MIN_BAND_6_VCID_1 = 1 QUANTIZE_CAL_MAX_BAND_6_VCID_2 = 255 QUANTIZE_CAL_MIN_BAND_6_VCID_2 = 1 QUANTIZE_CAL_MAX_BAND_7 = 255 QUANTIZE_CAL_MIN_BAND_7 = 1 QUANTIZE_CAL_MAX_BAND_8 = 255 QUANTIZE_CAL_MIN_BAND_8 = 1 END_GROUP = MIN_MAX_PIXEL_VALUE GROUP = PRODUCT_PARAMETERS CORRECTION_GAIN_BAND_1 = "CPF" CORRECTION_GAIN_BAND_2 = "CPF" CORRECTION_GAIN_BAND_3 = "CPF" CORRECTION_GAIN_BAND_4 = "CPF" CORRECTION_GAIN_BAND_5 = "CPF" CORRECTION_GAIN_BAND_6_VCID_1 = "CPF" CORRECTION_GAIN_BAND_6_VCID_2 = "CPF" CORRECTION_GAIN_BAND_7 = "CPF" CORRECTION_GAIN_BAND_8 = "CPF" CORRECTION_BIAS_BAND_1 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_2 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_3 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_4 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_5 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_6_VCID_1 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_6_VCID_2 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_7 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_8 = "INTERNAL_CALIBRATION" GAIN_BAND_1 = "H" GAIN_BAND_2 = "H" GAIN_BAND_3 = "H" GAIN_BAND_4 = "L" GAIN_BAND_5 = "H" GAIN_BAND_6_VCID_1 = "L" GAIN_BAND_6_VCID_2 = "H" GAIN_BAND_7 = "H" GAIN_BAND_8 = "L" GAIN_CHANGE_BAND_1 = "HH" GAIN_CHANGE_BAND_2 = "HH" GAIN_CHANGE_BAND_3 = "HH" GAIN_CHANGE_BAND_4 = "LL" GAIN_CHANGE_BAND_5 = "HH" GAIN_CHANGE_BAND_6_VCID_1 = "LL" GAIN_CHANGE_BAND_6_VCID_2 = "HH" GAIN_CHANGE_BAND_7 = "HH" GAIN_CHANGE_BAND_8 = "LL" GAIN_CHANGE_SCAN_BAND_1 = 0 GAIN_CHANGE_SCAN_BAND_2 = 0 GAIN_CHANGE_SCAN_BAND_3 = 0 GAIN_CHANGE_SCAN_BAND_4 = 0 GAIN_CHANGE_SCAN_BAND_5 = 0 GAIN_CHANGE_SCAN_BAND_6_VCID_1 = 0 GAIN_CHANGE_SCAN_BAND_6_VCID_2 = 0 GAIN_CHANGE_SCAN_BAND_7 = 0 GAIN_CHANGE_SCAN_BAND_8 = 0 END_GROUP = PRODUCT_PARAMETERS GROUP = RADIOMETRIC_RESCALING RADIANCE_MULT_BAND_1 = 0.779 RADIANCE_MULT_BAND_2 = 0.799 RADIANCE_MULT_BAND_3 = 0.622 RADIANCE_MULT_BAND_4 = 0.969 RADIANCE_MULT_BAND_5 = 0.126 RADIANCE_MULT_BAND_6_VCID_1 = 0.067 RADIANCE_MULT_BAND_6_VCID_2 = 0.037 RADIANCE_MULT_BAND_7 = 0.044 RADIANCE_MULT_BAND_8 = 0.976 RADIANCE_ADD_BAND_1 = -6.97874 RADIANCE_ADD_BAND_2 = -7.19882 RADIANCE_ADD_BAND_3 = -5.62165 RADIANCE_ADD_BAND_4 = -6.06929 RADIANCE_ADD_BAND_5 = -1.12622 RADIANCE_ADD_BAND_6_VCID_1 = -0.06709 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NWem@8o~,^9%,bώ) B2p+#kPe*&݃ l** RT)жA%\C&:r:QK,x%g~3nʇfm,o; K5Ha &$=@a%<.#-=)j-155ofx'2D~_$ 9n2l u%*ᓘM$>&>cCCAfUj !'jfq1ZSЮ9jiN`<4k  * =Ph"u -S)d,4(B1D&@rDF?JR%NeeH% (IGM ʫ={ be%RW%p#CP2+&Q'c6y# Tj|n`ꫲs$sg)FsV9n.N[T%#g+-WGy5FJiCTUo'Z: ;pBx Id79jy /=V,qWEoR㨀>CLCMNmyq/%eH:bFT|S&Jk=TJ,'*pE(-@-pW!iڌqBzr="!A!Ȅ8TF"F#Hf0E! cAAG1 I$c18EHH$#!Pv<R$!>PN'FB^&B#HHϣT8d3ZD!}MI(JAO8}BbdRDb#H!bnc$xt!U{T jC"DeBLMZAKdeWԕzA];fٺe 6"v Z B2#/v"M+^"MBۯȤLѢNj % !j rd!#%)\* ^7J(r Vrs3Mv:3; 6h$:63L3bTO@9>,e1CͨpH`ե4Q䔓~%jZVȳJ<"sL'lN$(r6M3 rXsZC3CJy4$d!4R(K!"1"Q 8uut]q%NbǶsZ* kKȄ쨵P(63t"A's\Z@HRD|0ES JlzO*A*͍;y/Q HBsΖ=Abm %mTx:jK$/ @kNݠh-ugM-d(Dddm--뽻on:[3Y iJPyS:I2DB;(BJ5Z>z\7=tHNǰ$2j[ǂ/9 eQgo*dq՟`mUb/}FƒCj) S ݤPHIi+slbj3.u o) 0V#FF>|x8Hٮ]Qlع :id,1I Փdj !`QlXR@ #fܲ9}-d`@satellite/inst/testdata/LT5/LT51670552010352MLK00_MTL.txt0000644000176200001440000017777713572463522021307 0ustar liggesusersGROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0101406012792_00050" LANDSAT_SCENE_ID = "LT51670552010352MLK00" FILE_DATE = 2014-06-02T21:33:27Z STATION_ID = "MLK" PROCESSING_SOFTWARE_VERSION = "LPGS_12.4.1" DATA_CATEGORY = "NOMINAL" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1T" DATA_TYPE_L0RP = "TMR_L0RP" ELEVATION_SOURCE = "GLS2000" OUTPUT_FORMAT = "GEOTIFF" EPHEMERIS_TYPE = "DEFINITIVE" SPACECRAFT_ID = "LANDSAT_5" SENSOR_ID = "TM" SENSOR_MODE = "BUMPER" WRS_PATH = 167 WRS_ROW = 055 DATE_ACQUIRED = 2010-12-18 SCENE_CENTER_TIME = 07:24:28.5030130Z CORNER_UL_LAT_PRODUCT = 8.16354 CORNER_UL_LON_PRODUCT = 39.40397 CORNER_UR_LAT_PRODUCT = 8.15575 CORNER_UR_LON_PRODUCT = 41.54374 CORNER_LL_LAT_PRODUCT = 6.28295 CORNER_LL_LON_PRODUCT = 39.40231 CORNER_LR_LAT_PRODUCT = 6.27698 CORNER_LR_LON_PRODUCT = 41.53327 CORNER_UL_PROJECTION_X_PRODUCT = 544500.000 CORNER_UL_PROJECTION_Y_PRODUCT = 902400.000 CORNER_UR_PROJECTION_X_PRODUCT = 780300.000 CORNER_UR_PROJECTION_Y_PRODUCT = 902400.000 CORNER_LL_PROJECTION_X_PRODUCT = 544500.000 CORNER_LL_PROJECTION_Y_PRODUCT = 694500.000 CORNER_LR_PROJECTION_X_PRODUCT = 780300.000 CORNER_LR_PROJECTION_Y_PRODUCT = 694500.000 REFLECTIVE_LINES = 6931 REFLECTIVE_SAMPLES = 7861 THERMAL_LINES = 6931 THERMAL_SAMPLES = 7861 FILE_NAME_BAND_1 = "LT51670552010352MLK00_B1.TIF" FILE_NAME_BAND_2 = "LT51670552010352MLK00_B2.TIF" FILE_NAME_BAND_3 = "LT51670552010352MLK00_B3.TIF" FILE_NAME_BAND_4 = "LT51670552010352MLK00_B4.TIF" FILE_NAME_BAND_5 = "LT51670552010352MLK00_B5.TIF" FILE_NAME_BAND_6 = "LT51670552010352MLK00_B6.TIF" FILE_NAME_BAND_7 = "LT51670552010352MLK00_B7.TIF" GROUND_CONTROL_POINT_FILE_NAME = "LT51670552010352MLK00_GCP.txt" REPORT_VERIFY_FILE_NAME = "LT51670552010352MLK00_VER.txt" BROWSE_VERIFY_FILE_NAME = "LT51670552010352MLK00_VER.jpg" METADATA_FILE_NAME = "LT51670552010352MLK00_MTL.txt" CPF_NAME = "L5CPF20101001_20101231.12" END_GROUP = PRODUCT_METADATA GROUP = IMAGE_ATTRIBUTES CLOUD_COVER = 0.00 IMAGE_QUALITY = 9 SUN_AZIMUTH = 139.48290834 SUN_ELEVATION = 49.25236265 GROUND_CONTROL_POINTS_MODEL = 213 GEOMETRIC_RMSE_MODEL = 3.589 GEOMETRIC_RMSE_MODEL_Y = 2.649 GEOMETRIC_RMSE_MODEL_X = 2.421 GROUND_CONTROL_POINTS_VERIFY = 3109 GEOMETRIC_RMSE_VERIFY = 0.143 GEOMETRIC_RMSE_VERIFY_QUAD_UL = 0.109 GEOMETRIC_RMSE_VERIFY_QUAD_UR = 0.118 GEOMETRIC_RMSE_VERIFY_QUAD_LL = 0.191 GEOMETRIC_RMSE_VERIFY_QUAD_LR = 0.154 END_GROUP = IMAGE_ATTRIBUTES GROUP = MIN_MAX_RADIANCE RADIANCE_MAXIMUM_BAND_1 = 193.000 RADIANCE_MINIMUM_BAND_1 = -1.520 RADIANCE_MAXIMUM_BAND_2 = 365.000 RADIANCE_MINIMUM_BAND_2 = -2.840 RADIANCE_MAXIMUM_BAND_3 = 264.000 RADIANCE_MINIMUM_BAND_3 = -1.170 RADIANCE_MAXIMUM_BAND_4 = 221.000 RADIANCE_MINIMUM_BAND_4 = -1.510 RADIANCE_MAXIMUM_BAND_5 = 30.200 RADIANCE_MINIMUM_BAND_5 = -0.370 RADIANCE_MAXIMUM_BAND_6 = 15.303 RADIANCE_MINIMUM_BAND_6 = 1.238 RADIANCE_MAXIMUM_BAND_7 = 16.500 RADIANCE_MINIMUM_BAND_7 = -0.150 END_GROUP = MIN_MAX_RADIANCE GROUP = MIN_MAX_PIXEL_VALUE QUANTIZE_CAL_MAX_BAND_1 = 255 QUANTIZE_CAL_MIN_BAND_1 = 1 QUANTIZE_CAL_MAX_BAND_2 = 255 QUANTIZE_CAL_MIN_BAND_2 = 1 QUANTIZE_CAL_MAX_BAND_3 = 255 QUANTIZE_CAL_MIN_BAND_3 = 1 QUANTIZE_CAL_MAX_BAND_4 = 255 QUANTIZE_CAL_MIN_BAND_4 = 1 QUANTIZE_CAL_MAX_BAND_5 = 255 QUANTIZE_CAL_MIN_BAND_5 = 1 QUANTIZE_CAL_MAX_BAND_6 = 255 QUANTIZE_CAL_MIN_BAND_6 = 1 QUANTIZE_CAL_MAX_BAND_7 = 255 QUANTIZE_CAL_MIN_BAND_7 = 1 END_GROUP = MIN_MAX_PIXEL_VALUE GROUP = PRODUCT_PARAMETERS CORRECTION_GAIN_BAND_1 = "CPF" CORRECTION_GAIN_BAND_2 = "CPF" CORRECTION_GAIN_BAND_3 = "CPF" CORRECTION_GAIN_BAND_4 = "CPF" CORRECTION_GAIN_BAND_5 = "CPF" CORRECTION_GAIN_BAND_6 = "INTERNAL_CALIBRATION" CORRECTION_GAIN_BAND_7 = "CPF" CORRECTION_BIAS_BAND_1 = "CPF" CORRECTION_BIAS_BAND_2 = "CPF" CORRECTION_BIAS_BAND_3 = "CPF" CORRECTION_BIAS_BAND_4 = "CPF" CORRECTION_BIAS_BAND_5 = "CPF" CORRECTION_BIAS_BAND_6 = "CPF" CORRECTION_BIAS_BAND_7 = "CPF" END_GROUP = PRODUCT_PARAMETERS GROUP = RADIOMETRIC_RESCALING RADIANCE_MULT_BAND_1 = 0.766 RADIANCE_MULT_BAND_2 = 1.448 RADIANCE_MULT_BAND_3 = 1.044 RADIANCE_MULT_BAND_4 = 0.876 RADIANCE_MULT_BAND_5 = 0.120 RADIANCE_MULT_BAND_6 = 0.055 RADIANCE_MULT_BAND_7 = 0.066 RADIANCE_ADD_BAND_1 = -2.28583 RADIANCE_ADD_BAND_2 = -4.28819 RADIANCE_ADD_BAND_3 = -2.21398 RADIANCE_ADD_BAND_4 = -2.38602 RADIANCE_ADD_BAND_5 = -0.49035 RADIANCE_ADD_BAND_6 = 1.18243 RADIANCE_ADD_BAND_7 = -0.21555 END_GROUP = RADIOMETRIC_RESCALING GROUP = PROJECTION_PARAMETERS MAP_PROJECTION = "UTM" DATUM = "WGS84" ELLIPSOID = "WGS84" UTM_ZONE = 37 GRID_CELL_SIZE_REFLECTIVE = 30.00 GRID_CELL_SIZE_THERMAL = 30.00 ORIENTATION = "NORTH_UP" RESAMPLING_OPTION = "CUBIC_CONVOLUTION" MAP_PROJECTION_L0RA = "NA" END_GROUP = PROJECTION_PARAMETERS END_GROUP = L1_METADATA_FILE END satellite/inst/testdata/LT5/LT51670552010352MLK00_B5.tif0000644000176200001440000002105013572463522021030 0ustar liggesusersII*eeQ=S    P)255} 165 112.3889814724 56 14.044873886999 >@>@!A'A!!# } )#UTM Zone 37, Northern Hemisphere|WGS 84|Nl3j3 &!jMf4͆|<4Mo; 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SL :B` ඓ!̅G8 zyP ANR VOLAѾBV :v(_ .(|[tAԌ \9EƒNc)?r / m #d)a՟0R\ au` &83BH\E \9֗"#q*HY\mIo JC8j 2C`ayH5P uD*E=Pd9e}AH^|W08P[^43jby"9<CDlmK$Ľ`I\1 R6!-rԜϣ7w-Å a|23v^ .b!AIq9YIwXCKIb\A ukdiB}TlY yㆀ֙dVҜ/"4To]p\ܜFXK2PXwl96p}0ɞd_z4I] p2/_A}~?ä_'ƻ.*̱WCr#ui\jHn <+ Cșa2Q(e1΄U'C }/D cA0^Ʉ7BWC9v,lpI 7u/л+Tp!c2(sr$ 0؏Fsatellite/inst/testdata/LT05/LT05_L1TP_167055_20000309_20161214_01_T1_B3.TIF0000644000176200001440000001426413572463522023336 0ustar liggesusersII*eeQ=S    P)255a 73 47.251936084698 29 5.6540851469754 >@>@!A'A!!# } )#UTM Zone 37, Northern Hemisphere|WGS 84| x, ED&/E _#f1 F1`2cAeEQ9P`1by^cLf"蘸d- LR (QuqEѤ:80…pȵr7H1}w1EF1V,1b"y6b5qDe^.gs#L+ C( 5b(Ky^G #XG=s t7;fT[Ogsyޝw2Wԛ^HHv;,Apf% 7!ͭac8j*.ٹ'ۚhB.P8ZPc-)!"K@T%l:np~=B5 B$=O\'At~2(!3Ÿ+(0#XN."cC#>,3zhJФ 3#:dT2|"(#L !PP5j-iҊ$I;m}rf¯ZA#0>7Lp o3\P\cBȕiv+]7#@AxNZqQr$ы8V$ʚJF=9=s@WY # !Pb`n# ) 1\c_}:X lA~mb>?t $a@DCW{̓I!w#-e]4Nd!j6'0Nb7>~!A@F?UnoIFA.U+$gV q t>ԧ`g6ӶHؙ$@)u !fTHJLP:7an9*˶HD}T앐t^G٦yH[}AL%PfgzUI H3t 9Uh1>3i76G B1)ʨڊ$Wj#Ŕ I}D=pmZmM YY440@ td)dyS l=hR+ڗSRnJ9w>#~(%J2gȣD,4MǸ-oq4@r 7nlz( @pqӣ+ipQx,P{BMz˳Y`$.sbN6hOWA!4瑼zr! d :@D;b׀BAE@r$A@Eװ,e'@氫 cW,6pdw(@A pT:K{ ١ x'OedYl7#,vD;PATz$L`۰;א"o6OҫRo;hq! 5KĠ<:DP*'$_tKɉ~49Bw;0DA"^<%6s$V轲i;+% -09l?j(H㢩m˛E"-UIyL2RFI@%6 V#ngF$b|g#xlOt|$.J D%Cܠ>%bJz'H"eNd~)`kjJ-T8bfk#<%PB<@bxAc7FtZ4z^O/>Jffe릩†FV-"<C],|nmzr<K JhehĦ ͆Fp Ƕ iؙ puO|j1&9d5gjNRZm&9lnF,f'jnJ^æ{d\ZR,lJn42*egno&(cJ4HND %䯘y -gHeiRMF1ÞZ |23xn d4<)j .*fWu+@A4C6zߑphGjܬ%0mfDI8UmC2n'a(%C- ˼? p~k!  #L،OBs 2$REia-ThJ^@UΆ_m7 f( "d!)br;kt.'JZƪnhZcUn&Ξ,fޫphY$l0v$O< p4b@w*@xN l;@A-nvXRLOlV5%@`&jQpOefzBn7k-$MTk`Fv23 p>H{"0'/<\.m3~h[bN B; ĠzVuBJ;2kGJХ#L)GM̩LJbP`Jx  ,)lfൊ&lUB'K^Oτnƌo`D0sEP3XHvrL"J &H=b\ޅ _ZhZd6j@hZGjĮ*r%nVHlfDnLrL #@bƌߠzTD0..π{;ͧ;:#bVCkhJrƒ1\ WER\.P,<$k8F=1FFbY%K @lTG#y\mNJ98OS~pJTI6t:J/%XH⩌Ƃpd$r7nF$q,%4#j"=D,v$Ho-`h9]$NDR9₤ `:F6V0^@2hI&$)FCf$26l9<1M%"Hb @<OTTK2 h"D $f+M "Y`(ъWW*#o &(p]I_NO,tAT0 dC'i bYM lwh5T];r5b%^6؆& LJ%9d7'mcbC'3" ͞Ď'vc s`DT(4XV MD]`Kc&Ot*Y`R 4z*&($JHNCGn pHU.,5 URxA1LR~ tNռIԵVFlK`O\LxhzjD6&5#LT3Ge䮀mx/T- gZbcb~O>EOY]f tO  p0 /ɇpX-TBhOAz APR5[93ؽAP^#:D쪠<Ȼ#,M$J*n46 xFzA-sX1l2̼$򢉅LxHTr; 3Թ.t2t`M]k# ".s%4̻MI9e)Ss&#[ˎ`!#ʤ:`\/P*qȲ"Ci*R$}"3x]X6/]` P26$.<ؗcq/G4Ld*';ɪC@Ot!F5esޫZ:U.ȳVݬnZ4pf8vkؕDPHK<0Kz=bnX5T=6T #(6P*Ą[H]\ug"/b%ѲfZNc7\sBZI֬e܈U;ag!'#k\7&֖޺%{cbݗas|1ifi \| 3r AAR)/SW"R"C;b^b6rjfhחPK X0 Ub2JkK`͢barV@' jKJ!GEV!1ʼ̡."$5A<+`ΟH&Պ>erl\bN(- A:I5). +,˦hraI"Q=FbrpY9 25jy&Xsatellite/inst/testdata/LT05/LT05_L1TP_167055_20000309_20161214_01_T1_MTL.txt0000644000176200001440000001662313572463522023724 0ustar liggesusersGROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0501612134675_10642" LANDSAT_SCENE_ID = "LT51670552000069AAA04" LANDSAT_PRODUCT_ID = "LT05_L1TP_167055_20000309_20161214_01_T1" COLLECTION_NUMBER = 01 FILE_DATE = 2016-12-14T23:49:32Z STATION_ID = "AAA" PROCESSING_SOFTWARE_VERSION = "LPGS_12.8.2" DATA_CATEGORY = "NOMINAL" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1TP" DATA_TYPE_L0RP = "TMR_L0RP" COLLECTION_CATEGORY = "T1" ELEVATION_SOURCE = "GLS2000" OUTPUT_FORMAT = "GEOTIFF" EPHEMERIS_TYPE = "PREDICTIVE" SPACECRAFT_ID = "LANDSAT_5" SENSOR_ID = "TM" SENSOR_MODE = "SAM" WRS_PATH = 167 WRS_ROW = 055 DATE_ACQUIRED = 2000-03-09 SCENE_CENTER_TIME = "07:08:03.9780190Z" CORNER_UL_LAT_PRODUCT = 8.13630 CORNER_UL_LON_PRODUCT = 39.50198 CORNER_UR_LAT_PRODUCT = 8.12821 CORNER_UR_LON_PRODUCT = 41.61158 CORNER_LL_LAT_PRODUCT = 6.23945 CORNER_LL_LON_PRODUCT = 39.49990 CORNER_LR_LAT_PRODUCT = 6.23327 CORNER_LR_LON_PRODUCT = 41.60079 CORNER_UL_PROJECTION_X_PRODUCT = 555300.000 CORNER_UL_PROJECTION_Y_PRODUCT = 899400.000 CORNER_UR_PROJECTION_X_PRODUCT = 787800.000 CORNER_UR_PROJECTION_Y_PRODUCT = 899400.000 CORNER_LL_PROJECTION_X_PRODUCT = 555300.000 CORNER_LL_PROJECTION_Y_PRODUCT = 689700.000 CORNER_LR_PROJECTION_X_PRODUCT = 787800.000 CORNER_LR_PROJECTION_Y_PRODUCT = 689700.000 REFLECTIVE_LINES = 6991 REFLECTIVE_SAMPLES = 7751 THERMAL_LINES = 6991 THERMAL_SAMPLES = 7751 FILE_NAME_BAND_1 = "LT05_L1TP_167055_20000309_20161214_01_T1_B1.TIF" FILE_NAME_BAND_2 = "LT05_L1TP_167055_20000309_20161214_01_T1_B2.TIF" FILE_NAME_BAND_3 = "LT05_L1TP_167055_20000309_20161214_01_T1_B3.TIF" FILE_NAME_BAND_4 = "LT05_L1TP_167055_20000309_20161214_01_T1_B4.TIF" FILE_NAME_BAND_5 = "LT05_L1TP_167055_20000309_20161214_01_T1_B5.TIF" FILE_NAME_BAND_6 = "LT05_L1TP_167055_20000309_20161214_01_T1_B6.TIF" FILE_NAME_BAND_7 = "LT05_L1TP_167055_20000309_20161214_01_T1_B7.TIF" FILE_NAME_BAND_QUALITY = "LT05_L1TP_167055_20000309_20161214_01_T1_BQA.TIF" GROUND_CONTROL_POINT_FILE_NAME = "LT05_L1TP_167055_20000309_20161214_01_T1_GCP.txt" ANGLE_COEFFICIENT_FILE_NAME = "LT05_L1TP_167055_20000309_20161214_01_T1_ANG.txt" REPORT_VERIFY_FILE_NAME = "LT05_L1TP_167055_20000309_20161214_01_T1_VER.txt" BROWSE_VERIFY_FILE_NAME = "LT05_L1TP_167055_20000309_20161214_01_T1_VER.jpg" METADATA_FILE_NAME = "LT05_L1TP_167055_20000309_20161214_01_T1_MTL.txt" CPF_NAME = "LT05CPF_20000101_20000331_01.03" END_GROUP = PRODUCT_METADATA GROUP = IMAGE_ATTRIBUTES CLOUD_COVER = 0.00 CLOUD_COVER_LAND = 0.00 IMAGE_QUALITY = 7 SUN_AZIMUTH = 107.22126345 SUN_ELEVATION = 53.14715018 EARTH_SUN_DISTANCE = 0.9929941 SATURATION_BAND_1 = "Y" SATURATION_BAND_2 = "Y" SATURATION_BAND_3 = "Y" SATURATION_BAND_4 = "Y" SATURATION_BAND_5 = "Y" SATURATION_BAND_6 = "N" SATURATION_BAND_7 = "Y" GROUND_CONTROL_POINTS_VERSION = 4 GROUND_CONTROL_POINTS_MODEL = 203 GEOMETRIC_RMSE_MODEL = 3.788 GEOMETRIC_RMSE_MODEL_Y = 2.611 GEOMETRIC_RMSE_MODEL_X = 2.744 GROUND_CONTROL_POINTS_VERIFY = 2959 GEOMETRIC_RMSE_VERIFY = 0.150 GEOMETRIC_RMSE_VERIFY_QUAD_UL = 0.134 GEOMETRIC_RMSE_VERIFY_QUAD_UR = 0.121 GEOMETRIC_RMSE_VERIFY_QUAD_LL = 0.192 GEOMETRIC_RMSE_VERIFY_QUAD_LR = 0.170 END_GROUP = IMAGE_ATTRIBUTES GROUP = MIN_MAX_RADIANCE RADIANCE_MAXIMUM_BAND_1 = 193.000 RADIANCE_MINIMUM_BAND_1 = -1.520 RADIANCE_MAXIMUM_BAND_2 = 365.000 RADIANCE_MINIMUM_BAND_2 = -2.840 RADIANCE_MAXIMUM_BAND_3 = 264.000 RADIANCE_MINIMUM_BAND_3 = -1.170 RADIANCE_MAXIMUM_BAND_4 = 221.000 RADIANCE_MINIMUM_BAND_4 = -1.510 RADIANCE_MAXIMUM_BAND_5 = 30.200 RADIANCE_MINIMUM_BAND_5 = -0.370 RADIANCE_MAXIMUM_BAND_6 = 15.303 RADIANCE_MINIMUM_BAND_6 = 1.238 RADIANCE_MAXIMUM_BAND_7 = 16.500 RADIANCE_MINIMUM_BAND_7 = -0.150 END_GROUP = MIN_MAX_RADIANCE GROUP = MIN_MAX_REFLECTANCE REFLECTANCE_MAXIMUM_BAND_1 = 0.307542 REFLECTANCE_MINIMUM_BAND_1 = -0.002422 REFLECTANCE_MAXIMUM_BAND_2 = 0.642792 REFLECTANCE_MINIMUM_BAND_2 = -0.005001 REFLECTANCE_MAXIMUM_BAND_3 = 0.548859 REFLECTANCE_MINIMUM_BAND_3 = -0.002432 REFLECTANCE_MAXIMUM_BAND_4 = 0.662728 REFLECTANCE_MINIMUM_BAND_4 = -0.004528 REFLECTANCE_MAXIMUM_BAND_5 = 0.446333 REFLECTANCE_MINIMUM_BAND_5 = -0.005468 REFLECTANCE_MAXIMUM_BAND_7 = 0.621504 REFLECTANCE_MINIMUM_BAND_7 = -0.005650 END_GROUP = MIN_MAX_REFLECTANCE GROUP = MIN_MAX_PIXEL_VALUE QUANTIZE_CAL_MAX_BAND_1 = 255 QUANTIZE_CAL_MIN_BAND_1 = 1 QUANTIZE_CAL_MAX_BAND_2 = 255 QUANTIZE_CAL_MIN_BAND_2 = 1 QUANTIZE_CAL_MAX_BAND_3 = 255 QUANTIZE_CAL_MIN_BAND_3 = 1 QUANTIZE_CAL_MAX_BAND_4 = 255 QUANTIZE_CAL_MIN_BAND_4 = 1 QUANTIZE_CAL_MAX_BAND_5 = 255 QUANTIZE_CAL_MIN_BAND_5 = 1 QUANTIZE_CAL_MAX_BAND_6 = 255 QUANTIZE_CAL_MIN_BAND_6 = 1 QUANTIZE_CAL_MAX_BAND_7 = 255 QUANTIZE_CAL_MIN_BAND_7 = 1 END_GROUP = MIN_MAX_PIXEL_VALUE GROUP = PRODUCT_PARAMETERS CORRECTION_GAIN_BAND_1 = "CPF" CORRECTION_GAIN_BAND_2 = "CPF" CORRECTION_GAIN_BAND_3 = "CPF" CORRECTION_GAIN_BAND_4 = "CPF" CORRECTION_GAIN_BAND_5 = "CPF" CORRECTION_GAIN_BAND_6 = "INTERNAL_CALIBRATION" CORRECTION_GAIN_BAND_7 = "CPF" CORRECTION_BIAS_BAND_1 = "CPF" CORRECTION_BIAS_BAND_2 = "CPF" CORRECTION_BIAS_BAND_3 = "CPF" CORRECTION_BIAS_BAND_4 = "CPF" CORRECTION_BIAS_BAND_5 = "CPF" CORRECTION_BIAS_BAND_6 = "CPF" CORRECTION_BIAS_BAND_7 = "CPF" END_GROUP = PRODUCT_PARAMETERS GROUP = RADIOMETRIC_RESCALING RADIANCE_MULT_BAND_1 = 7.6583E-01 RADIANCE_MULT_BAND_2 = 1.4482E+00 RADIANCE_MULT_BAND_3 = 1.0440E+00 RADIANCE_MULT_BAND_4 = 8.7602E-01 RADIANCE_MULT_BAND_5 = 1.2035E-01 RADIANCE_MULT_BAND_6 = 5.5375E-02 RADIANCE_MULT_BAND_7 = 6.5551E-02 RADIANCE_ADD_BAND_1 = -2.28583 RADIANCE_ADD_BAND_2 = -4.28819 RADIANCE_ADD_BAND_3 = -2.21398 RADIANCE_ADD_BAND_4 = -2.38602 RADIANCE_ADD_BAND_5 = -0.49035 RADIANCE_ADD_BAND_6 = 1.18243 RADIANCE_ADD_BAND_7 = -0.21555 REFLECTANCE_MULT_BAND_1 = 1.2203E-03 REFLECTANCE_MULT_BAND_2 = 2.5504E-03 REFLECTANCE_MULT_BAND_3 = 2.1704E-03 REFLECTANCE_MULT_BAND_4 = 2.6270E-03 REFLECTANCE_MULT_BAND_5 = 1.7787E-03 REFLECTANCE_MULT_BAND_7 = 2.4691E-03 REFLECTANCE_ADD_BAND_1 = -0.003642 REFLECTANCE_ADD_BAND_2 = -0.007552 REFLECTANCE_ADD_BAND_3 = -0.004603 REFLECTANCE_ADD_BAND_4 = -0.007155 REFLECTANCE_ADD_BAND_5 = -0.007247 REFLECTANCE_ADD_BAND_7 = -0.008119 END_GROUP = RADIOMETRIC_RESCALING GROUP = THERMAL_CONSTANTS K1_CONSTANT_BAND_6 = 607.76 K2_CONSTANT_BAND_6 = 1260.56 END_GROUP = THERMAL_CONSTANTS GROUP = PROJECTION_PARAMETERS MAP_PROJECTION = "UTM" DATUM = "WGS84" ELLIPSOID = "WGS84" UTM_ZONE = 37 GRID_CELL_SIZE_REFLECTIVE = 30.00 GRID_CELL_SIZE_THERMAL = 30.00 ORIENTATION = "NORTH_UP" RESAMPLING_OPTION = "CUBIC_CONVOLUTION" MAP_PROJECTION_L0RA = "NA" END_GROUP = PROJECTION_PARAMETERS END_GROUP = L1_METADATA_FILE END satellite/inst/testdata/LT05/LT05_L1TP_167055_20000309_20161214_01_T1_B1.TIF0000644000176200001440000001345613572463522023336 0ustar liggesusersII*eeQ=S    P)255d 96 71.022350749926 50 3.8681061894732 >@>@!A'A!!# } )#UTM Zone 37, Northern Hemisphere|WGS 84|YXH"HB)F$Ii $1fDrY$%&.D"1 A"ȱlG#H  УJ Mb$R83HbY4OlCDrICQ 2L&"! 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Rq#"0o9:b= @zg¸ , b7J8r9ԶNZ3yE[]fO2'W{y9Ztw%" }#0zD ƤBey:.j$ lgXèC!%:玺X=5COJ wN!,_LZ}N'N &F,j%CuLOj XC!y^!: *"/6B!"ʌ!%rCqppX~lE7@` ,!t l M@'ҞӳtAVM#$ 5ؠCf^KA naV:*@T2*dh߻/chI4%d@Yl N  o<8'J* KO(DB'e iF,4v'$V2Ιy>e?F4e1B$9pRLM TO)0}1L7Rtƙ6[$b_OyUj !DjF u P q!f!YO.(mcy}Vj&d9!*% NHZ&Ғpk!>e:(=xoՙ{C,WzPs ̄,*F9f䁿 #DW~=luRUH.+㜷vW]J _.{4C#n&KWh"n w kW͎ɐ^Z¦JuERu"i+V/ UV@@ P&Ŕ1ЀFepX$=>krk7Kh橗V r%^i :fԕ!<7P4b0a_l*V!hߍe!4C|aCzvz;@8$sVC%Q?-asatellite/inst/extdata/0000755000176200001440000000000013572463522014660 5ustar liggesuserssatellite/inst/extdata/LE07_L1TP_195025_20010730_20170204_01_T1_MTL.txt0000644000176200001440000002223713572463522023036 0ustar liggesusersGROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0501702031891_15339" LANDSAT_SCENE_ID = "LE71950252001211EDC00" LANDSAT_PRODUCT_ID = "LE07_L1TP_195025_20010730_20170204_01_T1" COLLECTION_NUMBER = 01 FILE_DATE = 2017-02-04T08:28:18Z STATION_ID = "EDC" PROCESSING_SOFTWARE_VERSION = "LPGS_12.8.3" DATA_CATEGORY = "NOMINAL" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1TP" COLLECTION_CATEGORY = "T1" ELEVATION_SOURCE = "GLS2000" OUTPUT_FORMAT = "GEOTIFF" EPHEMERIS_TYPE = "DEFINITIVE" SPACECRAFT_ID = "LANDSAT_7" SENSOR_ID = "ETM" SENSOR_MODE = "SAM" WRS_PATH = 195 WRS_ROW = 025 DATE_ACQUIRED = 2001-07-30 SCENE_CENTER_TIME = "10:04:52.9157671Z" CORNER_UL_LAT_PRODUCT = 51.26868 CORNER_UL_LON_PRODUCT = 7.28558 CORNER_UR_LAT_PRODUCT = 51.26807 CORNER_UR_LON_PRODUCT = 10.75597 CORNER_LL_LAT_PRODUCT = 49.27295 CORNER_LL_LON_PRODUCT = 7.35580 CORNER_LR_LAT_PRODUCT = 49.27238 CORNER_LR_LON_PRODUCT = 10.68405 CORNER_UL_PROJECTION_X_PRODUCT = 380400.000 CORNER_UL_PROJECTION_Y_PRODUCT = 5681100.000 CORNER_UR_PROJECTION_X_PRODUCT = 622500.000 CORNER_UR_PROJECTION_Y_PRODUCT = 5681100.000 CORNER_LL_PROJECTION_X_PRODUCT = 380400.000 CORNER_LL_PROJECTION_Y_PRODUCT = 5459100.000 CORNER_LR_PROJECTION_X_PRODUCT = 622500.000 CORNER_LR_PROJECTION_Y_PRODUCT = 5459100.000 PANCHROMATIC_LINES = 14801 PANCHROMATIC_SAMPLES = 16141 REFLECTIVE_LINES = 7401 REFLECTIVE_SAMPLES = 8071 THERMAL_LINES = 7401 THERMAL_SAMPLES = 8071 FILE_NAME_BAND_1 = "LE07_L1TP_195025_20010730_20170204_01_T1_B1.TIF" FILE_NAME_BAND_2 = "LE07_L1TP_195025_20010730_20170204_01_T1_B2.TIF" FILE_NAME_BAND_3 = "LE07_L1TP_195025_20010730_20170204_01_T1_B3.TIF" FILE_NAME_BAND_4 = "LE07_L1TP_195025_20010730_20170204_01_T1_B4.TIF" FILE_NAME_BAND_5 = "LE07_L1TP_195025_20010730_20170204_01_T1_B5.TIF" FILE_NAME_BAND_6_VCID_1 = "LE07_L1TP_195025_20010730_20170204_01_T1_B6_VCID_1.TIF" FILE_NAME_BAND_6_VCID_2 = "LE07_L1TP_195025_20010730_20170204_01_T1_B6_VCID_2.TIF" FILE_NAME_BAND_7 = "LE07_L1TP_195025_20010730_20170204_01_T1_B7.TIF" FILE_NAME_BAND_8 = "LE07_L1TP_195025_20010730_20170204_01_T1_B8.TIF" FILE_NAME_BAND_QUALITY = "LE07_L1TP_195025_20010730_20170204_01_T1_BQA.TIF" GROUND_CONTROL_POINT_FILE_NAME = "LE07_L1TP_195025_20010730_20170204_01_T1_GCP.txt" ANGLE_COEFFICIENT_FILE_NAME = "LE07_L1TP_195025_20010730_20170204_01_T1_ANG.txt" METADATA_FILE_NAME = "LE07_L1TP_195025_20010730_20170204_01_T1_MTL.txt" CPF_NAME = "LE07CPF_20010701_20010930_01.02" END_GROUP = PRODUCT_METADATA GROUP = IMAGE_ATTRIBUTES CLOUD_COVER = 0.00 CLOUD_COVER_LAND = 0.00 IMAGE_QUALITY = 9 SUN_AZIMUTH = 144.05820926 SUN_ELEVATION = 53.87765310 EARTH_SUN_DISTANCE = 1.0151738 SATURATION_BAND_1 = "Y" SATURATION_BAND_2 = "Y" SATURATION_BAND_3 = "Y" SATURATION_BAND_4 = "Y" SATURATION_BAND_5 = "Y" SATURATION_BAND_6_VCID_1 = "N" SATURATION_BAND_6_VCID_2 = "N" SATURATION_BAND_7 = "Y" SATURATION_BAND_8 = "N" GROUND_CONTROL_POINTS_VERSION = 4 GROUND_CONTROL_POINTS_MODEL = 224 GEOMETRIC_RMSE_MODEL = 3.284 GEOMETRIC_RMSE_MODEL_Y = 2.723 GEOMETRIC_RMSE_MODEL_X = 1.835 END_GROUP = IMAGE_ATTRIBUTES GROUP = MIN_MAX_RADIANCE RADIANCE_MAXIMUM_BAND_1 = 191.600 RADIANCE_MINIMUM_BAND_1 = -6.200 RADIANCE_MAXIMUM_BAND_2 = 196.500 RADIANCE_MINIMUM_BAND_2 = -6.400 RADIANCE_MAXIMUM_BAND_3 = 152.900 RADIANCE_MINIMUM_BAND_3 = -5.000 RADIANCE_MAXIMUM_BAND_4 = 241.100 RADIANCE_MINIMUM_BAND_4 = -5.100 RADIANCE_MAXIMUM_BAND_5 = 31.060 RADIANCE_MINIMUM_BAND_5 = -1.000 RADIANCE_MAXIMUM_BAND_6_VCID_1 = 17.040 RADIANCE_MINIMUM_BAND_6_VCID_1 = 0.000 RADIANCE_MAXIMUM_BAND_6_VCID_2 = 12.650 RADIANCE_MINIMUM_BAND_6_VCID_2 = 3.200 RADIANCE_MAXIMUM_BAND_7 = 10.800 RADIANCE_MINIMUM_BAND_7 = -0.350 RADIANCE_MAXIMUM_BAND_8 = 243.100 RADIANCE_MINIMUM_BAND_8 = -4.700 END_GROUP = MIN_MAX_RADIANCE GROUP = MIN_MAX_REFLECTANCE REFLECTANCE_MAXIMUM_BAND_1 = 0.304683 REFLECTANCE_MINIMUM_BAND_1 = -0.009859 REFLECTANCE_MAXIMUM_BAND_2 = 0.342780 REFLECTANCE_MINIMUM_BAND_2 = -0.011164 REFLECTANCE_MAXIMUM_BAND_3 = 0.324615 REFLECTANCE_MINIMUM_BAND_3 = -0.010615 REFLECTANCE_MAXIMUM_BAND_4 = 0.728850 REFLECTANCE_MINIMUM_BAND_4 = -0.015417 REFLECTANCE_MAXIMUM_BAND_5 = 0.453798 REFLECTANCE_MINIMUM_BAND_5 = -0.014610 REFLECTANCE_MAXIMUM_BAND_7 = 0.429777 REFLECTANCE_MINIMUM_BAND_7 = -0.013928 REFLECTANCE_MAXIMUM_BAND_8 = 0.596720 REFLECTANCE_MINIMUM_BAND_8 = -0.011537 END_GROUP = MIN_MAX_REFLECTANCE GROUP = MIN_MAX_PIXEL_VALUE QUANTIZE_CAL_MAX_BAND_1 = 255 QUANTIZE_CAL_MIN_BAND_1 = 1 QUANTIZE_CAL_MAX_BAND_2 = 255 QUANTIZE_CAL_MIN_BAND_2 = 1 QUANTIZE_CAL_MAX_BAND_3 = 255 QUANTIZE_CAL_MIN_BAND_3 = 1 QUANTIZE_CAL_MAX_BAND_4 = 255 QUANTIZE_CAL_MIN_BAND_4 = 1 QUANTIZE_CAL_MAX_BAND_5 = 255 QUANTIZE_CAL_MIN_BAND_5 = 1 QUANTIZE_CAL_MAX_BAND_6_VCID_1 = 255 QUANTIZE_CAL_MIN_BAND_6_VCID_1 = 1 QUANTIZE_CAL_MAX_BAND_6_VCID_2 = 255 QUANTIZE_CAL_MIN_BAND_6_VCID_2 = 1 QUANTIZE_CAL_MAX_BAND_7 = 255 QUANTIZE_CAL_MIN_BAND_7 = 1 QUANTIZE_CAL_MAX_BAND_8 = 255 QUANTIZE_CAL_MIN_BAND_8 = 1 END_GROUP = MIN_MAX_PIXEL_VALUE GROUP = PRODUCT_PARAMETERS CORRECTION_GAIN_BAND_1 = "CPF" CORRECTION_GAIN_BAND_2 = "CPF" CORRECTION_GAIN_BAND_3 = "CPF" CORRECTION_GAIN_BAND_4 = "CPF" CORRECTION_GAIN_BAND_5 = "CPF" CORRECTION_GAIN_BAND_6_VCID_1 = "CPF" CORRECTION_GAIN_BAND_6_VCID_2 = "CPF" CORRECTION_GAIN_BAND_7 = "CPF" CORRECTION_GAIN_BAND_8 = "CPF" CORRECTION_BIAS_BAND_1 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_2 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_3 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_4 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_5 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_6_VCID_1 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_6_VCID_2 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_7 = "INTERNAL_CALIBRATION" CORRECTION_BIAS_BAND_8 = "INTERNAL_CALIBRATION" GAIN_BAND_1 = "H" GAIN_BAND_2 = "H" GAIN_BAND_3 = "H" GAIN_BAND_4 = "L" GAIN_BAND_5 = "H" GAIN_BAND_6_VCID_1 = "L" GAIN_BAND_6_VCID_2 = "H" GAIN_BAND_7 = "H" GAIN_BAND_8 = "L" GAIN_CHANGE_BAND_1 = "HH" GAIN_CHANGE_BAND_2 = "HH" GAIN_CHANGE_BAND_3 = "HH" GAIN_CHANGE_BAND_4 = "LL" GAIN_CHANGE_BAND_5 = "HH" GAIN_CHANGE_BAND_6_VCID_1 = "LL" GAIN_CHANGE_BAND_6_VCID_2 = "HH" GAIN_CHANGE_BAND_7 = "HH" GAIN_CHANGE_BAND_8 = "LL" GAIN_CHANGE_SCAN_BAND_1 = 0 GAIN_CHANGE_SCAN_BAND_2 = 0 GAIN_CHANGE_SCAN_BAND_3 = 0 GAIN_CHANGE_SCAN_BAND_4 = 0 GAIN_CHANGE_SCAN_BAND_5 = 0 GAIN_CHANGE_SCAN_BAND_6_VCID_1 = 0 GAIN_CHANGE_SCAN_BAND_6_VCID_2 = 0 GAIN_CHANGE_SCAN_BAND_7 = 0 GAIN_CHANGE_SCAN_BAND_8 = 0 END_GROUP = PRODUCT_PARAMETERS GROUP = RADIOMETRIC_RESCALING RADIANCE_MULT_BAND_1 = 7.7874E-01 RADIANCE_MULT_BAND_2 = 7.9882E-01 RADIANCE_MULT_BAND_3 = 6.2165E-01 RADIANCE_MULT_BAND_4 = 9.6929E-01 RADIANCE_MULT_BAND_5 = 1.2622E-01 RADIANCE_MULT_BAND_6_VCID_1 = 6.7087E-02 RADIANCE_MULT_BAND_6_VCID_2 = 3.7205E-02 RADIANCE_MULT_BAND_7 = 4.3898E-02 RADIANCE_MULT_BAND_8 = 9.7559E-01 RADIANCE_ADD_BAND_1 = -6.97874 RADIANCE_ADD_BAND_2 = -7.19882 RADIANCE_ADD_BAND_3 = -5.62165 RADIANCE_ADD_BAND_4 = -6.06929 RADIANCE_ADD_BAND_5 = -1.12622 RADIANCE_ADD_BAND_6_VCID_1 = -0.06709 RADIANCE_ADD_BAND_6_VCID_2 = 3.16280 RADIANCE_ADD_BAND_7 = -0.39390 RADIANCE_ADD_BAND_8 = -5.67559 REFLECTANCE_MULT_BAND_1 = 1.2384E-03 REFLECTANCE_MULT_BAND_2 = 1.3935E-03 REFLECTANCE_MULT_BAND_3 = 1.3198E-03 REFLECTANCE_MULT_BAND_4 = 2.9302E-03 REFLECTANCE_MULT_BAND_5 = 1.8441E-03 REFLECTANCE_MULT_BAND_7 = 1.7469E-03 REFLECTANCE_MULT_BAND_8 = 2.3947E-03 REFLECTANCE_ADD_BAND_1 = -0.011098 REFLECTANCE_ADD_BAND_2 = -0.012558 REFLECTANCE_ADD_BAND_3 = -0.011935 REFLECTANCE_ADD_BAND_4 = -0.018348 REFLECTANCE_ADD_BAND_5 = -0.016454 REFLECTANCE_ADD_BAND_7 = -0.015675 REFLECTANCE_ADD_BAND_8 = -0.013931 END_GROUP = RADIOMETRIC_RESCALING GROUP = THERMAL_CONSTANTS K1_CONSTANT_BAND_6_VCID_1 = 666.09 K2_CONSTANT_BAND_6_VCID_1 = 1282.71 K1_CONSTANT_BAND_6_VCID_2 = 666.09 K2_CONSTANT_BAND_6_VCID_2 = 1282.71 END_GROUP = THERMAL_CONSTANTS GROUP = PROJECTION_PARAMETERS MAP_PROJECTION = "UTM" DATUM = "WGS84" ELLIPSOID = "WGS84" UTM_ZONE = 32 GRID_CELL_SIZE_PANCHROMATIC = 15.00 GRID_CELL_SIZE_REFLECTIVE = 30.00 GRID_CELL_SIZE_THERMAL = 30.00 ORIENTATION = "NORTH_UP" RESAMPLING_OPTION = "CUBIC_CONVOLUTION" END_GROUP = PROJECTION_PARAMETERS END_GROUP = L1_METADATA_FILE END 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Lf*@" S& F6@J B@B tr`>Bo8&c@`DB'n(J\((xA+%XwA"AxcB > >A<p D2 I!UF /J QB* ENI*DvI@`>?*M:E"@RF/C a@+`<A_0Md"GH> `@H @JvB >p{@ @?m`oABeIf 6 Q@D F<F$! >@> satellite/inst/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B10.TIF0000644000176200001440000001073713572463522022541 0ustar liggesusersII*)))(=S   N) 31926 29517.210588935 27494 893.69146010466 -32768>@>@TA@xUA!!# x )#UTM Zone 32, Northern Hemisphere|WGS 84|H[9?N cT:N; s=*L34<;N`9g8:C;ã:΂cw9NN39#Ns|; ǰzINǃFveTgJJ9:ƒL;#D':͒s^s3'Vm4`<*c,=bC:N3s sp'/U"i@c@ %%>ĎbƘaq; #p:C9C9㘰b0H;C<0Bm!0pJ(/h":G2NPaNn;;h>l`(68|jl&@1&`P ǠtgFZ :t:_W(&: C~: C,:àl:á:é;<M$1vZPtb8:`gVv8t(xP#;8o`o [\PC3:CQ1"xx:PFum<; 8vAɇP- QqHoA]˄Xt AjP:p#èx!h/Ua䔃^ ;t?!8 0Ch" 70J@r!e1N `A.C(: Pv Nap(v)C`;0'P<Y bWbo !EXr.@RGpu ;  ta p%C<6e; -ǜAڊHt8p h 7 p*CJ#8`'*a|v !Guj0Ǭ4P:<&x'^aJ:P;$7 _o@:raN5Y; gvPQ\ðH,:1Nwbu 5XgjZ(`<@89p 8XFF .`rO2 Э#12(@:5Eu!ZATl05@6°q!YJ?ðQw"|Nšf :\uPo"JcP9P gVGs Ap*Ch;1&O:6:t,;F*䙬d:!t^DpaVa`>LJŰ|A*`(u]wC[It:5Y@tbC`;쀅Ptx@h9  7C"94<=5uo@pt e@ xd:x1$;םp0p t7r 5qzBz AI\xQAE*eF@/֍{,e Pso a\GhmutDJCf|S`Y`9,ƍG!)PBͪ;S.-/x5 67@mאgkPZupnøݡ0*UMb8xu)NV4=èvP1CX# 7lmC(p a 4ðiMøR&rr Z$?nI?/LP!OCp D5ИmAWG]!+]  `9 @j O`Hhf`@oZ ` aP a 5!܉f 8 V2)!> v  RSLpb*o5qA} ( H  Ln !: !L D @``2 @ k`` p &@ ``X f  : x  B +\ a@,j N } 2 V ` J @@   ` `z 4 v @V B   p T %Է @\ 8T6 7@jr R @3' C0-W `@ @ `AA L `H ) T x : > H RF*+ )xA@f   _,N : b ҨN Lp@  g!N!   C\@A$ANƂ掠v   2 HBf  <@ lrAƠ@ @@@&@N  @_@ h n @` f `! `` qDHTQ`^HLUF 6~ &A8 $ 2 @ hs!N?@ ;`$oP  \ HP h B! d   |    e^dQ6F !h~  ( t $ R K l-aA Q5D`M5-Vx`Wa!0 h ` bsatellite/inst/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B5.TIF0000644000176200001440000001164413572463522022463 0ustar liggesusersII*)))=S   N) 25759 15496.998215348 8337 2973.0538735643 -32768>@>@TA@xUA!!# x )#UTM Zone 32, Northern Hemisphere|WGS 84| ѳ^"r؎ Ec Z ,AIH\$"⨼-- ݢE`-w Wp͖6p\7FK#Cxx7GQA(HiX/¡9s.0 $1rԸ, ۂS-.* c0ɤ3QCtx='P7p<,dx`< ĂALjx!E`&DɁjh&1( Z9N1QLckdFeLXbFZ~Ǣ(8 xe @ % Gp8~`Eh~:"xXa A0h aQb!p A a*0"Pz!QN(&!$ \XDaYi aQ!y ar!!a!aq.ᡚ %!$Zj ᘖa& !Q@x8XxL~hn$frn:(p f~8zQ0ZCHvVc~/jx(dX&d`f08b~(CX~`"Z xr8ZeTɨ>A!A⇣@~EbKي!"% ,Lph$a!Aa /e@j>F\b%b%bA< /d`}ANt\xh%8ZH\'j `f=X`(PgRp!a( ‰r)b laQ~i>,Ax*B /C$ P?=H1b 5`XavA!P@t$ Bb` 'pPb wѸ G# ypx;&@9 Q 10t @$ $f.2!d*Pf#Ag  zYp,`M P\b:`M@&P4C2@< @ `*g |B0Kx X,  0 e <J f 0Pc!(` EL @&G@E4 >[a A<3"h0pm M AL#00 @9 `;sxUP-hB`X !Pp*B0 +BHô ~ =0]U(8 j07FQȲ@ p@!zl` B(p B ((Xi L"z#pZ`v=A,7AФ "b>-n!B70SoD/4iR@@#N!Y S, D84I n9p8dH6 u`>$@ BF1+A)x  r0 #,"rk0Kaw_ h/}Pz | =B@w'AC @{y0lP`j8;L ~ j,D{h ~ H@ @r/\^@l  R`1a` 2A 6r^\ ~ nx ~XPf @xp\X,f*^  . `z  $bB A!z `pd nx  H`fv``@ |` z@`~x ^`\,ajn`pn@p zA- Hcb ` A. aHArDe@J>cje@| v0&|W!& ` da:o ! r r ~ $ a4  b^ fp tpmJ dvt `~O8a"  aA6r  @|x`@@@h n lV0``lˠ~ ` l bm6h     lZ d `h|a(!BNEAz ! * a !p` @d`v | @ r`rtda6@ A @ ?0@\|z@  . ^ ^@l7P4r  r`nhx~;a@ A"4 @@ d$`: P`t``@B *!aAJd``@Ł  ` ` ! vl`f~`x~ NA!Z , @@q|` 2  k`efhH RT @ `J , n Tj#b   @6R aN r@tA. ``z -@ @}@Z@bA8 aF @a ` @$AL\,a A1zt`8h \ f~Xj5a JMh<rJBAI~4l&2~x r J&Ft  !A !a&6aX a А@dl vnu@p    r tĚ@XD!4@`@Z@`~Es, `, @`Vš bAAT >@, `~ z N d @Z* d an^4\ ]@ : A&a$m@ aA8 u]` ~ | |aH@B!N``AAL[2 @tts$2b`>az( 2 bsatellite/inst/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt0000644000176200001440000002132613572463522023044 0ustar liggesusersGROUP = L1_METADATA_FILE GROUP = METADATA_FILE_INFO ORIGIN = "Image courtesy of the U.S. Geological Survey" REQUEST_ID = "0501705036310_00016" LANDSAT_SCENE_ID = "LC81950252013188LGN01" LANDSAT_PRODUCT_ID = "LC08_L1TP_195025_20130707_20170503_01_T1" COLLECTION_NUMBER = 01 FILE_DATE = 2017-05-03T12:18:52Z STATION_ID = "LGN" PROCESSING_SOFTWARE_VERSION = "LPGS_2.7.0" END_GROUP = METADATA_FILE_INFO GROUP = PRODUCT_METADATA DATA_TYPE = "L1TP" COLLECTION_CATEGORY = "T1" ELEVATION_SOURCE = "GLS2000" OUTPUT_FORMAT = "GEOTIFF" SPACECRAFT_ID = "LANDSAT_8" SENSOR_ID = "OLI_TIRS" WRS_PATH = 195 WRS_ROW = 25 NADIR_OFFNADIR = "NADIR" TARGET_WRS_PATH = 195 TARGET_WRS_ROW = 25 DATE_ACQUIRED = 2013-07-07 SCENE_CENTER_TIME = "10:17:42.1661960Z" CORNER_UL_LAT_PRODUCT = 51.34342 CORNER_UL_LON_PRODUCT = 7.42064 CORNER_UR_LAT_PRODUCT = 51.34001 CORNER_UR_LON_PRODUCT = 10.81471 CORNER_LL_LAT_PRODUCT = 49.18843 CORNER_LL_LON_PRODUCT = 7.49036 CORNER_LR_LAT_PRODUCT = 49.18527 CORNER_LR_LON_PRODUCT = 10.73461 CORNER_UL_PROJECTION_X_PRODUCT = 390000.000 CORNER_UL_PROJECTION_Y_PRODUCT = 5689200.000 CORNER_UR_PROJECTION_X_PRODUCT = 626400.000 CORNER_UR_PROJECTION_Y_PRODUCT = 5689200.000 CORNER_LL_PROJECTION_X_PRODUCT = 390000.000 CORNER_LL_PROJECTION_Y_PRODUCT = 5449500.000 CORNER_LR_PROJECTION_X_PRODUCT = 626400.000 CORNER_LR_PROJECTION_Y_PRODUCT = 5449500.000 PANCHROMATIC_LINES = 15981 PANCHROMATIC_SAMPLES = 15761 REFLECTIVE_LINES = 7991 REFLECTIVE_SAMPLES = 7881 THERMAL_LINES = 7991 THERMAL_SAMPLES = 7881 FILE_NAME_BAND_1 = "LC08_L1TP_195025_20130707_20170503_01_T1_B1.TIF" FILE_NAME_BAND_2 = "LC08_L1TP_195025_20130707_20170503_01_T1_B2.TIF" FILE_NAME_BAND_3 = "LC08_L1TP_195025_20130707_20170503_01_T1_B3.TIF" FILE_NAME_BAND_4 = "LC08_L1TP_195025_20130707_20170503_01_T1_B4.TIF" FILE_NAME_BAND_5 = "LC08_L1TP_195025_20130707_20170503_01_T1_B5.TIF" FILE_NAME_BAND_6 = "LC08_L1TP_195025_20130707_20170503_01_T1_B6.TIF" FILE_NAME_BAND_7 = "LC08_L1TP_195025_20130707_20170503_01_T1_B7.TIF" FILE_NAME_BAND_8 = "LC08_L1TP_195025_20130707_20170503_01_T1_B8.TIF" FILE_NAME_BAND_9 = "LC08_L1TP_195025_20130707_20170503_01_T1_B9.TIF" FILE_NAME_BAND_10 = "LC08_L1TP_195025_20130707_20170503_01_T1_B10.TIF" FILE_NAME_BAND_11 = "LC08_L1TP_195025_20130707_20170503_01_T1_B11.TIF" FILE_NAME_BAND_QUALITY = "LC08_L1TP_195025_20130707_20170503_01_T1_BQA.TIF" ANGLE_COEFFICIENT_FILE_NAME = "LC08_L1TP_195025_20130707_20170503_01_T1_ANG.txt" METADATA_FILE_NAME = "LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt" CPF_NAME = "LC08CPF_20130701_20130919_01.01" BPF_NAME_OLI = "LO8BPF20130707101045_20130707104320.02" BPF_NAME_TIRS = "LT8BPF20130707100651_20130707104413.01" RLUT_FILE_NAME = "LC08RLUT_20130211_20150302_01_11.h5" END_GROUP = PRODUCT_METADATA GROUP = IMAGE_ATTRIBUTES CLOUD_COVER = 6.03 CLOUD_COVER_LAND = 6.03 IMAGE_QUALITY_OLI = 9 IMAGE_QUALITY_TIRS = 9 TIRS_SSM_MODEL = "ACTUAL" TIRS_SSM_POSITION_STATUS = "NOMINAL" TIRS_STRAY_LIGHT_CORRECTION_SOURCE = "TIRS" ROLL_ANGLE = -0.001 SUN_AZIMUTH = 146.98479703 SUN_ELEVATION = 58.99675180 EARTH_SUN_DISTANCE = 1.0166988 SATURATION_BAND_1 = "Y" SATURATION_BAND_2 = "Y" SATURATION_BAND_3 = "Y" SATURATION_BAND_4 = "Y" SATURATION_BAND_5 = "Y" SATURATION_BAND_6 = "Y" SATURATION_BAND_7 = "Y" SATURATION_BAND_8 = "N" SATURATION_BAND_9 = "N" GROUND_CONTROL_POINTS_VERSION = 4 GROUND_CONTROL_POINTS_MODEL = 391 GEOMETRIC_RMSE_MODEL = 7.053 GEOMETRIC_RMSE_MODEL_Y = 4.332 GEOMETRIC_RMSE_MODEL_X = 5.566 GROUND_CONTROL_POINTS_VERIFY = 144 GEOMETRIC_RMSE_VERIFY = 3.924 TRUNCATION_OLI = "UPPER" END_GROUP = IMAGE_ATTRIBUTES GROUP = MIN_MAX_RADIANCE RADIANCE_MAXIMUM_BAND_1 = 735.30042 RADIANCE_MINIMUM_BAND_1 = -60.72135 RADIANCE_MAXIMUM_BAND_2 = 752.95660 RADIANCE_MINIMUM_BAND_2 = -62.17940 RADIANCE_MAXIMUM_BAND_3 = 693.84302 RADIANCE_MINIMUM_BAND_3 = -57.29778 RADIANCE_MAXIMUM_BAND_4 = 585.08752 RADIANCE_MINIMUM_BAND_4 = -48.31672 RADIANCE_MAXIMUM_BAND_5 = 358.04440 RADIANCE_MINIMUM_BAND_5 = -29.56742 RADIANCE_MAXIMUM_BAND_6 = 89.04239 RADIANCE_MINIMUM_BAND_6 = -7.35315 RADIANCE_MAXIMUM_BAND_7 = 30.01205 RADIANCE_MINIMUM_BAND_7 = -2.47840 RADIANCE_MAXIMUM_BAND_8 = 662.15808 RADIANCE_MINIMUM_BAND_8 = -54.68123 RADIANCE_MAXIMUM_BAND_9 = 139.93187 RADIANCE_MINIMUM_BAND_9 = -11.55562 RADIANCE_MAXIMUM_BAND_10 = 22.00180 RADIANCE_MINIMUM_BAND_10 = 0.10033 RADIANCE_MAXIMUM_BAND_11 = 22.00180 RADIANCE_MINIMUM_BAND_11 = 0.10033 END_GROUP = MIN_MAX_RADIANCE GROUP = MIN_MAX_REFLECTANCE REFLECTANCE_MAXIMUM_BAND_1 = 1.210700 REFLECTANCE_MINIMUM_BAND_1 = -0.099980 REFLECTANCE_MAXIMUM_BAND_2 = 1.210700 REFLECTANCE_MINIMUM_BAND_2 = -0.099980 REFLECTANCE_MAXIMUM_BAND_3 = 1.210700 REFLECTANCE_MINIMUM_BAND_3 = -0.099980 REFLECTANCE_MAXIMUM_BAND_4 = 1.210700 REFLECTANCE_MINIMUM_BAND_4 = -0.099980 REFLECTANCE_MAXIMUM_BAND_5 = 1.210700 REFLECTANCE_MINIMUM_BAND_5 = -0.099980 REFLECTANCE_MAXIMUM_BAND_6 = 1.210700 REFLECTANCE_MINIMUM_BAND_6 = -0.099980 REFLECTANCE_MAXIMUM_BAND_7 = 1.210700 REFLECTANCE_MINIMUM_BAND_7 = -0.099980 REFLECTANCE_MAXIMUM_BAND_8 = 1.210700 REFLECTANCE_MINIMUM_BAND_8 = -0.099980 REFLECTANCE_MAXIMUM_BAND_9 = 1.210700 REFLECTANCE_MINIMUM_BAND_9 = -0.099980 END_GROUP = MIN_MAX_REFLECTANCE GROUP = MIN_MAX_PIXEL_VALUE QUANTIZE_CAL_MAX_BAND_1 = 65535 QUANTIZE_CAL_MIN_BAND_1 = 1 QUANTIZE_CAL_MAX_BAND_2 = 65535 QUANTIZE_CAL_MIN_BAND_2 = 1 QUANTIZE_CAL_MAX_BAND_3 = 65535 QUANTIZE_CAL_MIN_BAND_3 = 1 QUANTIZE_CAL_MAX_BAND_4 = 65535 QUANTIZE_CAL_MIN_BAND_4 = 1 QUANTIZE_CAL_MAX_BAND_5 = 65535 QUANTIZE_CAL_MIN_BAND_5 = 1 QUANTIZE_CAL_MAX_BAND_6 = 65535 QUANTIZE_CAL_MIN_BAND_6 = 1 QUANTIZE_CAL_MAX_BAND_7 = 65535 QUANTIZE_CAL_MIN_BAND_7 = 1 QUANTIZE_CAL_MAX_BAND_8 = 65535 QUANTIZE_CAL_MIN_BAND_8 = 1 QUANTIZE_CAL_MAX_BAND_9 = 65535 QUANTIZE_CAL_MIN_BAND_9 = 1 QUANTIZE_CAL_MAX_BAND_10 = 65535 QUANTIZE_CAL_MIN_BAND_10 = 1 QUANTIZE_CAL_MAX_BAND_11 = 65535 QUANTIZE_CAL_MIN_BAND_11 = 1 END_GROUP = MIN_MAX_PIXEL_VALUE GROUP = RADIOMETRIC_RESCALING RADIANCE_MULT_BAND_1 = 1.2147E-02 RADIANCE_MULT_BAND_2 = 1.2438E-02 RADIANCE_MULT_BAND_3 = 1.1462E-02 RADIANCE_MULT_BAND_4 = 9.6653E-03 RADIANCE_MULT_BAND_5 = 5.9147E-03 RADIANCE_MULT_BAND_6 = 1.4709E-03 RADIANCE_MULT_BAND_7 = 4.9578E-04 RADIANCE_MULT_BAND_8 = 1.0938E-02 RADIANCE_MULT_BAND_9 = 2.3116E-03 RADIANCE_MULT_BAND_10 = 3.3420E-04 RADIANCE_MULT_BAND_11 = 3.3420E-04 RADIANCE_ADD_BAND_1 = -60.73349 RADIANCE_ADD_BAND_2 = -62.19184 RADIANCE_ADD_BAND_3 = -57.30925 RADIANCE_ADD_BAND_4 = -48.32638 RADIANCE_ADD_BAND_5 = -29.57334 RADIANCE_ADD_BAND_6 = -7.35462 RADIANCE_ADD_BAND_7 = -2.47890 RADIANCE_ADD_BAND_8 = -54.69217 RADIANCE_ADD_BAND_9 = -11.55793 RADIANCE_ADD_BAND_10 = 0.10000 RADIANCE_ADD_BAND_11 = 0.10000 REFLECTANCE_MULT_BAND_1 = 2.0000E-05 REFLECTANCE_MULT_BAND_2 = 2.0000E-05 REFLECTANCE_MULT_BAND_3 = 2.0000E-05 REFLECTANCE_MULT_BAND_4 = 2.0000E-05 REFLECTANCE_MULT_BAND_5 = 2.0000E-05 REFLECTANCE_MULT_BAND_6 = 2.0000E-05 REFLECTANCE_MULT_BAND_7 = 2.0000E-05 REFLECTANCE_MULT_BAND_8 = 2.0000E-05 REFLECTANCE_MULT_BAND_9 = 2.0000E-05 REFLECTANCE_ADD_BAND_1 = -0.100000 REFLECTANCE_ADD_BAND_2 = -0.100000 REFLECTANCE_ADD_BAND_3 = -0.100000 REFLECTANCE_ADD_BAND_4 = -0.100000 REFLECTANCE_ADD_BAND_5 = -0.100000 REFLECTANCE_ADD_BAND_6 = -0.100000 REFLECTANCE_ADD_BAND_7 = -0.100000 REFLECTANCE_ADD_BAND_8 = -0.100000 REFLECTANCE_ADD_BAND_9 = -0.100000 END_GROUP = RADIOMETRIC_RESCALING GROUP = TIRS_THERMAL_CONSTANTS K1_CONSTANT_BAND_10 = 774.8853 K2_CONSTANT_BAND_10 = 1321.0789 K1_CONSTANT_BAND_11 = 480.8883 K2_CONSTANT_BAND_11 = 1201.1442 END_GROUP = TIRS_THERMAL_CONSTANTS GROUP = PROJECTION_PARAMETERS MAP_PROJECTION = "UTM" DATUM = "WGS84" ELLIPSOID = "WGS84" UTM_ZONE = 32 GRID_CELL_SIZE_PANCHROMATIC = 15.00 GRID_CELL_SIZE_REFLECTIVE = 30.00 GRID_CELL_SIZE_THERMAL = 30.00 ORIENTATION = "NORTH_UP" RESAMPLING_OPTION = "CUBIC_CONVOLUTION" END_GROUP = PROJECTION_PARAMETERS END_GROUP = L1_METADATA_FILE END satellite/inst/extdata/LE07_L1TP_195025_20010730_20170204_01_T1_B5.TIF0000644000176200001440000000506213572463522022450 0ustar liggesusersII*)))=S   J) 139 70.564544913742 27 14.946118441733 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The satellite class

To start a remote sensing alaysis workflow with satellite you simply use its workhorse function satellite() and point it to a folder where your satellite data is stored.

library(satellite)
path <- system.file("extdata", package = "satellite") 
files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE) # Landsat 8 example data files

sat <- satellite(files)

This will create an object of class satellite with three slots:

  • @layers - a list of RasterLayers of all available bands
  • @meta - a data frame containing all the meta data needed to process the data layers
  • @log - a list containing information about the processing history of the object

For supported satellite platforms all of this is done automatically. At the moment of this writing, supported platforms are Landsat generations 4 to 8. It is, however, very easy to expand this support to other platforms by providing suitable look-up-tables (LUT). Even if no suitable LUT is available, satellite will still import slots @layers and @log.

The @layers slot

As mentioned above, @layers contains a list of RasterLayers of all available bands. The reason for storing the individual bands as RasterLayers rather than a RasterStack is that most satellite platforms provide at least one layer of different spatial resolution that the rest.

str(sat@layers, 1)
## List of 12
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots
##  $ :Formal class 'RasterLayer' [package "raster"] with 12 slots

It is, however, easy to create a RasterStack from selected layers as stack-method is defined for class satellite. By default this will take all layers with the same resolution as the first and stack them. A suitable warning is provided so that the user is informed which layers were not included in the RasterStack. Furthermore, one can simply provide a vector of layer IDs (either layer names or numbers) to be stacked.

## default (all that are similar to layer 1; panchromatic 15-m band 8 is skipped here)
sat_stack <- stack(sat)
## Warning in .local(x, ...): 
## layer B008n has different resolution
## not stacking this layer
sat_stack
## class      : RasterStack 
## dimensions : 41, 41, 1681, 11  (nrow, ncol, ncell, nlayers)
## resolution : 30, 30  (x, y)
## extent     : 483285, 484515, 5627295, 5628525  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : B001n, B002n, B003n, B004n, B005n, B006n, B007n, B009n, B010n, B011n, B0QAn 
## min values :  9827,  8709,  7647,  6600,  8337,  6697,  6013,  5033, 27494, 24874,  2720 
## max values : 15466, 15069, 14143, 15257, 25759, 18589, 14713,  5113, 31926, 27882,  2720
## or by layer names
stack(sat, layer = c("B001n", "B002n", "B003n"))
## class      : RasterStack 
## dimensions : 41, 41, 1681, 3  (nrow, ncol, ncell, nlayers)
## resolution : 30, 30  (x, y)
## extent     : 483285, 484515, 5627295, 5628525  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : B001n, B002n, B003n 
## min values :  9827,  8709,  7647 
## max values : 15466, 15069, 14143
## or by layer indices
stack(sat, layer = 2:6)
## class      : RasterStack 
## dimensions : 41, 41, 1681, 5  (nrow, ncol, ncell, nlayers)
## resolution : 30, 30  (x, y)
## extent     : 483285, 484515, 5627295, 5628525  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : B002n, B003n, B004n, B005n, B006n 
## min values :  8709,  7647,  6600,  8337,  6697 
## max values : 15069, 14143, 15257, 25759, 18589

The @meta slot

The @meta slot provides meta information for each of the layers of the satellite object. Here’s a non-exhaustive list of the most important entries:

  • BCDE - the band code (i.e. layer name)
  • DATE - the date the image was captured
  • SID - a short ID for the sensor (e.g. “LC8”)
  • TYPE - type of the solar spectrum the band falls into (e.g. visible, near-infrared)
  • SPECTRUM - the broad spectrum classification according to shart-wave vs. long-wave radiation (i.e. solar vs thermal)
  • CALIB - the calibration level of the layer (“SC” means source calibration, i.e. as provided by the raw data)

In addition to these, several calibration coefficients (such as the sun zenith and azimuth angles , sun elevation, earth-sun distance etc.), information on spatial resolution and projection as well as information about file names and paths is also stored.

For the Landsat 8 example data shipped with the package the meta data looks like this:

str(sat@meta)
## 'data.frame':    12 obs. of  42 variables:
##  $ SCENE   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ BCDE    : Factor w/ 12 levels "B001n","B002n",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ DATE    : POSIXct, format: "2013-07-07" "2013-07-07" ...
##  $ SID     : Factor w/ 1 level "LC8": 1 1 1 1 1 1 1 1 1 1 ...
##  $ SENSOR  : chr  "Landsat 8" "Landsat 8" "Landsat 8" "Landsat 8" ...
##  $ SGRP    : Factor w/ 1 level "Landsat": 1 1 1 1 1 1 1 1 1 1 ...
##  $ BID     : Factor w/ 12 levels "1","10","11",..: 1 4 5 6 7 8 9 10 11 2 ...
##  $ TYPE    : Factor w/ 6 levels "NIR","PCM","QA",..: 6 6 6 6 1 4 4 2 4 5 ...
##  $ SPECTRUM: Factor w/ 2 levels "solar","thermal": 1 1 1 1 1 1 1 1 1 2 ...
##  $ CALIB   : chr  "SC" "SC" "SC" "SC" ...
##  $ RID.x   : chr  "R00001" "R00001" "R00001" "R00001" ...
##  $ RADA    : num  -60.7 -62.2 -57.3 -48.3 -29.6 ...
##  $ RADM    : num  0.01215 0.01244 0.01146 0.00967 0.00591 ...
##  $ REFA    : num  -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 NA ...
##  $ REFM    : num  2e-05 2e-05 2e-05 2e-05 2e-05 2e-05 2e-05 2e-05 2e-05 NA ...
##  $ BTK1    : num  NA NA NA NA NA ...
##  $ BTK2    : num  NA NA NA NA NA ...
##  $ SZEN    : num  31 31 31 31 31 ...
##  $ SAZM    : num  147 147 147 147 147 ...
##  $ SELV    : num  59 59 59 59 59 ...
##  $ ESD     : num  1.02 1.02 1.02 1.02 1.02 ...
##  $ LMIN    : num  0.43 0.45 0.53 0.64 0.85 1.57 2.11 0.5 1.36 10.6 ...
##  $ LMAX    : num  0.45 0.51 0.59 0.67 0.88 ...
##  $ RADMAX  : num  735 753 694 585 358 ...
##  $ RADMIN  : num  -60.7 -62.2 -57.3 -48.3 -29.6 ...
##  $ REFMAX  : num  1.21 1.21 1.21 1.21 1.21 ...
##  $ REFMIN  : num  -0.1 -0.1 -0.1 -0.1 -0.1 ...
##  $ LNBR    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ LAYER   : chr  "LC08_L1TP_195025_20130707_20170503_01_T1_B1" "LC08_L1TP_195025_20130707_20170503_01_T1_B2" "LC08_L1TP_195025_20130707_20170503_01_T1_B3" "LC08_L1TP_195025_20130707_20170503_01_T1_B4" ...
##  $ FILE    : chr  "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B1.TIF" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B2.TIF" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B3.TIF" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_B4.TIF" ...
##  $ METAFILE: chr  "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt" "C:/Windows/Temp/RtmpYV3Tfx/Rinst347863a540f/satellite/extdata/LC08_L1TP_195025_20130707_20170503_01_T1_MTL.txt" ...
##  $ RID.y   : chr  "R00001" "R00001" "R00001" "R00001" ...
##  $ XRES    : num  30 30 30 30 30 30 30 15 30 30 ...
##  $ YRES    : num  30 30 30 30 30 30 30 15 30 30 ...
##  $ NROW    : int  41 41 41 41 41 41 41 82 41 41 ...
##  $ NCOL    : int  41 41 41 41 41 41 41 82 41 41 ...
##  $ NCELL   : num  1681 1681 1681 1681 1681 ...
##  $ XMIN    : num  483285 483285 483285 483285 483285 ...
##  $ XMAX    : num  484515 484515 484515 484515 484515 ...
##  $ YMIN    : num  5627295 5627295 5627295 5627295 5627295 ...
##  $ YMAX    : num  5628525 5628525 5628525 5628525 5628525 ...
##  $ PROJ    : chr  "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0" "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0" "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0" "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0" ...

Everytime the user performs some calculation on some or all of the layers, this meta information will be updated accordingly. Here’s an example:

## add digital elevation model to existing 'Satellite' object
dem <- raster(system.file("extdata/DEM.TIF", package = "satellite"))
sat <- addSatDataLayer(sat, data = dem, info = NULL, bcde = "DEM", in_bcde = "DEM")

## perform topographic correction
sat_tc <- calcTopoCorr(sat)
tail(sat_tc@meta[, 1:6])
##                  BCDE SCENE       DATE SID    SENSOR    SGRP
## 38 B004n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat
## 39 B005n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat
## 40 B006n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat
## 41 B007n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat
## 42 B008n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat
## 43 B009n_REF_TopoCorr    NA 2019-12-09 LC8 Landsat 8 Landsat

As you can see, all bands have been topographically corrected and the meta data for the resulting layers is automatically appended to the original data frame. Note for example how $DATE is set to the date that layers were calculated.

Note, that in order to avoid too long console output, we only show the first and last six columns and rows, respectively, of the meta data here.

The @log slot

Similar to the meta data, log data is also updated every time an analyis is carried out on the object. The default entries (i.e. the ones created on intial import) are as follows:

sat@log
## $ps0001
## $ps0001$time
## [1] "2019-12-09 08:52:42 CET"
## 
## $ps0001$info
## [1] "Initial import"
## 
## $ps0001$layers
## [1] "all"
## 
## $ps0001$output
## [1] "all"
## 
## 
## $ps0002
## $ps0002$time
## [1] "2019-12-09 08:52:43 CET"
## 
## $ps0002$info
## NULL
## 
## $ps0002$in_bcde
## [1] "DEM"
## 
## $ps0002$out_bcde
## [1] "DEM"

And here’s how this is modified after the topographic correction:

str(sat_tc@log[1:2])
## List of 2
##  $ ps0001:List of 4
##   ..$ time  : POSIXct[1:1], format: "2019-12-09 08:52:42"
##   ..$ info  : chr "Initial import"
##   ..$ layers: chr "all"
##   ..$ output: chr "all"
##  $ ps0002:List of 4
##   ..$ time    : POSIXct[1:1], format: "2019-12-09 08:52:43"
##   ..$ info    : NULL
##   ..$ in_bcde : chr "DEM"
##   ..$ out_bcde: chr "DEM"

Note how, in addition to the info about the initial import, we now have additional logs entries for each band that was topographically corrected clearly showing which call was dispatched, when and on which layer. Even though we only show the first additional log entry here, entries are created for all processed layers. This ensures that we can easily trace what has been done so far and serves as a reference for the current state of processing.