R/rs_indices.R
In gisma/uavRst: Unmanned Aerial Vehicle Remote Sensing Tools
Defines functions
rgb_indices
Documented in
rgb_indices
#' RGB indices
#'
#' @description
#' This function calculates various spectral indices from a RGB. It returns at least red green and blue as splitted channels in a stack. Additionally you can choose RGB indices.
#' \code{Raster*} object.
#'
#' @param red a single \code{Raster} or \code{RasterBrick} band.
#' @param green a single \code{Raster} or \code{RasterBrick} band.
#' @param blue a single \code{Raster} or \code{RasterBrick} band.
#' @param rgbi the implemented RGB indices currently
#' \itemize{
#'\item \code{BI} sqrt((R**2+G**2+B*2)/3 Brightness Index\cr
#'\item \code{SCI} (R-G)/(R+G) Soil Colour Index\cr
#'\item \code{GLI} (2*g - r - b)/(2*g + r + b) Green leaf index Vis Louhaichi et al. (2001)\cr
#'\item \code{HI} (2*R-G-B)/(G-B) Primary colours Hue Index MATHIEU et al. (1998)\cr
#'\item \code{NDTI} (R-G)/(R+G) Normalized difference turbidity index Water Lacaux et al. (2007)\cr
#'\item \code{NGRDI} (G-R)/(G+R) Normalized green red difference index (sometimes GRVI) Tucker (1979)
#'\item \code{RI} R**2/(B*G**3) Redness Index\cr
#'\item \code{SI} (R-B)/(R+B) Spectral Slope Saturation Index\cr
#'\item \code{TGI} -0.5[190(R670-R550)-120(R670 - R480)] The triangular greenness index (TGI) estimates chlorophyll concentration in leaves and canopies\cr
#'\item \code{VARI} (green-red)/(green+red-blue). A Visible Atmospherically Resistant Index (VARI)\cr
#'\item \code{VVI} (1-(r-30)/(r+30))*(1-(g-50)/(g+50))*(1-(b-1)/(b+1))\cr
#'\item \code{GLAI} (25 * (green - red) / (green + red - blue ) + 1.25 )\cr
#'\item \code{GRVI} (green-red)/(green+red) Green-Red Vegetation Index
#'\item \code{CI} (red - blue) / red Coloration Index
#'\item \code{HUE} atan(2 * (red - green - blue) / 30.5 * (green - blue)) Overall Hue Index
#'\item \code{SAT} (max(red,green,blue) - min(red,green,blue)) / max(red,green,blue) Overall Saturation Index
#'\item \code{SHP} 2 * (red - green - blue) / (green - blue) Shape index
#' }
#'
#' @export rgb_indices
#' @return raster* object
#'
#' @references
#'
#' Planetary Habitability Laboratory (2015): Visible Vegetation Index (VVI). Available online via \href{http://phl.upr.edu/projects/visible-vegetation-index-vvi}{VVI}.\cr
#' Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., and Lafaye, M.: Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, Remote Sens. Environ., 106, 66-74, 2007.(NDTI) )\cr
#' Gitelson, A., et al.: Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction. International Journal of Remote Sensing 23 (2002): 2537-2562. (VARI)\cr
#' MADEIRA, J., BEDIDI, A., CERVELLE, B., POUGET, M. and FLAY, N., 1997, Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: 5490 N. Levin et al. the application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil. International Journal of Remote Sensing, 18, pp. 2835-2852.(RI)\cr
#' MATHIEU, R., POUGET, M., CERVELLE, B. and ESCADAFAL, R., 1998, Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil colour of an arid environment. Remote Sensing of Environment, 66, pp. 17-28.(BI, SI, HI) \cr
#' Louhaichi, M., Borman, M.M., Johnson, D.E., 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International 16, 65-70.\cr
#' Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127-150.\cr
#' GRVI Green-Red Vegetation Index Remote Sensing 2010, 2, 2369-2387; doi:10.3390/rs2102369\cr
#' CI \href{https://www.indexdatabase.de/search/?s=color}{IDB Coloration}\cr
#' HUE Index \href{https://www.indexdatabase.de/search/?s=HUE}{IDB Hue}\cr
#' Saturation Index \href{https://www.indexdatabase.de/db/i-single.php?id=77}{IDB Saturation}\cr
#' Shape Index \href{https://www.indexdatabase.de/search/?s=shape}{IDB Shape}\cr
#'
#' @seealso
#' For a comprehensive overview of remote sensing indices have a look at: \href{http://www.indexdatabase.de/db/i.php}{A database for remote sensing indices}\cr
#' Approximately wavelength ranges for overlapping digital camera bands are:
#' \itemize{
#' \item \code{red} 580-670 nm,
#' \item \code{green} 480-610 nm,
#' \item \code{blue} 400-520 nm
#'}
#' \href{http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=2161&context=usdaarsfacpub}{Hunt et al., 2005}. However check the manual of your camera.
#'
#' @examples
#' ## ## ##
#'##- setup environment
#'data(rgb)
#'
#'##- visualize the image
#'raster::plotRGB(rgb)
#'
#'##- calculate the indices
#'rgbI<-rgb_indices(red = rgb[[1]],
#' green = rgb[[2]],
#' blue = rgb[[3]],
#' rgbi = c("NDTI","VARI","TGI"))
#'
#'##- visualize the indices
#'raster::plot(rgbI)
#'##+
rgb_indices <- function(red,green,blue,rgbi=c("VVI","VARI","NDTI","RI","SCI","BI",
"SI","HI",
"TGI","GLI",
"NGRDI","GRVI",
"GLAI","HUE",
"CI","SAT","SHP")) {
## compatibility check
# if (raster::nlayers(rgb) < 3)
# stop("Argument 'rgb' needs to be a Raster* object with at least 3 layers (usually red, green and blue).")
### processing
## separate visible bands
# red <- raster::raster(rgb[[1]])
# green <- raster::raster(rgb[[2]])
# blue <- raster::raster(rgb[[3]])
indices <- lapply(rgbi, function(item){
## calculate Visible Vegetation Index vvi
if (item == "VVI"){
message(getCrayon()[[3]](":::: Visible Vegetation Index ",item,"\n"))
#message("\n calculate Visible Vegetation Index (VVI)")
VVI <- (1 - abs((red - 30) / (red + 30))) *
(1 - abs((green - 50) / (green + 50))) *
(1 - abs((blue - 1) / (blue + 1)))
names(VVI) <- "VVI"
return(VVI)
} else if (item == "VARI") {
# calculate Visible Atmospherically Resistant Index (VARI)
message(getCrayon()[[3]](":::: Visible Atmospherically Resistant Index ",item,"\n"))
#message("\n calculate Visible Atmospherically Resistant Index (VARI)")
VARI <- (green - red) / (green + red - blue)
names(VARI) <- "VARI"
return(VARI)
} else if (item == "NDTI") {
## Normalized difference turbidity index
message(getCrayon()[[3]](":::: Normalized Difference Turbidity Index ",item,"\n"))
#message("\n calculate Normalized difference turbidity index (NDTI)")
NDTI <- (red - green) / (red + green)
names(NDTI) <- "NDTI"
return(NDTI)
GLAI
} else if (item == "RI") {
# redness index
message(getCrayon()[[3]](":::: Redness Index ",item,"\n"))
#message("\n calculate redness index (RI)")
RI <- red**2 / (blue*green**3)
names(RI) <- "RI"
return(RI)
} else if (item == "SCI") {
# SCI Soil Colour Index
message(getCrayon()[[3]](":::: Soil Colour Index ",item,"\n"))
#message("\n calculate Soil Colour Index (SCI)")
SCI <- (red - green) / (red + green)
names(SCI) <- "SCI"
return(SCI)
} else if (item == "BI") {
# Brightness Index
message(getCrayon()[[3]](":::: Brightness Index ",item,"\n"))
#message("\n calculate Brightness Index (BI)")
BI <- sqrt((red**2 + green**2 + blue*2) / 3)
names(BI) <- "BI"
return(BI)
} else if (item == "SI") {
# SI Spectra Slope Saturation Index
message(getCrayon()[[3]](":::: Spectra Slope Saturation Index ",item,"\n"))
#message("\n calculate Spectra Slope Saturation Index (SI)")
SI <- (red - blue) / (red + blue)
names(SI) <- "SI"
return(SI)
} else if (item=="HI"){
# HI Primary colours Hue Index
message(getCrayon()[[3]](":::: Primary Colours Hue Index ",item,"\n"))
#message("\n calculate Primary colours Hue Index (HI)")
HI<-(2*red-green-blue)/(green-blue)
names(HI) <- "HI"
return(HI)
} else if (item=="TGI"){
# Triangular greenness index
message(getCrayon()[[3]](":::: Triangular Greenness Index ",item,"\n"))
#message("\n calculate Triangular greenness index (TGI)")
TGI <- -0.5*(190*(red - green)- 120*(red - blue))
names(TGI) <- "TGI"
return(TGI)
} else if (item=="GLI"){
#message("\n calculate Green leaf index (GLI)")
message(getCrayon()[[3]](":::: Green Leaf Index ",item,"\n"))
# Green leaf index
GLI<-(2*green-red-blue)/(2*green+red+blue)
names(GLI) <- "GLI"
return(GLI)
} else if (item=="NGRDI"){
# NGRDI Normalized green red difference index
message(getCrayon()[[3]](":::: Normalized Green-Red Difference Index ",item,"\n"))
#message("\n calculate Normalized green red difference index (NGRDI)")
NGRDI<-(green-red)/(green+red)
names(NGRDI) <- "NGRDI"
return(NGRDI)
} else if (item=="GLAI"){
# NGRDI Normalized green red difference index
message(getCrayon()[[3]](":::: Greenish Leaf Area Index ",item,"\n"))
#message("\n calculate greenish Leaf Area Index (GLAI) (highly experimental)")
# vevi<-(green - red) / (green + red - blue )
GLAI = (25 * ((green - red) / (green + red - blue )) + 1.25 )
names(GLAI) <- "GLAI"
return(GLAI)
} else if (item=="GRVI"){
# GRVI Green-Red Vegetation Index Remote Sensing 2010, 2, 2369-2387; doi:10.3390/rs2102369
message(getCrayon()[[3]](":::: Green-Red Vegetation Index ",item,"\n"))
#message("\n calculate Green-Red Vegetation Index (GRVI)")
GRVI<-(green-red)/(green+red)
names(GRVI) <- "GRVI"
return(GRVI)
} else if (item == "CI") {
# CI https://www.indexdatabase.de/search/?s=color
message(getCrayon()[[3]](":::: Coloration Index ",item,"\n"))
# message("\n calculate Coloration Index (CI)")
CI <- (red - blue) / red
names(CI) <- "CI"
return(CI)
} else if (item == "HUE") {
# HUE Index https://www.indexdatabase.de/search/?s=HUE
message(getCrayon()[[3]](":::: Hue Index ",item,"\n"))
#message("\n calculate Hue Index (HUE)")
HUE <- atan(2 * (red - green - blue) / 30.5 * (green - blue))
names(HUE) <- "HUE"
return(HUE)
} else if (item == "SAT") {
# Saturation Index https://www.indexdatabase.de/db/i-single.php?id=77
message(getCrayon()[[3]](":::: Saturation Index ",item,"\n"))
#message("\n calculate Saturation Index (SAT)")
SAT <- (max(red,green,blue) - min(red,green,blue)) / max(red,green,blue)
names(SAT) <- "SAT"
return(SAT)
} else if (item == "SHP") {
# Shape Index https://www.indexdatabase.de/search/?s=shape
message(getCrayon()[[3]](":::: Shape Index ",item,"\n"))
#message("\n calculate Shape Index (SHP)")
SHP <- 2 * (red - green - blue) / (green - blue)
names(SHP) <- "SHP"
return(SHP)
}
})
return(c(indices))
}
gisma/uavRst documentation built on Feb. 14, 2023, 8:49 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions rgb_indices
Documented in rgb_indices
#' RGB indices
#'
#' @description
#' This function calculates various spectral indices from a RGB. It returns at least red green and blue as splitted channels in a stack. Additionally you can choose RGB indices.
#' \code{Raster*} object.
#'
#' @param red a single \code{Raster} or \code{RasterBrick} band.
#' @param green a single \code{Raster} or \code{RasterBrick} band.
#' @param blue a single \code{Raster} or \code{RasterBrick} band.
#' @param rgbi the implemented RGB indices currently
#' \itemize{
#'\item \code{BI} sqrt((R**2+G**2+B*2)/3 Brightness Index\cr
#'\item \code{SCI} (R-G)/(R+G) Soil Colour Index\cr
#'\item \code{GLI} (2*g - r - b)/(2*g + r + b) Green leaf index Vis Louhaichi et al. (2001)\cr
#'\item \code{HI} (2*R-G-B)/(G-B) Primary colours Hue Index MATHIEU et al. (1998)\cr
#'\item \code{NDTI} (R-G)/(R+G) Normalized difference turbidity index Water Lacaux et al. (2007)\cr
#'\item \code{NGRDI} (G-R)/(G+R) Normalized green red difference index (sometimes GRVI) Tucker (1979)
#'\item \code{RI} R**2/(B*G**3) Redness Index\cr
#'\item \code{SI} (R-B)/(R+B) Spectral Slope Saturation Index\cr
#'\item \code{TGI} -0.5[190(R670-R550)-120(R670 - R480)] The triangular greenness index (TGI) estimates chlorophyll concentration in leaves and canopies\cr
#'\item \code{VARI} (green-red)/(green+red-blue). A Visible Atmospherically Resistant Index (VARI)\cr
#'\item \code{VVI} (1-(r-30)/(r+30))*(1-(g-50)/(g+50))*(1-(b-1)/(b+1))\cr
#'\item \code{GLAI} (25 * (green - red) / (green + red - blue ) + 1.25 )\cr
#'\item \code{GRVI} (green-red)/(green+red) Green-Red Vegetation Index
#'\item \code{CI} (red - blue) / red Coloration Index
#'\item \code{HUE} atan(2 * (red - green - blue) / 30.5 * (green - blue)) Overall Hue Index
#'\item \code{SAT} (max(red,green,blue) - min(red,green,blue)) / max(red,green,blue) Overall Saturation Index
#'\item \code{SHP} 2 * (red - green - blue) / (green - blue) Shape index
#' }
#'
#' @export rgb_indices
#' @return raster* object
#'
#' @references
#'
#' Planetary Habitability Laboratory (2015): Visible Vegetation Index (VVI). Available online via \href{http://phl.upr.edu/projects/visible-vegetation-index-vvi}{VVI}.\cr
#' Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., and Lafaye, M.: Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, Remote Sens. Environ., 106, 66-74, 2007.(NDTI) )\cr
#' Gitelson, A., et al.: Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction. International Journal of Remote Sensing 23 (2002): 2537-2562. (VARI)\cr
#' MADEIRA, J., BEDIDI, A., CERVELLE, B., POUGET, M. and FLAY, N., 1997, Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: 5490 N. Levin et al. the application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil. International Journal of Remote Sensing, 18, pp. 2835-2852.(RI)\cr
#' MATHIEU, R., POUGET, M., CERVELLE, B. and ESCADAFAL, R., 1998, Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil colour of an arid environment. Remote Sensing of Environment, 66, pp. 17-28.(BI, SI, HI) \cr
#' Louhaichi, M., Borman, M.M., Johnson, D.E., 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International 16, 65-70.\cr
#' Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127-150.\cr
#' GRVI Green-Red Vegetation Index Remote Sensing 2010, 2, 2369-2387; doi:10.3390/rs2102369\cr
#' CI \href{https://www.indexdatabase.de/search/?s=color}{IDB Coloration}\cr
#' HUE Index \href{https://www.indexdatabase.de/search/?s=HUE}{IDB Hue}\cr
#' Saturation Index \href{https://www.indexdatabase.de/db/i-single.php?id=77}{IDB Saturation}\cr
#' Shape Index \href{https://www.indexdatabase.de/search/?s=shape}{IDB Shape}\cr
#'
#' @seealso
#' For a comprehensive overview of remote sensing indices have a look at: \href{http://www.indexdatabase.de/db/i.php}{A database for remote sensing indices}\cr
#' Approximately wavelength ranges for overlapping digital camera bands are:
#' \itemize{
#' \item \code{red} 580-670 nm,
#' \item \code{green} 480-610 nm,
#' \item \code{blue} 400-520 nm
#'}
#' \href{http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=2161&context=usdaarsfacpub}{Hunt et al., 2005}. However check the manual of your camera.
#'
#' @examples
#' ## ## ##
#'##- setup environment
#'data(rgb)
#'
#'##- visualize the image
#'raster::plotRGB(rgb)
#'
#'##- calculate the indices
#'rgbI<-rgb_indices(red = rgb[[1]],
#' green = rgb[[2]],
#' blue = rgb[[3]],
#' rgbi = c("NDTI","VARI","TGI"))
#'
#'##- visualize the indices
#'raster::plot(rgbI)
#'##+
rgb_indices <- function(red,green,blue,rgbi=c("VVI","VARI","NDTI","RI","SCI","BI",
"SI","HI",
"TGI","GLI",
"NGRDI","GRVI",
"GLAI","HUE",
"CI","SAT","SHP")) {
## compatibility check
# if (raster::nlayers(rgb) < 3)
# stop("Argument 'rgb' needs to be a Raster* object with at least 3 layers (usually red, green and blue).")
### processing
## separate visible bands
# red <- raster::raster(rgb[[1]])
# green <- raster::raster(rgb[[2]])
# blue <- raster::raster(rgb[[3]])
indices <- lapply(rgbi, function(item){
## calculate Visible Vegetation Index vvi
if (item == "VVI"){
message(getCrayon()[[3]](":::: Visible Vegetation Index ",item,"\n"))
#message("\n calculate Visible Vegetation Index (VVI)")
VVI <- (1 - abs((red - 30) / (red + 30))) *
(1 - abs((green - 50) / (green + 50))) *
(1 - abs((blue - 1) / (blue + 1)))
names(VVI) <- "VVI"
return(VVI)
} else if (item == "VARI") {
# calculate Visible Atmospherically Resistant Index (VARI)
message(getCrayon()[[3]](":::: Visible Atmospherically Resistant Index ",item,"\n"))
#message("\n calculate Visible Atmospherically Resistant Index (VARI)")
VARI <- (green - red) / (green + red - blue)
names(VARI) <- "VARI"
return(VARI)
} else if (item == "NDTI") {
## Normalized difference turbidity index
message(getCrayon()[[3]](":::: Normalized Difference Turbidity Index ",item,"\n"))
#message("\n calculate Normalized difference turbidity index (NDTI)")
NDTI <- (red - green) / (red + green)
names(NDTI) <- "NDTI"
return(NDTI)
GLAI
} else if (item == "RI") {
# redness index
message(getCrayon()[[3]](":::: Redness Index ",item,"\n"))
#message("\n calculate redness index (RI)")
RI <- red**2 / (blue*green**3)
names(RI) <- "RI"
return(RI)
} else if (item == "SCI") {
# SCI Soil Colour Index
message(getCrayon()[[3]](":::: Soil Colour Index ",item,"\n"))
#message("\n calculate Soil Colour Index (SCI)")
SCI <- (red - green) / (red + green)
names(SCI) <- "SCI"
return(SCI)
} else if (item == "BI") {
# Brightness Index
message(getCrayon()[[3]](":::: Brightness Index ",item,"\n"))
#message("\n calculate Brightness Index (BI)")
BI <- sqrt((red**2 + green**2 + blue*2) / 3)
names(BI) <- "BI"
return(BI)
} else if (item == "SI") {
# SI Spectra Slope Saturation Index
message(getCrayon()[[3]](":::: Spectra Slope Saturation Index ",item,"\n"))
#message("\n calculate Spectra Slope Saturation Index (SI)")
SI <- (red - blue) / (red + blue)
names(SI) <- "SI"
return(SI)
} else if (item=="HI"){
# HI Primary colours Hue Index
message(getCrayon()[[3]](":::: Primary Colours Hue Index ",item,"\n"))
#message("\n calculate Primary colours Hue Index (HI)")
HI<-(2*red-green-blue)/(green-blue)
names(HI) <- "HI"
return(HI)
} else if (item=="TGI"){
# Triangular greenness index
message(getCrayon()[[3]](":::: Triangular Greenness Index ",item,"\n"))
#message("\n calculate Triangular greenness index (TGI)")
TGI <- -0.5*(190*(red - green)- 120*(red - blue))
names(TGI) <- "TGI"
return(TGI)
} else if (item=="GLI"){
#message("\n calculate Green leaf index (GLI)")
message(getCrayon()[[3]](":::: Green Leaf Index ",item,"\n"))
# Green leaf index
GLI<-(2*green-red-blue)/(2*green+red+blue)
names(GLI) <- "GLI"
return(GLI)
} else if (item=="NGRDI"){
# NGRDI Normalized green red difference index
message(getCrayon()[[3]](":::: Normalized Green-Red Difference Index ",item,"\n"))
#message("\n calculate Normalized green red difference index (NGRDI)")
NGRDI<-(green-red)/(green+red)
names(NGRDI) <- "NGRDI"
return(NGRDI)
} else if (item=="GLAI"){
# NGRDI Normalized green red difference index
message(getCrayon()[[3]](":::: Greenish Leaf Area Index ",item,"\n"))
#message("\n calculate greenish Leaf Area Index (GLAI) (highly experimental)")
# vevi<-(green - red) / (green + red - blue )
GLAI = (25 * ((green - red) / (green + red - blue )) + 1.25 )
names(GLAI) <- "GLAI"
return(GLAI)
} else if (item=="GRVI"){
# GRVI Green-Red Vegetation Index Remote Sensing 2010, 2, 2369-2387; doi:10.3390/rs2102369
message(getCrayon()[[3]](":::: Green-Red Vegetation Index ",item,"\n"))
#message("\n calculate Green-Red Vegetation Index (GRVI)")
GRVI<-(green-red)/(green+red)
names(GRVI) <- "GRVI"
return(GRVI)
} else if (item == "CI") {
# CI https://www.indexdatabase.de/search/?s=color
message(getCrayon()[[3]](":::: Coloration Index ",item,"\n"))
# message("\n calculate Coloration Index (CI)")
CI <- (red - blue) / red
names(CI) <- "CI"
return(CI)
} else if (item == "HUE") {
# HUE Index https://www.indexdatabase.de/search/?s=HUE
message(getCrayon()[[3]](":::: Hue Index ",item,"\n"))
#message("\n calculate Hue Index (HUE)")
HUE <- atan(2 * (red - green - blue) / 30.5 * (green - blue))
names(HUE) <- "HUE"
return(HUE)
} else if (item == "SAT") {
# Saturation Index https://www.indexdatabase.de/db/i-single.php?id=77
message(getCrayon()[[3]](":::: Saturation Index ",item,"\n"))
#message("\n calculate Saturation Index (SAT)")
SAT <- (max(red,green,blue) - min(red,green,blue)) / max(red,green,blue)
names(SAT) <- "SAT"
return(SAT)
} else if (item == "SHP") {
# Shape Index https://www.indexdatabase.de/search/?s=shape
message(getCrayon()[[3]](":::: Shape Index ",item,"\n"))
#message("\n calculate Shape Index (SHP)")
SHP <- 2 * (red - green - blue) / (green - blue)
names(SHP) <- "SHP"
return(SHP)
}
})
return(c(indices))
}
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