Rasterdiv basics. Derive indices of diversity from NDVI.

Overview

A RasterLayer called copNDVI is loaded together with package rasterdiv. copNDVI is at 8-bit meaning that pixel values range from 0 to 255. You could stretch it to match a more familiar (-1,1) values range using raster::stretch(copNDVI,minv=-1,maxv=1) .

Reclassify NDVI

Pixels with values 253, 254 and 255 (water) will be set as NA’s.

copNDVI <- reclassify(copNDVI, cbind(253, 255, NA), right=TRUE)

Resample NDVI to coarser resolution

To speed up the calculation, the RasterLayer will be “resampled” at a resolution 20 times coarser than original.

#Resample using raster::aggregate and a linear factor of 10
copNDVIlr <- raster::aggregate(copNDVI, fact=20)
#Set float numbers as integers to further speed up the calculation
storage.mode(copNDVIlr[]) = "integer"

Compare NDVI low and high resolution

levelplot(copNDVI,layout=c(0,1,1), main="NDVI 21st of June 1999-2017 - ~8km pixel resolution")

levelplot(copNDVIlr,layout=c(0,1,1), main="NDVI 21st of June 1999-2017 - ~150km pixel resolution")

Derive Hill’s and Renyi’s indices

Renyi and Hill’s indices are calculated from copNDVI with a moving window of 81 pixels (9 px side) for alpha values from 0 to 2 every 0.5. In addition, we set na.tolerance=0.5, meaning that pixels whose 9x9 px window has more than 50% of NA values will be set to NA.

#Shannon's Diversity
sha <- Shannon(copNDVIlr,window=9,np=1,na.tolerance=0.1)

#Pielou's Evenness
pie <- Pielou(copNDVIlr,window=9,np=1,na.tolerance=0.1)

#Berger-Parker
ber <- BergerParker(copNDVIlr,window=9,np=1,na.tolerance=0.1)

#Rao's quadratic Entropy
rao <- Rao(copNDVIlr,window=9,np=1,na.tolerance=0.1,dist_m="euclidean",shannon=FALSE)

#Cumulative Residual Entropy
cre <- CRE(copNDVIlr,window=9,np=1,na.tolerance=0.1)

#Hill's numbers
hil <- Hill(copNDVIlr,window=9,np=1,na.tolerance=0.1,alpha=seq(0,2,0.5))

#Renyi
ren <- Renyi(copNDVIlr,window=9,np=1,na.tolerance=0.1,alpha=seq(0,2,0.5))

Transform output matrices to RasterLayer

All functions in rasterdiv output numerical matrices or list of numerical matrices. Therefore, they need to be placed in space to be transformed in RasterLayer. This is done by using the input NDVI RasterLayer as template.

shara <- raster(sha,template=copNDVIlr)
piera <- raster(pie,template=copNDVIlr)
berra <- raster(ber,template=copNDVIlr)
raora <- raster(rao,template=copNDVIlr)
crera <- raster(cre,template=copNDVIlr)
hilra <- lapply(hil, function(x) raster(x,template=copNDVIlr))
renra <- lapply(ren, function(x) raster(x,template=copNDVIlr))

Visualise RasterLayers

#Shannon's Diversity
levelplot(shara,main="Shannon's entropy from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,1,1), ylim=c(-60,75), margin = list(draw = TRUE))

#Pielou's Evenness
levelplot(piera,main="Pielou's evenness from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,1,1), ylim=c(-60,75), margin = list(draw = TRUE))

#Berger-Parker' Index
levelplot(berra,main="Berger-Parker's index from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,1,1), ylim=c(-60,75), margin = list(draw = TRUE))

#Rao's quadratic Entropy
levelplot(crera,main="Rao's quadratic entropy from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,1,1), ylim=c(-60,75), margin = list(draw = TRUE))

#Cumulative Residual Entropy
levelplot(crera,main="Cumulative Resiudal Entropy from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,1,1), ylim=c(-60,75), margin = list(draw = TRUE))

#Hill's numbers (alpha=0, 1, 1.5 and 2)
levelplot(stack(hilra),main="Hill's numbers from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,5,1),names.attr=paste("alpha",seq(0,2,0.5),sep=" "), ylim=c(-60,75))

#Renyi' Index (alpha=0, 1, 1.5 and 2)
levelplot(stack(renra),main="Renyi's entropy from Copernicus NDVI 5 km (9 px-side moving window)",as.table = T,layout=c(0,5,1),names.attr=paste("alpha",seq(0,2,0.5),sep=" "), ylim=c(-60,75), margin = list(draw = FALSE))