rasterdiv
How to install?
Stable versions can be installed from the CRAN:
install.packages("rasterdiv")
Development versions stored in the Git repository can be installed with:
library(devtools)
install_github("mattmar/rasterdiv")
What is rasterdiv?
rasterdiv is a package for the R statistical software and environment. It aims to provide functions to apply Information Theory based diversity indexes on RasterLayer or numerical matrices, such as Shannon's entropy or Cumulative Residual Entropy (CRE).
Rasterdiv basics. Derive indices of diversity from NDVI.
Here, we show how to use rasterdiv to derive global series of indices of diversity based on Information Theory. The input dataset is the Copernicus Long-term (1999-2017) average Normalise Difference Vegetation Index for the 21st of June (copNDVI).
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 a more familiar (-1,1) 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.
library(raster)
copNDVI <- reclassify(copNDVI, cbind(252, 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
library(rasterVis)
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")`
Compute all indexes in rasterdiv
rasterdiv allows the computation of 8 diversity indexes based on information theory. In the following section, all these indexes will be computed for copNDVIlr using a moving window of 81 pixels (9 px side). Alpha values for the Hill, Renyi and parametric Rao indexes will be set from 0 to 2 every 0.5. In addition, we will set na.tolerance=0.1, meaning that all moving windows with more than 10% of pixels equal NA will be set to NA.
#Shannon's Diversity
sha <- Shannon(copNDVIlr,window=9,na.tolerance=0.1,np=1)
#Pielou's Evenness
pie <- Pielou(copNDVIlr,window=9,na.tolerance=0.1,np=1)
#Berger-Parker's Index
ber <- BergerParker(copNDVIlr,window=9,na.tolerance=0.1,np=1)
#Rao's quadratic Entropy
rao <- Rao(copNDVIlr,window=9,na.tolerance=0.1,dist_m="euclidean",shannon=FALSE,np=1)
#Parametric Rao's quadratic entropy with alpha ranging from 1 to 5
prao <- paRao(copNDVIlr,window=9,alpha=1:5,na.tolerance=0.1,dist_m="euclidean",np=1)
#Cumulative Residual Entropy
cre <- CRE(copNDVIlr,window=9,na.tolerance=0.1,np=1)
#Hill's numbers
hil <- Hill(copNDVIlr,window=9,alpha=seq(0,2,0.5),na.tolerance=0.1,np=1)
#Renyi's Index
ren <- Renyi(copNDVIlr,window=9,alpha=seq(0,2,0.5),na.tolerance=0.1,np=1)