Quantifying the relationship between weather and vegetation on a regional scale is a statistical problem which requires long time-series of NDVI. GIMMS is a 25 year (1981-2006) NDVI dataset based on inputs from a sequence of NOAA weather satellites carrying AVHRR radiometers. The GIMMS team removed satellite drift, calibration effects etc.[1] Of course NDVI has a complex spatial variation which is related to variations in vegetation cover type, not to weather variations. To isolate the impact of weather variations, it makes sense to “standardize” NDVI. Vegetation Condition Index (VCI) is:

For example, NDVI_min might refer to the minimum local May NDVI value observed during 1981-2006. A tropical rainforest and a semi-arid region have very different NDVI, but both may have VCI close to 1 during a favourable period.

The maps below show the correlation between VCI and 3-month Standardized Precipitation Index (SPI3) computed for Africa by calendar month. GIMMS 8km NDVI was upscaled to 200km to match the scale of gridded monthly climate datasets for precipitation (GPCP).[2] The red areas correspond to areas of high correlation between VCI and SPI3. Vegetation in these areas responsive to rainfall variations on a 3-month timescale. A striking feature  is the line high correlation in the Sahel region. Vegetation in the Sahel is highly sensitive to drought. Southern Africa is also highly rainfall sensitive during Dec-May. It is impressive that the combination of datasets of very different origins (GPCP and GIMMS) produces spatially and temporally consistent information.

Sensitivity of Vegetation to Precipitation 1981-2006

R code snippets

Most of the effort is in pre-processing the climate and NDVI data so that they share the same projection, grid, time coordinate etc. GDAL and the interp() function from the akima package were used for this purpose. Eventually we end up with SPI3 and VCI data represented by N × T matrices where N = Nx ×Ny is the dimension of the spatial maps and T is the number of observations. From there,  SPI3 and VCI maps were organized by calendar month,

vci.m <- lapply(seq(1:12),function(m) vci[,seq(from=m, to=T, by=12)])
spi3.m <- lapply(seq(1:12), function(m) spi3[,seq(from=m, to=T, by=12)])

Correlation maps between VCI and SPI3  for each calendar month are obtained from,

corr.maps <- lapply(seq(1:12),function(m) sapply(seq(1:N), function(i) cor( vci.m[[m]][i,],spi3.m[[m]][i,]) ))

Finally the above map table was produced using spplot() from the sp package with overlays from the maps package.

[1]  Tucker, C.J., J.E. Pinzon, and M.E. Brown (2004), Global Inventory Modeling and Mapping Studies, NA94apr15b.n11-VIg, 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, 04/15/1994.

[2] SG: Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4,1147-1167.