Time series analysis with satellite derived greenness indexes (e.g. NDVI) is a powerfull tool to assess environmental processes. AVHRR, MODIS and SPOT VGT provide global and daily imagery. Creating some plots is a simple task, and here is a rough start how it is done with GIMMS NDVI. All we need is the free software R.

I assume we already have the NDVI images in a folder, so the first 3 steps can be skipped, if not, they can be downloaded with a simple command:

wget -r ftp://ftp.glcf.umd.edu/glcf/GIMMS/Albers/Africa/

now unzip it:

find . -name "*.gz" -exec unp {} \;

As it comes in Albers projection, I reproject the Tiffs to a Lat Long WGS84 projection:

for in in *tif; do gdalwarp -s_srs EPSG:9001 -t_srs EPSG:4326 -r near -of GTiff $i /home/martin/Dissertation/GIMMS/GIMMS_lat/`basename $i`; done

I delete all 1981 images, as the year is not complete.

next, we start R, load the raster package and set the directory with the GIMMS Geotiffs:

```
library(raster)
library(rgdal)
setwd("~/GIMMS")
```

now load the Tiffs as a raster brick:

sg = stack(list.files(pattern='*.tif')) gimmsb = brick(sg) rm(sg)

now let’s chose a pixel and create a time series object. It can either be done by Row and Col:

i=20 # row j=23 # col gimms = gimmsb[i,j] gimms.ts = ts(gimms, start=1982, end=c(2006,24), frequency=24)

or by coordinates

xy <- cbind(-3.6249,14.3844) sp <- SpatialPoints(xy) data <- extract(gimmsb, sp) data=as.vector(data) gimms.ts = ts(data, start=1982, end=c(2006,24), frequency=24)

now the time series can be used for further analysis or plotted:

plot(gimms.ts)

A regression line and a LOESS smoothing can be added:

gimms.time = time(gimms.ts) plot(gimms.ts) abline(reg=lm(gimms.ts ~ gimms.time)) lines(lowess(gimms.ts, f=0.3, iter=5), col="blue")

some playing with boxplots and annual values:

layout(1:2) plot(aggregate(gimms.ts)); boxplot(gimms.ts ~ cycle(gimms.ts))

averaged over the sudy area:

g=cellStats(gimmsb, stat='mean') g.ts = ts(g, start=1982, end=c(2006,24), frequency=24) plot(g.ts)

There are great possibilities for time series analysis in R, e.g. the series can be smoothed, decomposed…etc. The zoo package provides further functions and can handle irregular series. See also the lattice package for better visual presentation. How pixelwise analyis is conducted for time series of rasters, I’ll write in another post.