  We aim to identify the spatial distribution of vegetation and its growth
dynamics with the purpose of obtaining a qualitative assessment of vegetation
health and land degradation. To do so, we compare a statistical model of
vegetation growth and real-world Earth observation data. Specifically, we
analyze a stochastic cellular automata model and data obtained from satellite
images, namely using the Normalized Difference Vegetation Index (NDVI) and the
Leaf Area Index (LAI). In the experimental data, we look for areas where
vegetation is broken into small patches and qualitatively compare it to the
percolating, fragmented, and degraded states that appear in the cellular
automata model. We model the periodic effect of seasons, finding numerical
evidence of a periodic fragmentation and recovery phenomenology if the model
parameters are sufficiently close to the model's percolation transition. We
qualitatively recognize these effects in real-world vegetation images and
consider them a signal of increased environmental stress and vulnerability.
Finally, we show an estimation of the environmental stress in land images by
considering both the vegetation density and its clusterization.
