Hi -
I was fascinated when I first read this post, and wanted to know more about this technology -- how to monitor vegitation growth from satellite data -- so I Googled it and found this explanation:
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The Normalized Difference Vegetation Index (NDVI) has been in use for many years to measure and monitor plant growth (vigor), vegetation cover, and biomass production from multispectral satellite data. The NDVI image maps shown here are prepared from 1-km AVHRR spectral data in the visible (Channel 1; 0.58-0.68 micrometers) and near infrared (Channel 2; 0.725-1.10 micrometers) regions of the electromagnetic spectrum. NDVI is calculated as follows:
NDVI = (Channel 2 - Channel 1) / (Channel 2 + Channel 1)
The principle behind NDVI is that Channel 1 is in the red-light region of the electromagnetic spectrum where chlorophyll causes considerable absorption of incoming sunlight, whereas Channel 2 is in the near-infrared region of the spectrum where a plant's spongy mesophyll leaf structure creates considerable reflectance (Tucker 1979, Jackson et al.1983, Tucker et al. 1991). As a result, vigorously growing healthy vegetation has low red-light reflectance and high near-infrared reflectance, and hence, high NDVI values. This relatively simply algorithm produces output values in the range of -1.0 to 1.0. Increasing positive NDVI values, shown in increasing shades of green on the images, indicate increasing amounts of green vegetation. NDVI values near zero and decreasing negative values indicate non-vegetated features such as barren surfaces (rock and soil) and water, snow, ice, and clouds.
(Taken from:<http://edc.usgs.gov/greenness/whatndvi.html>)
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In the example worked by and presented by forestriver of vegitative growth in the Dominican Republic over the years 1999-2006, he presented plots of change for just one time period, which I assume was a comparison of 1999 vs 2006. He could have calculated the NDVI for each single year change, giving seven such maps. This opens the analysis to interpreting HOW the changes are occurring over time, and could involve multidimensional smoothing to arrive at the final conclusions.
An alternate way to look at the seven resulting maps would be to morph each transition to the succeding year and run all of the morphs serially. You would then see the changes as smooth transitions throughout the seven year period. This is perfectly possible with some of the morphing software. The results may have to be viewed multiple times mine the information on changes, but this may produce clean results.
This was enough to satisfy
my curiosity, but if you want more information, just click
here Fascinating stuff!
hale
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