NASA Science News posted a fascinating piece about the use of satellite imagery in detecting tornado damage tracks.

When I was an undergraduate research assistant at the University of Wisconsin – Madison about a decade ago, I examined similar infrared satellite imagery for indications of drought. As we all learned in biology class, leaves maintain crucial range of moisture levels and temperatures in their interior largely by controlling water flux through their outermost few layers of cells. Tornadoes batter vegetation and shred leaves into tiny pieces, exposing their moist interiors. (I never saw the rain-wrapped 10 May 2010 Tecumseh, OK tornado pass by a few miles to my south, but shredded leaves rained down out of the sky for several minutes after it passed. I only see that kind of vegetation lofting in the immediate aftermath of a tornado.) If the plant isn’t killed outright, it becomes “stressed”, changing its reflectance characteristics at different wavelengths.

Healthy vegetation reflects strongly in the near-infrared wavelengths (around 0.9 microns), but stressed vegetation reflects weakly. In addition, stressed vegetation lights up in a band centered around 1.9 microns, as this diagram from NASA shows:

A logical “signature” for stressed vegetation, therefore, might be R(0.9 microns) – R(1.9 microns). When this value is positive, the vegetation can be inferred to be healthy. When it’s negative, indications are that the vegetation is stressed. This is likely not the exact formula used by the ASTER researchers; I just made that up on the spot. A robust index would be based on examination of many different wavelengths, different vegetation types, and over many seasons. (Obviously, this is not my area of expertise any longer!) One might be able to determine an optimal stressed vegetation indicator using a statistical technique like principal component analysis (PCA).

I remember seeing similar satellite images of the 3 May 1999 tornado damage swath through Moore. This paper, from Dr. May Yuan of the OU Department of Geography, contains prime examples. Neat stuff.