National Aeronautics and Space Administration

National Climate Assessment

NASA National Climate Assessment (NCA) Activities

NASA Indicators Solicitation Proposals

Development and Testing of Potential Indicators for the National Climate Assessment

Lead PI and Center: Steven Running, University of Montana
Title: Translating EOS Datasets Into National Ecosystem Biophysical Indicators

In 2007, while on the IGBP Science Committee, I originated the idea of building what became the IGBP Climate Change Index as a means to translate the raw scientific data into a more palatable and accurate but simplified metric for the public and policy makers. In the last 5 years I have spend alot of time thinking about how best to do this. Concurrently I have been evaluating what variables are both of climate significance, as a forcing or an impact, and how good are the datasets that represent each one.  I think since NASA is the leader of developing new biophysical geospatial datasets, that we should prototype national indicator maps, and see how well they are accepted by the larger National Assessment community. So I propose to use some of our existing MODIS land datasets to build test maps. I have found maps of biophysical data are most easily interpreted when they are a relative anomaly, or departure from "normal" rather than some absolute measure in awkward or unfamiliar units. A well built anomaly map would implicitly define normal as the 0 point, with departures above or below normal that are deemed significant colored in clearly opposing colors. The width of the 0 point implies non-significant variability.

So I propose to use the ongoing 12+ year MODIS record to build anomaly maps in identical format for annual:
Spectral vegetation index anomaly, NDVI or EVI
Growing season length
Snowcover duration
% forest disturbed by fire, insect epidemics or other mortality

It is important to evaluate each year, and each pixel on the basis of "what would be normal". Now that we have 12yr of MODIS record we can compute a 12 average for each pixel/variable, then in forward processing measure the current year against this normal. The audience then can easily understand if the current year is above or below average for each metric.