Regrowth biomass estimation in the amazon using JERS-1/RADARSAT SAR composites

Synthetic Aperture Radar (SAR) is known to have a response that is directly related to the amount of living material that it interacts with. It is this property that our research seeks to exploit in order to better understand carbon dynamics in the Amazon. The vegetation density causes the radar response to saturate such that vegetation that is more dense than some threshold is indistinguishable from each other. However, the areas of regrowth are likely to have a low enough biomass during the first 10 years of regrowth to be accurately assessed using radar. Our efforts involve obtaining appropriate pairs of radar images at L and C bands from different sites and for both seasons. These data are then orthorectified to allow accurate calibration and incidence angle correction. The seasonality of the data is used to deal with the moisture sensitivity of the data, and the different frequency data is used to help classify the data into several classes for use in class-specific biomass estimates. We have chosen 2 sites in Brazil for our study. we use the JERS-1 (L-band) and RADARSAT (C-band) data to create a 2-channel composite. These data are then classified into the following classes: flat area (water, bare soil), short vegetation, regrowth, and trees. We report on the accuracy of both our classification and biomass estimation efforts.