TROPICAL FOREST BIOMASS DENSITY-ESTIMATION USING JERS-1 SAR - SEASONAL-VARIATION, CONFIDENCE-LIMITS, AND APPLICATION TO IMAGE MOSAICS

This study describes the development of a semiempirical model for the retrieval of above-ground biomass density of regenerating tropical for est using JERS-1 Synthetic Aperture Radar (SAR). The magnitude and var iability of the response of the L-band SAR to above-ground biomass den sity was quantified using field data collected at Tapajos in central A mazonia and imagery from a series of dates. A simple backscatter model was fitted to this response and validated using image and field data acquired independently at Manaus, 500 km to the west of Tapajos. The s ources of variability in biomass density and SAR back-scatter were inv estigated so as to determine confidence limits for the subsequent retr ieval of biomass density using the model. This analysis suggested that only three broad classes of regenerating forest biomass density may b e positively distinguished. While the backscatter appears to saturate at around 60 tonnes per hectare, the biomass limit for retrieval purpo ses which is tolerant to both speckle and image texture is only 31 ton nes per hectare. The spatial distribution of biomass density in centra l Amazonia was estimated by applying the model to a mosaic of 90 JERS- 1 images. A favorable comparison of this distribution to a map of rege neration derived from NOAA AVHRR imagery suggested that L-band SAR wil l provide a useful method of monitoring tropical forests on a regional scale. (C) Elsevier Science Inc., 1998.