Tropical forest biomass density estimation using JERS-1 SAR: Seasonal variation, confidence limits, and application to image mosaics

Adrian Luckman, John Baker, Miroslav Honzák, Richard Lucas

Research output: Contribution to journalArticlepeer-review

134 Citations (SciVal)

Abstract

This study describes the development of a semiempirical model for the retrieval of above-ground biomass density of regenerating tropical forest using JERS-1 Synthetic Aperture Radar (SAR). The magnitude and variability of the response of the L-band SAR to above-ground biomass density was quantified using field data collected at Tapajós in central Amazonia 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 Tapajós. The sources of variability in biomass density and SAR backscatter were investigated so as to determine confidence limits for the subsequent retrieval of biomass density using the model. This analysis suggested that only three broad classes of regenerating forest biomass density may be positively distinguished. While the backscatter appears to saturate at around 60 tonnes per hectare, the biomass limit for retrieval purposes which is tolerant to both speckle and image texture is only 31 tonnes per hectare. The spatial distribution of biomass density in central 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 regeneration derived from NOAA AVHRR imagery suggested that L-band SAR will provide a useful method of monitoring tropical forests on a regional scale.
Original languageEnglish
Pages (from-to)126-139
Number of pages14
JournalRemote Sensing of Environment
Volume63
Issue number2
DOIs
Publication statusPublished - 01 Feb 1998
Externally publishedYes

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