TY - JOUR
T1 - Tropical forest biomass density estimation using JERS-1 SAR: Seasonal variation, confidence limits, and application to image mosaics
AU - Luckman, Adrian
AU - Baker, John
AU - Honzák, Miroslav
AU - Lucas, Richard
N1 - Funding Information:
This study was funded by the U.K. Natural Environment Research Council as part of the Terrestrial Initiative in Global Environment Research (TIGER). Fieldwork was carried out in collaboration with the Instituto Nacional de Pesquisas Espaciais (INPE—the Brazilian Space Agency), the Instituto Nacional de Pesquisas da Amazônia (INPA—Brazil's Amazon Research Institute), Sheffield University, and the NERC ABRACOS Project. Essential assistance was given in the field by the Instituto Brasileiro do Meio Ambiente (IBAMA—Brazil's environmental and renewable resources agency), the Superintendência do Desenvolvimento da Amazônia (SUDAM—Brazil's agency for the development of the Amazon), and the Smithsonian Institute. Landsat Images used for calibrating the AVHRR classification were provided through INPE by Thelma Krug and Silvana Amaral. JERS-1 images and mosaics were provided by NASDA. We are indebted to many people for their help in this study, not least those who helped in the ground data collection at Tapajós and Manaus. These include Bruce Nelson, Ieda do Amaral, Corina Yanasse, Tatiana Mora Kuplich, Pedro Hernandez Filho, Geoff Groom, Saira Luckman, and Kevin Grover.
PY - 1998/2/1
Y1 - 1998/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0031988534&partnerID=8YFLogxK
U2 - 10.1016/S0034-4257(97)00133-8
DO - 10.1016/S0034-4257(97)00133-8
M3 - Article
SN - 0034-4257
VL - 63
SP - 126
EP - 139
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 2
ER -