The use of remote sensing to estimate biomass in tropical forests has met with varying degrees of success. Previous work has demonstrated that reliable estimates of biomass can be made with Artificial Neural Networks, using data from single sites and with a degree of transferability between other tropical forests. Here JERS-1 data for three sites is combined with Landsat TM images, with the aim of improving the spatial transferability of the method. The image data were compared to ground data from sites in Malaysia, Brazil and Thailand using a feed-forward artificial neural network. The SAR data were weakly correlated with biomass, and when combined with optical data led to a marginal increase in the correlation coefficient for each site individually. However, improved relationships were observed when combined SAR/optical data for all sites were presented to the network, thus indicating marginally improved transferability in the method compared to using optical data alone.
|Title of host publication||Sustaining the Millennium Development Goals|
|Subtitle of host publication||33rd International symposium on remote sensing of environment|
|Place of Publication||Tucson|
|Publisher||International Center for Remote Sensing of Environment,|
|Number of pages||4|
|ISBN (Print)||093291313X, 9780932913135|
|Publication status||Published - 2009|
|Event||33rd International Symposium on Remote Sensing of Environment, ISRSE 2009 - Stresa, Italy|
Duration: 4 May 2009 → 8 May 2009
|Name||Proceedings of the International Symposium on Remote Sensing of Environment|
|Conference||33rd International Symposium on Remote Sensing of Environment, ISRSE 2009|
|Period||4/05/09 → 8/05/09|
Cutler, M. E. J., Foody, G. M., & Boyd, D. S. (2009). An estimation of tropical forest biomass with a combination of JERS-1 and Landsat TM data. In Sustaining the Millennium Development Goals: 33rd International symposium on remote sensing of environment (pp. 262-265). ( Proceedings of the International Symposium on Remote Sensing of Environment; Vol. 33). International Center for Remote Sensing of Environment,.