Mapping the biomass of Bornean tropical rain forest from remotely sensed data

The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only pr actical means of acquiring information on forest biomass but has not always been used successfully. Here the conventional approaches to the estimation of forest biomass from remotely sensed data were evaluated relative to tec hniques based on the application of artificial neural networks. Together th ese approaches were used to estimate and map the biomass of tropical forest s in north-eastern Borneo from Landsat TM data. The neural networks were fo und to be particularly suited to the application. A basic multi-layer perce ptron network, for example, provided estimates of biomass that were strongl y correlated with those measured in the field (r = 0.80). Moreover, these e stimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).