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).