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

Giles M. Foody, Mark E. Cutler, Julia McMorrow, Dieter Pelz, Hamzah Tangki, Doreen S. Boyd, Ian Douglas

Research output: Contribution to journalArticlepeer-review


The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only practical 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 techniques based on the application of artificial neural networks. Together these approaches were used to estimate and map the biomass of tropical forests in north-eastern Borneo from Lansat TM data. The neural networks were found to be particularly suited to the application. A basic multilayer perceptron network, for example, provided estimates of biomass that were strongly correlated with those measured in the field (r = 0.80). Moreover, these estimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).
Original languageEnglish
Pages (from-to)379-387
Number of pages8
JournalGlobal Ecology and Biogeography
Issue number4
Publication statusPublished - 2001


  • Borneo
  • Land cover change
  • Landsat TM
  • NDVI
  • Neural network
  • Remote sensing
  • Tropical forest biomass


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