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Lookup NU author(s): Dr Mark Cutler
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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).
Author(s): Foody GM, Cutler ME, McMorrow J, Pelz D, Tangki H, Boyd DS, Douglas I
Publication type: Article
Publication status: Published
Journal: Global Ecology and Biogeography
ISSN (print): 1466-822X
ISSN (electronic): 1466-8238
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