Remote sensing of biodiversity: using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data

Two types of neural network were used to derive measures of biodiversity from Landsat TM data of a tropical rainforest. A feedforward neural network was used to estimate species richness while a Kohonen neural network was used to provide information on species composition. The results indicate the potential of remote sensing as a source of maps of biodiversity.