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

Record type: Conference or Workshop Item (Paper)

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.

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Citation

Foody, G.M. and Cutler, M.E. (2002) Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data In Papers in proceedings of the IGARSS '02 conference. IEEE., pp. 497-499. (doi:10.1109/IGARSS.2002.1025085).

More information

Published date: 2002
Venue - Dates: Geoscience and Remote Sensing Symposium, IGARSS 2002 IEEE International, 2002-06-24 - 2002-06-28

Identifiers

Local EPrints ID: 15214
URI: http://eprints.soton.ac.uk/id/eprint/15214
PURE UUID: d8ce6d4f-22f4-4b89-9986-4f4b88945e28

Catalogue record

Date deposited: 30 Mar 2005
Last modified: 17 Jul 2017 16:50

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Contributors

Author: G.M. Foody
Author: M.E. Cutler

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