Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data
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.
497-499
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Cutler, M.E.
14c202a6-3da4-4e08-a0aa-19eca7aa8489
2002
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Cutler, M.E.
14c202a6-3da4-4e08-a0aa-19eca7aa8489
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.
.
(doi:10.1109/IGARSS.2002.1025085).
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Conference or Workshop Item
(Paper)
Abstract
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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Published date: 2002
Venue - Dates:
Geoscience and Remote Sensing Symposium, IGARSS 2002 IEEE International, Toronto, Canada, 2002-06-24 - 2002-06-28
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Local EPrints ID: 15214
URI: http://eprints.soton.ac.uk/id/eprint/15214
PURE UUID: d8ce6d4f-22f4-4b89-9986-4f4b88945e28
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Date deposited: 30 Mar 2005
Last modified: 15 Mar 2024 05:36
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Author:
G.M. Foody
Author:
M.E. Cutler
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