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

Giles M. Foody; Mark E. Cutler

doi:10.1109/IGARSS.2002.1025085

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

Giles M. Foody, Mark E. Cutler

Research output: Contribution to conference › Paper › peer-review

6 Citations (Scopus)

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.

Original language	English
Pages	497-499
Number of pages	3
DOIs	https://doi.org/10.1109/IGARSS.2002.1025085
Publication status	Published - 1 Jan 2002
Event	2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) - Toronto, Ont., Canada Duration: 24 Jun 2002 → 28 Jun 2002

Conference

Conference	2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)
Country/Territory	Canada
City	Toronto, Ont.
Period	24/06/02 → 28/06/02

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

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10.1109/IGARSS.2002.1025085Licence: No Licence / Unknown

https://ieeexplore.ieee.org/document/1025085Licence: No Licence / Unknown

Cite this

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

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.",

author = "Foody, {Giles M.} and Cutler, {Mark E.}",

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day = "1",

doi = "10.1109/IGARSS.2002.1025085",

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Foody, GM & Cutler, ME 2002, 'Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data', Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada, 24/06/02 - 28/06/02 pp. 497-499. https://doi.org/10.1109/IGARSS.2002.1025085

Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data. / Foody, Giles M.; Cutler, Mark E.
2002. 497-499 Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada.

Research output: Contribution to conference › Paper › peer-review

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AU - Foody, Giles M.

AU - Cutler, Mark E.

PY - 2002/1/1

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N2 - 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.

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

Abstract

Conference

ASJC Scopus subject areas

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