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 conferencePaper

5 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 languageEnglish
Pages497-499
Number of pages3
DOIs
Publication statusPublished - 1 Jan 2002
Event2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) - Toronto, Ont., Canada
Duration: 24 Jun 200228 Jun 2002

Conference

Conference2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)
CountryCanada
CityToronto, Ont.
Period24/06/0228/06/02

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    Foody, G. M., & 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. 497-499. Paper presented at 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, Ont., Canada. https://doi.org/10.1109/IGARSS.2002.1025085