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Evolution of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

Evolution of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing
Evolution of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing
The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.
coniferous forest cover, land cover transformation, multiple regression, neural networks, Pacific Northwest, remote sensing, vegetation indices
0143-6228
375-392
Boyd, D.S.
cc3e74df-9587-4328-a591-f67144fffa82
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Ripple, W.J.
bd9e5405-44f1-4f31-b474-e80961fd9ab7
Boyd, D.S.
cc3e74df-9587-4328-a591-f67144fffa82
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Ripple, W.J.
bd9e5405-44f1-4f31-b474-e80961fd9ab7

Boyd, D.S., Foody, G.M. and Ripple, W.J. (2002) Evolution of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Applied Geography, 22 (4), 375-392. (doi:10.1016/S0143-6228(02)00048-6).

Record type: Article

Abstract

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.

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More information

Published date: October 2002
Keywords: coniferous forest cover, land cover transformation, multiple regression, neural networks, Pacific Northwest, remote sensing, vegetation indices

Identifiers

Local EPrints ID: 58742
URI: http://eprints.soton.ac.uk/id/eprint/58742
ISSN: 0143-6228
PURE UUID: 2ac33f96-7f6c-4708-9c7f-7fda74bd1758

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Date deposited: 15 Aug 2008
Last modified: 13 Mar 2019 20:31

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