Geography

Application of spectral and environmental variables to map the Kissimmee Prairie ecosystem using classification trees

Document Type

Article

Abstract

This paper compares a variety of classification tree-based approaches to map 10 vegetation cover classes and a single built-up class in the Kissimmee Prairie Ecosystem, an endangered grass-shrubland landscape in south-central Florida (USA). This comparison is provided to identify an effective and replicable mapping methodology and facilitate the ongoing regional-scale management and monitoring of grass-shrubland ecosystems. Results showed that the best-performing models included environmental variables, due to the ability of these variables to help distinguish spectrally similar classes. The highest overall proportional accuracy of 81% was the result of incorporating linear spectral mixture analysis and geo-environmental variables into the classification tree.

Publication Title

GIScience and Remote Sensing

Publication Date

2011

Volume

48

Issue

3

First Page

299

Last Page

323

ISSN

1548-1603

DOI

10.2747/1548-1603.48.3.299

Keywords

environmental effect, grassland, mapping, multispectral image, prairie, shrubland, vegetation cover

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