Geography

A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery

Document Type

Article

Abstract

Forested ecosystems in California are undergoing accelerated change due to natural and anthropogenic disturbances. Change detection is a remote sensing technique used to monitor and map landcover change between two or more time periods and is now an essential tool in forest management activities. We compared the ability of two linear change enhancement techniques, the Multitemporal Kauth Thomas (MKT) and Multitemporal Spectral Mixture Analysis (MSMA), and two classification techniques, maximum likelihood (ML) and decision tree (DT), to accurately identify changes in vegetation cover in a southern California study area between 1990 and 1996. Supervised classification accuracy results were high (>70% correct classification for four vegetation change classes and one no-change class) and showed that (1) the DT classification approach outperformed the ML classification approach by ∼ 10%, regardless of the enhancement technique used, and (2) using DT classification, MSMA change fractions [i.e., green vegetation (GV), nonphotosynthetic vegetation (NPV), shade, and soil] outperformed MKT change features (i.e., change in brightness, greenness, and wetness) by ∼ 5%. © 2002 Elsevier Science Inc. All rights reserved.

Publication Title

Remote Sensing of Environment

Publication Date

2002

Volume

80

Issue

1

First Page

143

Last Page

156

ISSN

0034-4257

DOI

10.1016/S0034-4257(01)00296-6

Keywords

decision tree classification, remote sensing, spectral mixture analysis, vegetation change

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