A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery
A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery
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DOI：
10.1016/s0034-4257(01)00296-6
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发表时间：
2002-04-01
影响因子：
13.5
通讯作者：
Roberts, DA
中科院分区：
文献类型：
--
作者：
Rogan, J;Franklin, J;Roberts, DA
由于自然和人为干扰，加州的森林生态系统正在经历加速变化。变化探测是一种遥感技术，用于监测和绘制两个或两个以上时期之间土地覆盖物的变化情况，现在是森林管理活动的一个基本工具。我们比较了两种线性变化增强技术，多时相Kauth托马斯（MKT）和多时相光谱混合分析（MSMA），和两种分类技术，最大似然（NIL）和决策树（DT），准确地识别1990年和1996年之间在南加州研究区植被覆盖的变化的能力。监督的分类准确性结果很高（对于四个植被变化类和一个无变化类，>70%的正确分类），并且表明（1）DT分类方法优于ML分类方法，接近10%，无论使用何种增强技术，以及（2）使用DT分类，MSMA变化分数[即，绿色植被（GV）、非光合植被（NPV）、树荫和土壤]的表现优于MKT变化特征（即，亮度、绿度和湿度的变化）接近5%。(C)2002年爱思唯尔科技有限公司All rights reserved.
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 (NIL) 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 similar to 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 similar to 5%. (C) 2002 Elsevier Science Inc. All rights reserved.