Spectral and temporal linear mixing model for vegetation classification
Abstract:The objective of this study is to find a better method for sub-pixel classification of vegetation. The proposed new technique of a linear mixing model (LMM) is the sequential combination of spectral LMM and temporal LMM. Sub-pixel components of 'relative green vegetation' are derived by spectral LMM; sub-pixel components of vegetation types are estimated by subsequent temporal LMM. The proposed method was applied to five temporal Landsat Enhanced Thematic Mapper (ETM) images for the year 2000 for areas south of Lake Baikal, Russia. Dominant vegetation types there are pine, birch/aspen, shrubs and wheat with weedy plants. Ground truth data of vegetation types were prepared by field survey and visual interpretation of Landsat ETM images by experts. Both the comparisons of classification results among the proposed method and conventional LMM methods and the simulation results among them indicate that the proposed spectral and temporal LMM has better accuracy than conventional methods.
Document Type: Research Article
Affiliations: 1: Center for Environmental Remote Sensing (CEReS) Chiba University 1-33 Yayoi-cho, Inage-ku Chiba 263-8522 Japan, Email: email@example.com 2: Pasco Corporation No. 1-2, Higashiyama 1-chome, Meguro-ku Tokyo 153-0043 Japan, Email: firstname.lastname@example.org 3: Mongolian Complex Biological Expedition, Institute for Ecology and Evolution Russian Academy of Sciences Pyatnitskaja str. 47, building 3 109017 Moscow Russia, Email: email@example.com
Publication date: October 1, 2004