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.

Acknowledgments

The authors would like to thank members of the Mongolian Complex Biological Expedition of the Institute for Ecology and Evolution, Russian Academy of Sciences for their assistance and cooperation with the field survey and vegetation interpretation. Also, the authors' appreciation is extended to Dr Park Jong Geol of Tokyo Information University for his cooperation in the field survey and his advice regarding this study. The authors also thank the anonymous reviewers for their valuable comments to improve this paper.