STUDY ON FOREST VEGETATION CLASSIFICATION BASED ON MULTI-TEMPORAL REMOTE SENSING IMAGES

摘要

It is very difficult to classify forest vegetation in mountain areas because of the impact of complex terrain. A new method, classification of forest vegetation based on multi-temporal remote sensing, is proposed in this paper. The forest vegetation could get better classification precision by avoiding the interactions of different plants with multi-temporal images. So it enhanced the separability of coniferous forest and broadleaf forest. The classification result showed that the accuracy could be greatly improved by using multi-temporal remote sensing images. The overall accuracy and kappa coefficient were 81.3% and 0.72, respectively. So the method delivered in this essay has obviously technological advantages and important application potentiality in forest vegetation classification.