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

被引：0

作者：

Jing, Xia ^{[2
]}

Wang, JiHua ^{[1
]}

Huang, WenJiang ^{[1
]}

Liu, LiangYun ^{[1
]}

Wang, JinDi ^{[2
]}

机构：

[1] Natl Engn Res Ctr Informat Technol Agr, POB 2449-26, Beijing 100097, Peoples R China

[2] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

来源：

COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE II, VOL 1 | 2009年 / 293卷

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

multi-temporal; remote sensing; forest vegetation; classification;

D O I：

暂无

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

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 broad leaf 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.

引用

页码：115 / +

页数：2

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