Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt

doi:10.1371/journal.pone.0238165

	Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt
	Zhang, Junyao 1,2; Yao, Yonghui 2; Suo, Nandongzhu 1,2
刊名	PLOS ONE
	2020-08-25
卷号	15 期号:8 页码:25
ISSN号	1932-6203
DOI	10.1371/journal.pone.0238165
通讯作者	Yao, Yonghui(yaoyh@lreis.ac.cn)
英文摘要	Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.
资助项目	National Natural Science Foundation of China[41871350] ; National Natural Science Foundation of China[41571099]
WOS关键词	REMOTE-SENSING IMAGES ; LAND-COVER ; TAIBAI MOUNTAIN ; POPULATION-STRUCTURE ; RANDOM FOREST ; AREA ; IDENTIFICATION ; VALIDATION ; VARIANCE ; ACCURACY
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000565553400015
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/157928]
专题	中国科学院地理科学与资源研究所
通讯作者	Yao, Yonghui
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Skate Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Junyao,Yao, Yonghui,Suo, Nandongzhu. Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt[J]. PLOS ONE,2020,15(8):25.
APA	Zhang, Junyao,Yao, Yonghui,&Suo, Nandongzhu.(2020).Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt.PLOS ONE,15(8),25.
MLA	Zhang, Junyao,et al."Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt".PLOS ONE 15.8(2020):25.