Comparison modeling for alpine vegetation distribution in an arid area

doi:10.1007/s10661-016-5417-x

CORC > 植物研究所 > 中国科学院植物研究所 > 中科院北方资源植物重点实验室

	Comparison modeling for alpine vegetation distribution in an arid area
	Zhou, Jihua 1; Lai, Liming ; Guan, Tianyu 1; Cai, Wetao 1; Gao, Nannan 1; Zhang, Xiaolong 1; Yang, Dawen 3; Cong, Zhentao 3; Zheng, Yuanrun
刊名	ENVIRONMENTAL MONITORING AND ASSESSMENT
	2016
卷号	188 期号:7
关键词	Classification tree Random forest Landsat8 OLI Spectral vegetation indices Vegetation mapping Qilian Mountains
ISSN号	0167-6369
DOI	10.1007/s10661-016-5417-x
文献子类	Article
英文摘要	Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.
学科主题	Environmental Sciences
电子版国际标准刊号	1573-2959
出版地	DORDRECHT
WOS关键词	RANDOM FOREST CLASSIFICATION ; SPATIAL-DISTRIBUTION ; QILIAN MOUNTAINS ; THEMATIC MAPPER ; UPPER HEIHE ; LANDSAT TM ; VARIABLES ; IMAGERY ; DISCRIMINATION ; INTEGRATION
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000378840300025
资助机构	National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91225302]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/24640]
专题	中科院北方资源植物重点实验室
作者单位	1.Chinese Acad Sci, West China Subalpine Bot Garden, Inst Bot, Key Lab Resource Plants,Beijing Bot Garden, 20 Nanxincun, Beijing 100093, Peoples R China 2.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Jihua,Lai, Liming,Guan, Tianyu,et al. Comparison modeling for alpine vegetation distribution in an arid area[J]. ENVIRONMENTAL MONITORING AND ASSESSMENT,2016,188(7).
APA	Zhou, Jihua.,Lai, Liming.,Guan, Tianyu.,Cai, Wetao.,Gao, Nannan.,...&Zheng, Yuanrun.(2016).Comparison modeling for alpine vegetation distribution in an arid area.ENVIRONMENTAL MONITORING AND ASSESSMENT,188(7).
MLA	Zhou, Jihua,et al."Comparison modeling for alpine vegetation distribution in an arid area".ENVIRONMENTAL MONITORING AND ASSESSMENT 188.7(2016).