中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 植物研究所
  3. 中国科学院植物研究所
  4. 中科院北方资源植物重点实验室
Comparison modeling for alpine vegetation distribution in an arid area

文献类型：期刊论文

作者Zhou, Jihua2; Lai, Liming; Guan, Tianyu2; Cai, Wetao2; Gao, Nannan2; Zhang, Xiaolong2; Yang, Dawen1; Cong, Zhentao1; Zheng, Yuanrun
刊名ENVIRONMENTAL MONITORING AND ASSESSMENT
出版日期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
DOI10.1007/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
出版地DORDRECHT
电子版国际标准刊号1573-2959
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
WOS记录号WOS:000378840300025
出版者SPRINGER
资助机构National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91225302]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/25288]  
专题中科院北方资源植物重点实验室
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, West China Subalpine Bot Garden, Inst Bot, Key Lab Resource Plants,Beijing Bot Garden, 20 Nanxincun, Beijing 100093, Peoples R China
3.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, 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).

入库方式： OAI收割

来源：植物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。