The Study of Quantitative Assessment of Regional Eco-environmental Vulnerability Based on Multi-source Remote Sensing

被引:4
|
作者
Wu Xu [1 ,2 ,5 ]
He Binbin [1 ]
Kan Aike [3 ,4 ,5 ]
Cirenluobu [5 ]
Yang Xiao [3 ,5 ]
机构
[1] Univ Elect Sci & Technol China, Coll Resources & Environm, 2006 XiYuan Ave, Chengdu, Sichuan, Peoples R China
[2] Chengdu Univ Technol, Coll Informat Sci & Technol, Chengdu, Sichuan, Peoples R China
[3] Chengdu Univ Technol, Coll Geophys, Chengdu, Sichuan, Peoples R China
[4] Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, 11 Datun Rd, Beijing, Peoples R China
[5] Inst Informat Sci & Technol Tibet Autonomous Reg, 19 West Beijing Rd, Lhasa, Peoples R China
关键词
D O I
10.1088/1755-1315/94/1/012141
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
How to estimate vulnerability of eco-environment quickly and accurately is an important research to predict the trend of environmental change in the future. Based on the analysis of the previous methods of eco-environment assessment, we tried to build a quantitative assessment model of eco-environment vulnerability by multi-source remote sensing data. The model focuses on extracting the change information of vegetation and land cover types from remote sensing (RS) data, and reveals the law of eco-environment vulnerability change. In the process of building the model, the correlation between normalized difference vegetation index (NDVI) and topographic data was analysed. The nonlinear regression method was used to estimating vegetation coverage taken as one of the main parameters of the model. And then, the model was applied to a specific study area. The quantitative assessment used Multi temporal data obtained to calculate the vulnerability values. It described the spatial distribution and variation characteristics of eco-environmental vulnerability in this region. We also estimate the accuracy and stability of the model. The calculation results show that we can quickly evaluate regional eco-environmental vulnerability and improve the efficiency of ecological environment monitoring through our proposed model.
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收藏
页数:11
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