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

2017 3rd International Conference on Energy, Environment and Materials Science

能源学;生态环境科学;材料科学

Xu, Wu^1,2,5 ; Binbin, He^1 ; Aike, Kan^3,4,5 ; Cirenluobu^5 ; Xiao, Yang^3,5

College of Resources and Environment, University of Electronic Science and Technology of China, No.2006 XiYuan Avenue, Chengdu, China^1

College of Information Science and Technology, Chengdu University of Technology, Dongsanlu, Erxianqiao, Chengdu, China^2

College of Geophysics, Chengdu University of Technology, Dongsanlu, Erxianqiao, Chengdu, China^3

State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, No.11 Datun Road, Beijing, China^4

Institute of Information Science and Technology of Tibet Autonomous Region, No.19 West Beijing Road, Lhasa, China^5

关键词: Calculation results;    Ecological environments;    Environmental change;    Multi-temporal data;    Non-linear regression method;    Normalized difference vegetation index;    Quantitative assessments;    Variation characteristics;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/94/1/012141/pdf
DOI  :  10.1088/1755-1315/94/1/012141

来源: IOP

【 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.