Resources and Environmental Carrying Capacity Using RS and GIS

被引:11
作者
Wang, Shi-Xin [1 ]
Shang, Ming [1 ,2 ]
Zhou, Yi [1 ]
Liu, Wen-Liang [1 ]
Wang, Feng [1 ]
Wang, Li-Tao [1 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源
POLISH JOURNAL OF ENVIRONMENTAL STUDIES | 2017年 / 26卷 / 06期
关键词
resources and environmental carrying capacity; remote sensing; mean-variance method; URBAN-DEVELOPMENT; CLASSIFICATION; CHINA; MODEL; LAKES;
D O I
10.15244/pjoes/70927
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Evaluating resources and environmental carrying capacity (RECC) plays an important role in sustainable regional development. Using the urban agglomerations of Beijing, Tianjin, and Hebei Province as examples, in this paper we utilize remote sensing (RS) and geographic information system (GIS) techniques to study RECC. Based on data obtained from statistical information and RS technology, we selected 22 indicators with which to construct an RECC evaluation scheme. Then we conducted a mean-variance analysis to determine the weight of each indicator. Finally, we calculated the RECC of each city in the study area and statistically analyzed the main factors influencing RECC. Our results indicate that: The environment carries the most weight in RECC assessments, followed by resources, economic, and infrastructure In the study area, the RECC ranking is as follows: Beijing, Tianjin, Chengde, Langfang, Qin-huangdao, Cangzhou, Shijiazhuang, Tangshan, Baoding, Zhangjiakou, Hengshui, Handan, Xingtai Geographically, the eastern and central regions have higher RECC than the southern and northeast regions A region's per capita fiscal income is the most important factor affecting its RECC
引用
收藏
页码:2793 / 2800
页数:8
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