| 光谱学与光谱分析 |
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| Mapping Environmental Vulnerability from ETM + Data in the Yellow River Mouth Area |
| WANG Rui-yan1, 2, 3, YU Zhen-wen1*, XIA Yan-ling4, WANG Xiang-feng5, ZHAO Geng-xing2, JIANG Shu-qian2 |
1. College of Agronomy，Shandong Agricultural University，Tai’an 271018, China
2. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018，China
3. National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources，Tai’an 271018，China
4. College of Geography and Planning，Ludong University，Yantai 264025，China
5. Bureau of Land and Resource of Kenli County，Kenli 257500，China |
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Abstract The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.
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Received: 2012-12-21
Accepted: 2013-03-09
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Corresponding Authors:
YU Zhen-wen
E-mail: yuzw@sdau.edu.cn
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[1] Hinkel J. Global Environmental Change, 2011, 21: 198.
[2] Lahsen M, Sanchez-Rodriguez R, Lankao P R, et al. Current Opinion in Environmental Sustainability, 2010, 2: 364.
[3] Ministry of Environmental Protection of the People’s Republic of China(中国环境保护部), National Ecotone Rrotection Planning Framework(全国生态脆弱区保护规划纲要), 2008, 9
[4] Villa F, McLeod H. Environmental Management, 2002, 29: 335.
[5] Kvarner J, Swensen G, Erikstad, L. Environmental Impact Assessment Review, 2006, 26: 511.
[6] Skondras N A, Karavitis C A, Gkotsis I I. Ecological Indicators, 2011, 11: 1699.
[7] Tran L T, O’Neill R V, Smith E R. Environmental Impact Assessment Review, 2012, 34: 58.
[8] Young O R. Global Environmental Change, 2010, 20: 378.
[9] Navas M, Telfer T C, Ross L G. Marine Pollution Bulletin, 2011, 62: 1786.
[10] Kaly U L, Briguglio L, McLeod H, et al. SOPAC Technical Report 275, 1999.
[11] Schnur M T, Hongjie Xie, Xianwei Wang. Ecological Informatics, 2010, 5: 400.
[12] Chen Chi-Farn, Son Nguyen-Thanh, Chang Li-Yu, et al. Applied Geography, 2011, 31: 463.
[13] Guerschman J P, Michael J. Hill et al. Remote Sens. Environ., 2009, 113: 928.
[14] LIU Liang-yun, WANG Ji-hua, HUANG Wen-jiang, et al(刘良云, 王纪华, 黄文江, 等). Transactions of the Chinese Society of Agri-cultural Engineering(农业工程学报), 2004, 20: 172.
[15] LI Hua, ZENG Yong-nian, YUN Pei-dong, et al(历 华, 曾永年, 贠培东, 等). Journal of Remote Sensing(遥感学报), 2007, 11: 891.
[16] ZHOU Peng, DING Jian-li, WANG Fei, et al(周 鹏, 丁建丽, 王 飞, 等). Journal of Remote Sensing(遥感学报), 2010, 14: 959.
[17] LIU Zheng-jia, YU Xing-xiu, LI Lei, et al (刘正佳, 于兴修, 李 蕾, 等). Chinese Journal of Applied Ecology(应用生态学报), 2011, 22: 2084.
[18] Evan D G Fraser, Mabee W, Slaymaker O. Global Environmental Change, 2003, 3: 137. |
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