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[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].
Wang, Rui-Yan; Yu, Zhen-Wen; Xia, Yan-Ling; Wang, Xiang-Feng; Zhao, Geng-Xing; Jiang, Shu-Qian.
Afiliación
  • Wang RY; College of Agronomy, Shandong Agricultural University, Tai'an 271018, China. wry@sdau.edu.cn
  • Yu ZW; College of Agronomy, Shandong Agricultural University, Tai'an 271018, China.
  • Xia YL; College of Geography and Planning, Ludong University, Yantai 64025, China.
  • Wang XF; Bureau of Land and Resource of Kenli County, Kenli 257500, China.
  • Zhao GX; College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
  • Jiang SQ; College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Guang Pu Xue Yu Guang Pu Fen Xi ; 33(10): 2809-14, 2013 Oct.
Article en Zh | MEDLINE | ID: mdl-24409741
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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Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Ríos / Tecnología de Sensores Remotos Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: Zh Revista: Guang Pu Xue Yu Guang Pu Fen Xi Año: 2013 Tipo del documento: Article País de afiliación: China Pais de publicación: China
Buscar en Google
Añadir a Mi BVS
Imprimir
XML
PubMed Links

Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Ríos / Tecnología de Sensores Remotos Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: Zh Revista: Guang Pu Xue Yu Guang Pu Fen Xi Año: 2013 Tipo del documento: Article País de afiliación: China Pais de publicación: China