期刊
REMOTE SENSING
卷 9, 期 4, 页码 -出版社
MDPI AG
DOI: 10.3390/rs9040375
关键词
impervious surface area; normalized impervious surface index; support vector regression; DMSP-OLS; MODIS NDVI; Landsat
类别
资金
- Zhejiang Agriculture and Forestry University's Research and Development Fund [2013FR052]
Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) data have been used for ISA mapping, but high uncertainty existed due to mixed-pixel and data-saturation problems. This paper presents a new index called normalized impervious surface index (NISI), which is an integration of DMSP-OLS and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, in order to reduce these problems. Meanwhile, this newly developed index is compared with previously used indices-Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI)-in ISA mapping performance. We selected China as an example to map fractional ISA distribution through a support vector regression approach based on the relationship between the index and Landsat-derived ISA data. The results indicate that the proposed NISI provided better ISA estimation accuracy than HSI and VANUI, especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e., <0.2). This approach can be used to rapidly update ISA datasets at regional and global scales.
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