There is an increasing need to understand pattern and growth of impervious surfaces in rural regions. However, studies using remote sensing of impervious surfaces have often focused on mapping impervious surfaces in urban regions with less emphasis placed on the rural impervious surfaces. In this paper, we proposed a new index, Rural Impervious Surface Index (RISI) by taking advantage of narrow spectral bands of Landsat 8 OLI for estimating impervious surfaces within rural land covers. This index is based on the combination of Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI) and Soil Index (SI). Respectively, these represent the three major rural land covers components: impervious surfaces, vegetation, and soil. The index was further used for estimating fraction of impervious surfaces using fuzzy KNN classifier. The performance of this technique was also compared with Linear Spectral Mixture Analysis (LSMA). Our results showed that RISI could accurately detect spatial pattern of rural impervious surfaces due to the suppressing background noise and minimizing spectral confusion. Accuracy assessment revealed that incorporation of RISI with fuzzy KNN classification generates higher correlation coefficient, lower root mean square and systematic error compared to the LSMA technique.

Date Published: 8 November 2014
PDF: 12 pages
Proc. SPIE 9260, Land Surface Remote Sensing II, 926030 (8 November 2014); doi: 10.1117/12.2068807

Published in SPIE Proceedings Vol. 9260:
Land Surface Remote Sensing II
Thomas J. Jackson; Jing Ming Chen; Peng Gong; Shunlin Liang, Editor(s)