PROJECT TITLE :

Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas

ABSTRACT:

The distribution of urban impervious surface could be a vital indicator of the degree of urbanization, along with a serious indicator of environmental quality. Hence, cashing in on remotely sensed imagery to map impervious surface has become an important topic. Spectral indices are developed due to its convenience to use, among which feature extraction approach has shown superiority in reliability and applicability. However, impervious surface is usually confused with bare soil when the current existing indices are used with their sensor-specific limitations. In this study, a brand new index, combinational build-up index (CBI), is proposed to extract impervious surface. The new index combines the first part of a principal component analysis (PC1), normalized distinction water index (NDWI), and soil-adjusted vegetation index (SAVI), representing high albedo, low albedo, and vegetation, respectively, to cut back the original bands into 3 thematic-oriented options. The new index was tested using varied remote sensing pictures at different spectral and spatial resolutions. Qualitative and quantitative assessments of the accuracy and separability of CBI, along with the comparison with different existing indices, were performed. The result of this study indicates that the proposed technique is able to serve as a good impervious index and will be applied widely.