Abstract
Impervious surface (IS) area is an important indicator of ecological environment condition in the basin. We propose an index for IS extraction [i.e., enhanced normalized difference impervious surfaces index (ENDISI)] by integrating the spectrum character of Landsat-operational land imager (OLI) images, and an automatic threshold selection method using the generalized Gaussian model. Dianchi and Erhai Basin are employed as study areas to test the ENDISI method at the plateau basin scale. The results show that: (1) the ENDISI can reduce the impacts of arid land, bare rock, and bare soil on IS extraction effectively; (2) ENDISI had a much higher separability degree between ISs and pervious surfaces compared with normalized difference built-up index, modified normalized difference IS index, and combinational biophysical composition index; and (3) the overall accuracy and kappa coefficient values of IS extraction via automatic threshold selection exceed 93.9% and 82.4%, respectively. Therefore, the ENDISI can serve as an effective index algorithm for rapid and high-precision IS extraction at the plateau basin scale.
Highlights
Impervious surfaces (ISs) have constantly expanded with significant effects on urban natural and human environments through rapid urbanization
The enhanced normalized difference impervious surfaces index (ENDISI) results show that the values for waterbodies, shaded surfaces, and forest are very low, most of which fall below −0.25
Bright and dark impervious surface area (ISA) present higher ENDISI values in plateau basins, and they can be separated from bare soil and arid land areas
Summary
Impervious surfaces (ISs) have constantly expanded with significant effects on urban natural and human environments through rapid urbanization. The V-I-S model has been widely used in many studies.[29,30] the method is affected by the selection of endmembers, rendering the results generated more uncertain.[31] Some index methods (e.g., the NDBI, CBI, and BCI) involve masking out water bodies or bare soil before extracting ISA. Yang et al.[35] analyzed the spectral radiance of ISs and bare soil, obtained a normalized difference bare soil index (ρ) for the Dianchi basin, and facilitated the extraction of IS cover by eliminating most bare soils and vegetation using the INDBI algorithm. To highlight the four-major urban biophysical compositions (i.e., ISs, bare soil, vegetation, and water), a CBCI was proposed by Zhang.[19] Compared with NDBI, CBI, CBI, and NDVI, the CBCI has the significant correlation with IS and vegetation at different spatial resolution images, especially for high-resolution imagery. The CBCI cannot be applied to the region of Plateau Basin effectively, and there are still a lot of bare soil and arid land in the extraction results
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