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 Landsatoperational 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.

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The accuracy and efficiency of impervious surface extraction using different algorithms vary greatly, and algorithm applicability depends on the study area. Therefore, it is necessary to carry out a comparative study of different algorithms across different study areas. This study compared six impervious surface extraction indices (i.e., normalized difference built-up index (NDBI), index-based built-up index (IBI), biophysical composition index (BCI), combinational build-up index (CBI), combinational biophysical composition index (CBCI), and enhanced normalized difference impervious surfaces index (ENDISI)) using Sentinel-2 imagery in Fuxian Lake Basin, Shenzhen City, and Nanjing City. Three study areas with different geographical locations, climatic conditions and altitudes can test spatial heterogeneity of different indices. The results show that: (1) All indices could be used to extract impervious surface, but BCI and CBI were greatly disturbed by water bodies; (2) CBCI, IBI, and NDBI were influenced by study area, while ENDISI could be used across all three study areas; (3) ENDISI algorithm was the best among the six algorithms with a much higher separability degree and an overall accuracy of more than 91.00%. ENDISI can extract impervious surface quickly and accurately from Sentinel-2 imagery across different study areas, and can be well applied in the field of impervious surface change monitoring.