A comparative study of impervious surface extraction using Sentinel-2 imagery. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Chen, Junyi; Chen, Suozhong; Yang, Chao; He, Liang; Hou, Manqing; Shi, Tiezhu
• Is Part Of: European journal of remote sensing. Volume 53:Issue 1(2020)
• Page Start: 274
• Page End: 292
• Publication Date: 2020-01-01
• Subjects: Enhanced normalized difference impervious surfaces index (ENDISI) -- impervious surface area (ISA) -- Sentinel-2 imagery -- IBI -- NDBI -- BCI -- CBI -- CBCI; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2020.1820383
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: 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.