The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis.

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities.