| Summary: | Impervious surface extraction with high accuracy is important for monitoring urban expansion to sustainably manage the land resources and save the environment. In this context, use of spectral built-up indices has been extensively explored. This study examines the performance of Built-up Area Extraction Index (BAEI), Band Ratio for Built-up Area (BRBA), Modified Built-up Index (MBI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Normalized Difference Built-up Index (NDBI), and Urban Index (UI) in the classification and change detection of impervious surfaces using sentinel-2A MSI imagery. All these maps were classified into built-up and non-built-up areas, and evaluated based on histogram overlap and spectral discrimination index (SDI). Simultaneously, support vector machine (SVM) algorithm was employed to classify the imageries into land-use and land-cover (LULC) classes, viz., bare land, built-up, forest, vegetation, and water bodies. The findings indicate that NBAI, NBI, and NDBI have the highest SDI values of 1.24, 1.23 and 1.54; 1.06, 1.08, and 1.23; 1.16, 1.1 and 1.26 for 2016, 2018, and 2020, respectively. However, other indices show unsatisfactory results. The LULC change between 2016 and 2020 showed that the built-up area, bare land, and water bodies have increased by 9,084.5 ha, 813 ha, and 2 ha, respectively whereas vegetation and forest areas declined by 9,279.3 ha and 620.2 ha, respectively. The classification overall accuracy was 81%, 86%, and 82% for 2016, 2018, and 2020 images, respectively thereby affirming that spectral built-up indices in urban environments can deduce impervious surface extraction quickly and accurately.
|
|---|