Classification and Change detection of built-up lands using remote sensing images

this study examines and compares the performance of seven spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. The Study site with an area of 68,995 hectares in this study in the Tehran It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, ,one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI) and visible blue/ mid-infrared (SWIR1) based built-up indices (VbSWIR1). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu’s method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 3500 reference points for each image The results show that the VbSWIR1-BI, with an overall accuracy of 92.88% (Landsat-7) and 91.68% (Landsat-8), were more robust and superior. The results also show indications that the detected spatiotemporal urban LULC changes based on the VbSWIR1-BI were also the most accurate.