An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

Quantitative and accurate urban land information on regional global scales is urgently required for studying socioeconomic eco-environmental problems. The spatial distribution of a significant part development planning, which vital optimizing use patterns promoting sustainable development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective extracting land. However, saturation blooming within DMSP-OLS NTL hinder its capacity provide information. This paper proposes an optimized approach that combines with multiple index overcome limitations based only data. We combined three sources data, DMSP-OLS, normalized difference vegetation (NDVI), water (NDWI), establish novel called vegetation–water-adjusted (VWANUI), used rapidly extract areas scales. results show proposed reduces essentially eliminates effects. Next, we developed regression models human settlement (HSI), vegetation-adjusted (VANUI), VWANUI analyze estimate areas. model provides highest performance all tested. To summarize, blooming, improves accuracy are extracted, thereby providing valuable support decision-making references designing