Delineating Urban Boundaries by Integrating Nighttime Light Data and Spectral Indices

Urban boundary delineation is essential for understanding spatial structure, monitoring urbanization, and guiding sustainable land management. Nighttime light (NTL) data effectively capture urban dynamics across multiple spatial scales. This study integrates NTL data with spectral indices to delineate the urban boundaries of the Barcelona Metropolitan Region (BMR) from 2006 to 2018. Through multivariate regression analysis, the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are identified as key indicators of urban spatial heterogeneity. These indices are combined with brightness thresholds derived from three NTL datasets, DMSP-OLS, Black Marble, and VIIRS, to delineate urban areas more accurately. Results indicate that VIIRS achieved the highest precision in identifying construction land and urbanized areas, with an overall accuracy exceeding 90% and consistency with population density and GDP distribution. A strong spatial correlation between urban distribution and the NDVI–NDBI relationship is confirmed in the BMR. The coupling of multisource remote sensing data improves the accuracy, stability, and reliability of urban boundary delineation, overcoming single-source limitations. This integrated method supports urban planning and sustainable land management through consistent, objective urban mapping and offers a practical reference for applying remote sensing technologies to monitor urbanization dynamics across broader spatial and temporal contexts.