Spatiotemporal Assessment of Urban Expansion Using Multi-Resolution Remote Sensing:Evidence from Tier-I Urban Growth Centres and the Hyderabad region

Abstract. Urban expansion in India has accelerated significantly over the past two decades, leading to widespread changes in land use and land cover patterns. This study examines the spatiotemporal dynamics of urban expansion and land cover changes from 2001 to 2022 using a dual-resolution geospatial framework. MODIS MCD12Q1 (500 m) data were used for national-scale assessment across twenty Tier- I urban growth centres, while GLC_FCS30D (30 m) data supported high-resolution assessment in the Hyderabad metropolitan region. At the national level, the study revealed a steady increase in built-up areas, often exceeding 30–80% growth across key urban growth centres. Croplands were identified as the primary land category converted into urban use, followed by losses in grasslands and shrublands. The Hyderabad case study demonstrated the limitations of coarse-resolution datasets in detecting fragmented growth and peri-urban development. In contrast, the high-resolution GLC_FCS30D data enabled more detailed mapping of edge expansion, spatial fragmentation, and heterogeneous growth morphology. Unlike prior studies limited to either national or local focus, this work develops a unified, dual-resolution LULC analysis framework with pixel-level transition tracking enables cross-scale insights into urban expansion patterns in India. The integration of both datasets facilitated a comprehensive understanding of urban land changes, combining long-term trend detection with local-level spatial clarity. This approach underscores the importance of resolution–aware methods in urban monitoring and supports evidence–based decision–making in sustainable urban planning, infrastructure development, and land governance. The findings highlight the need for scalable geospatial strategies to address the challenge of rapid urbanization in India, particularly in developing countries undergoing intense land transformation.