PREDICTIVE MODELING OF URBAN SPRAWL USING QGIS SOFTWARE: A CASE STUDY OF CHANDAPURA, BANGALORE
Keywords:
Urban Sprawl, QGIS, MOLUSCE Plugin, Cellular Automata, LULC, Remote Sensing, Predictive Modeling, Land Use Change, Bangalore, ChandapuraAbstract
Urban sprawl—the unplanned and uncontrolled expansion of urban areas—poses significant challenges to sustainable development, infrastructure planning, and environmental management. This paper presents a study on predictive modeling of urban growth in the Chandapura suburban region of Bangalore using open-source geospatial tools. By integrating multi-temporal Landsat satellite imagery (2015 and 2025) with the MOLUSCE (Modules for Land Use Change Evaluation) plugin in QGIS, the study employs change detection analysis, Artificial Neural Network (ANN)-based transition potential modeling, and Cellular Automata (CA) simulation to forecast future Land Use/Land Cover (LULC) patterns. The results reveal significant built-up area expansion along transport corridors, declining vegetation cover, and emerging growth hotspots. Findings support evidence-based policy recommendations including urban growth boundaries, green belt protection, and transit-oriented development to promote sustainable and compact urban growth.
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