Land Use Change Forecasting in Kakinada Using Open-Source Remote Sensing and GIS Tools

Understanding land use and land cover (LULC) dynamics is essential for effective environmental management and urban planning. This study investigates LULC changes in Kakinada over multiple time periods using QGIS and ArcGIS. Satellite imagery underwent preprocessing, including cloud masking, image enhancement, and georeferencing, to ensure spatial accuracy. QGIS’s raster-based tools enabled classification into five LULC categories—built-up areas, agricultural land, forest cover, water bodies, and barren land—while ArcGIS supported advanced spatial analysis and visualization. Results indicate a significant increase in built-up areas from 12.5% in 2014 to a projected 36.4% by 2040, a ~191% relative increase, driven by population growth and urbanization. Conversely, agricultural land decreased from 48.6% to 34.8% (~28.4% reduction), and forest cover declined from 28.2% to 25.6%. Future LULC changes were simulated using the MOLUSCE plugin in QGIS, employing an Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) architecture and Cellular Automata (CA), achieving a prediction accuracy above 86% and a Kappa coefficient of 0.942. These findings underscore the rapid urban expansion in Kakinada and the urgent need for sustainable land management strategies to balance development with environmental conservation.