Land use/land cover (LULC) change serves as a critical indicator of anthropogenic influence on terrestrial landscapes, particularly in agriculturally dominated regions such as the Indo-Gangetic Plains. This study analyses the spatiotemporal dynamics of LULC in Sambhal District, Uttar Pradesh, for the years 2010, 2017, and 2025, and projects future land use patterns for 2035 using a Cellular Automaton-Artificial Neural Network (CA-ANN) model implemented through the MOLUSCE plug-in in QGIS. Three spatial drivers’ digital elevation model (DEM), distance from settlements, and distance from roads were incorporated as explanatory variables. Five land cover classes (settlement, cropland, waterbody, fallow land, and agroforestry) were mapped with overall classification accuracies of 88.51% to 92.68% and Kappa coefficients of 0.85 to 0.90. Results indicate a substantial decline in cropland from 71.06% (2010) to 44.52% (2025), likely driven by groundwater stress, labour migration, and declining agricultural profitability, alongside a marked increase in fallow land (4.17% to 38.07%), reflecting widespread land abandonment or transitional use change. Settlement and agroforestry areas expanded steadily throughout the study period. Projected 2035 scenarios suggest continued growth of settlement (5.56%) and agroforestry (17.43%), with ongoing cropland contraction. The exceptional decline in waterbody extent (from 20.57% in 2010 to 0.41% in 2025) is partly attributed to seasonal imagery inconsistencies and possible classification artefacts, and warrants further hydrological validation. These findings provide a baseline for sustainable land use planning, groundwater conservation, and evidence-based policy interventions in rapidly transforming agrarian landscapes.
Sharma et al. (Wed,) studied this question.