Spatiotemporal analysis of land use land cover and future simulation for agricultural sustainability in a sub-tropical region of India

Spatial distribution of rice cultivated area and its future projections are indispensable for providing food security, irrigation water management, greenhouse gas emission, ecosystem, and environmental protection. Accurate simulation of future rice cultivation is challenging in the context of climate change involving spatial modeling. This study introduced a hydrological data-based prediction approach integrating cellular automata-Markov chain model with multi-criteria decision analysis. Three decadal transitions of different land use land cover (LULC) in an agricultural intensified canal irrigated area were analyzed using Random Forest algorithm. The Landsat satellite imageries were classified with an overall accuracy of more than 90%. The results revealed that the area under water sources, plantation, and fallow land decreased by 53.1%, 33.9%, and 63.6%, respectively, while habitation and Boro rice cultivated area increased significantly by 1243.3% and 211%, respectively, during 1988-2018. The kappa coefficient (0.86), allocation disagreement (10.9%), and quantity disagreement (1.16%) revealed good agreement between the predicted and classified LULC map of 2018. The probable distribution of the LULC map in 2028 and 2038 indicated 54.5% and 59.3% of the command area under Boro rice cultivation as a dominant land cover, respectively. Further, the predicted LULC map anticipated that Boro rice cultivation was likely to expand at the expense of plantation and fallow land which may lead to massive water scarcity in the near future. The proposed approach covered up the limitations of spatial modeling on agricultural growth trends by integrating significant driving factors with quantitative and spatiotemporal aspects of LULC transition. These findings will provide a baseline map for sustainable policy reformation and contemplation on the repercussions that local LULC transition has on global environmental change.