Synergistic approach for land use and land cover dynamics prediction in Uttarakhand using cellular automata and Artificial neural network

Alterations in Land use and Land cover (LULC) stand out as a key catalyst for shifts in global climate patterns, environmental conditions, and ecological dynamics. In order to further enhance our comprehension of the effects of variability and climate shifts on the environment, Remote sensing and GIS analytical approaches have been thoroughly explored and are reflected in an imperative vision. Thus, the objective of this study is to model Uttarakhand's LULC pattern for 2032 and analyse changes in Uttarakhand's LULC trend between 1992 and 2022. LULC change mapping was conducted utilizing a semi-automated hybrid classification approach for high level of accuracy which integrates both Maximum likelihood and Object based image analysis classification techniques on Landsat datasets. The machine learning techniques of Cellular automata and Artificial neural networks (CA-ANN) within MOLUSCE plugin of QGIS have been applied to model future LULC patterns. The accuracy assessment results showed that the overall accuracy of LULC for the years 1992, 2002, 2012, and 2022 was 96.94 %, 97.77 %, 98.61 % and 98.87 % respectively, and the overall kappa statistics coefficient was 0.92, 0.95, 0.94 and 0.95 respectively. The simulated and projected LULC map for 2022 implies a substantially high level of accuracy, with an overall Kappa coefficient value of 0.77 % and 85.39 % of overall correctness. Then, LULC changes for the year 2032 are predicted using CA-ANN. The observed alterations between 1992 and 2032 are significant, characterized by an augmentation in built-up areas, open land, and water bodies, alongside a decline in snow-covered regions, vegetation cover. Whereas, a slight increase is seen in Forested areas. Planners and policy makers aiming to accomplish a more sustainable future and efficient management of the environment will find the changes in LULC over a prolonged period of time to be a useful asset for optimal land use planning.