Enhanced Accuracy in Cellular Automata-Markov Chain Model for Land Classification Analysis and Prediction

Abstract

Technology can leverage green solutions with Land Classification (LC) analysis and prediction to promote sustainable land management, paving its way to eco-centric development by analyzing hyper-spectral satellite images for land classification using machine learning (ML) techniques and predicting future trends with improved accuracy. LC-ML synergy aligns with green technology to monitor deforestation, habitat destruction, and climate change. Improved accurate land classification and prediction assist in sustainable land resource management and optimization. It supplements data-driven insights into urban planning and eco-friendly infrastructure development by identifying areas for reforestation relating to carbon sequestration and renewable energy integration. Sustainable management of agricultural land, forests, urban areas, and water bodies can prevent resource depletion from controlling environmental degradation. Accurate land cover predictions over 30 years can help policymakers avoid resource depletion and promote sustainability. This study analyzed historical data for LC classification and then used the Cellular Automata Markov Chain (CA-MC) model to predict future trends. Model reliability is assessed by metrics such as the Kappa and overall accuracy. With an overall model accuracy of 81.33%, these refinements contribute to policymakers’ decision-making to plan sustainable land use, allocate resources, and balance environmental conservation with economic development. The model supports stakeholders in identifying LC patterns, particularly in urban expansion and deforestation, to promote equitable and sustainable growth.