Current World Environment

Land Use and Land Cover (LULC) dynamics have a major impact on environmental sustainability, re-source management, and urban development. Effective decision-making depends on correct forecasting of these changes. This research predicts LULC changes with the Random Forest (RF) machine learning tech-nique using satellite-derived data from the National Remote Sensing Centre (NRSC) for the years from 2005 to 2023. The dataset includes various LULC categories such as built-up land, agricultural lands, plan-tation/orchards, forests, wetlands, grasslands, wastelands, and water bodies. This study focuses on an area of Tamilnadu, India, covered by NRSC’s LULC datasets from 2005 to 2023. The Random Forest model was first trained on data for the years from 2005 to 2017 to predict LULC for the next consecutive years 2018–2023, with validated against actual LULC values for Tamilnadu, India from the year 2018 to 2023, achieving high accuracy with a correlation coefficient (R > 0.97 in later years) and decreasing Mean Abso-lute Error. Based on the complete historical dataset from 2005 to 2023, the trained model is then applied to predict LULC changes for the years from 2024 to 2028. The results indicate a significant increase in urban development of 0.001 hectare annually and a consistent decrease in double/triple cropping areas of ap-proximately 1.5 hectare between 2024 and 2028, stable but slightly declining forest cover, and water body spread oscillations. The steady increase in built-up land underscores the importance of controlled urban expansion and the decline in double/triple cropping areas calls for policies that support sustainable farm-ing practices. With forest cover slightly declining, policymakers should strengthen conservation initia-tives, afforestation efforts, and enforce stricter land-use regulations to prevent degradation. The oscillating spread of water bodies highlights the need for improved watershed management strategies.

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