Isolation Forest for Environmental Monitoring: A Data-Driven Approach to Land Management

This paper examines land management technologies to enhance environmental monitoring more efficiently. The study highlights the interactions between human activities and environmental systems with a data-driven environmental monitoring approach. There are many human pressures, such as pollution, land degradation, and habitat loss, negatively impacting soil health. The methodology proposed improves soil status assessments in response to evolving environmental pressures by utilizing satellite imagery and predictive modeling. The integration of Sentinel-2 imagery, the calculation of various spectral indices (NDVI, NBR, NDMI, EVI, SAVI) at different time intervals, and the application of the Isolation Forest algorithm are employed in this study to determine the specific area that is affected by the environmental issue. The chosen algorithm was favored due to its superior performance in handling high-dimensionality data, enhanced computational efficiency, provision of interpretable results, and insensitivity to disparities in class distribution. This study analyzes two separate study cases at different scales. The first involves wildfire identification achieving an overall accuracy of 98%. The second focuses on the expansion areas to pre-existing quarries with an overall accuracy of 95%. The NBR proved most effective in delineating burned areas, whereas the EVI generated the most remarkable results in the quarry case study. This approach provides an effective and scalable tool for environmental monitoring, supporting sustainable management policies, and strengthening ecosystem resilience.