Advanced Backscatter Modeling for Enhanced Detection of Forest Disturbances in the Indian Subcontinent

Forest ecosystems play a pivotal role in maintaining ecological balance, serving as carbon sinks, biodiversity reservoirs, and providers of critical ecosystem services such as climate regulation and water cycle maintenance. Despite their importance, forests are increasingly threatened by deforestation, degradation, and climate-induced disruptions, leading to significant ecological and socio-economic consequences. Timely and accurate detection of forest disturbances is essential for formulating effective conservation policies, mitigating biodiversity loss, and ensuring sustainable forest management. This study presents a novel backscatter modeling framework designed to enhance the detection of forest disturbances across diverse and heterogeneous landscapes of the Indian subcontinent. Implementing the unique capabilities of synthetic aperture radar (SAR) data, the framework integrates physical scattering mechanisms with vegetation structural variations, enabling precise monitoring of changes in forest cover. SAR's all-weather, day-and-night imaging capabilities make it particularly suitable for regions with frequent cloud cover and varied terrain, addressing key challenges faced by optical-only methods. The proposed methodology employs a hybrid approach that combines theoretical backscatter modeling with advanced machine learning algorithms for feature extraction and classification. This integration includes the strengths of both data-driven analytics and physics-based modeling, offering robust detection capabilities for both abrupt disturbances, such as clear-cutting and gradual changes like forest degradation. The framework's adaptability allows it to account for the complexities of diverse forest structures, dynamic seasonal variations, and landscape heterogeneity, making it a scalable solution for large-scale forest monitoring. Validation of the framework was conducted using multi-temporal SAR datasets and high-resolution optical imagery from key forested regions in the Indian subcontinent. The results highlight the framework’s superior sensitivity and accuracy compared to existing methods, demonstrating its ability to detect a wide range of disturbances with precision. This improved detection capability is critical for understanding the underlying drivers of forest changes and their ecological impacts. By addressing limitations in current forest monitoring techniques, this backscatter modeling framework provides a powerful tool for conservation and sustainable management. Its implementation has the potential to support policy-makers and environmental managers in formulating data-driven strategies for forest protection and restoration. Ultimately, the study underscores the framework’s transformative potential in enhancing forest resilience, promoting biodiversity conservation, and contributing to sustainable development in regions facing increasing environmental and anthropogenic pressures.