Detection of soil degradation processes in river basins using satellite indices and a custom QGIS plugin

Background. This study addresses the challenge of identifying and assessing soil degradation risks in river basins, which significantly impact the ecological state of territories. Special emphasis is placed on enhancing the efficiency of spatial analysis through the use of an advanced QGIS plugin developed by the authors. Methods. The software tool was implemented using the OSGeo Python package and integrates algorithms for weighted overlay and semi-automated vectorization of results. It is designed as an internal function set that requires no additional installations. The plugin's functionality includes satellite data processing, automated detection of anomalous zones, and the construction of a generalized correspondence table linking spectral indices to specific forms of degradation. The analysis employed multi-decadal Landsat imagery (1985–2025) together with Sentinel-2 data (2025), processed within the QGIS environment. Testing was conducted in two contrasting study areas – the Dnipro and Supii river basins – characterized by markedly different levels of anthropogenic pressure. Results. The analysis revealed clear patterns of soil degradation processes, including vegetation decline, salinization, waterlogging, drought manifestations, and erosion. The use of the generalized spectral index table optimized the interpretation of satellite imagery, enhancing the reliability of classifying degradation types. The plugin demonstrated high effectiveness in integrating index-based analysis with spatial modeling, thereby enabling comprehensive evaluation of basin systems. Conclusions. The enhanced tool proved its practical applicability for ecological monitoring and decision support. A key innovation lies in the integration of semi-automated remote sensing interpretation with multi-level index classification, enabling both rapid anomaly detection and detailed spatio-temporal assessment of degradation dynamics. Prospective directions for development include the incorporation of machine learning algorithms for automated classification, the use of Big Data processing and parallel computing to ensure scalability, and integration with cloud-based platforms such as Google Earth Engine to provide real-time access to updated data streams. Collectively, these advancements lay the methodological foundation for developing a universal system to forecast and mitigate degradation processes in river basins.