Scenario-Driven Land-Use Changes Anomaly Assessment for Decision Support in Border-Adjacent Regions

This study develops a scenario driven decision-support framework that is driven by scenarios for the assessment of anomalous land-use change patterns with the help of remote sensing-inspired indicators. Rather than being an automated monitoring or surveillance system, the proposed method is designed to assist human analysts openly by pointing out the areas that warrant prioritized examination. The method overcomes the limitation of inaccessible defense- or security-sensitive satellite datasets through the use of synthetically generated feature representations to mimic realistic land-change scenarios and to allow controlled testing of model performance under data-limited conditions. The framework brings together different kinds of abstract numerical indicators, which are among the spectral change metrics, the temporal behavior attributes, the historical stability measures, the spatial context descriptors, and the regulatory or policy-based constraints. These indicators are formulated to represent land-use changes in a more holistic and contextual manner than by relying only on pixel-level change detection, thereby emphasizing contextual interpretation. Unlike the traditional methods, which predominantly depend on a limited selection of spectral indices such as NDVI, NDWI, and NDBI, the approach in question encompasses a greater spatio-temporal context as a means to lessen the occurrences of false alarms and increase the interpretability. By separating normal civilian expansion from potentially anomalous land-use behavior, the framework supports environmental monitoring, local planning, and decision-making for land-use assessment in border-near areas, while still keeping the analytical emphasis on human intervention.