High-Resolution Dynamic Monitoring of Rocky Desertification of Agricultural Land Based on Spatio-Temporal Fusion

Abstract

The current research on rocky desertification primarily prioritizes large-scale surveillance, with minimal attention given to internal agricultural areas. This study offers a comprehensive framework for bedrock extraction in agricultural areas, employing spatial constraints and spatio-temporal fusion methodologies. Utilizing the high resolution and capabilities of Gaofen-2 imagery, we first delineate agricultural land, use these boundaries as spatial constraints to compute the agricultural land bedrock response Index (ABRI), and apply the spatial and temporal adaptive reflectance fusion model (STARFM) to achieve spatio-temporal fusion of Gaofen-2 imagery and Sentinel-2 imagery from multiple time periods, resulting in a high-spatio-temporal-resolution bedrock discrimination index (ABRI*) for analysis. This work demonstrates the pronounced rocky desertification phenomenon in the agricultural land in the study area. The ABRI* effectively captures this phenomenon, with the classification accuracy for the bedrock, based on the ABRI* derived from Gaofen-2 imagery, reaching 0.86. The bedrock exposure area in the farmland showed a decreasing trend from 2019 to 2021, a significant increase from 2021 to 2022, and a gradual decline from 2022 to 2024. Cultivation activities have a significant impact on rocky desertification within agricultural land. The ABRI significantly enhances the capabilities for the dynamic monitoring of rocky desertification in agricultural areas, providing data support for the management of specialized farmland. For vulnerable areas, timely adjustments to planting schemes and the prioritization of intervention measures such as soil conservation, vegetation restoration, and water resource management could help to improve the resilience and stability of agriculture, particularly in karst regions.