Research on Protective Forest Change Detection in Aral City Based on Deep Learning

Protective forests play a crucial role in ecosystems, particularly in arid and semi-arid regions, where they provide irreplaceable ecological functions such as windbreaks, sand fixation, soil and water conservation, and climate regulation. This study selects Aral City in Xinjiang as the research area and proposes a method that integrates high-resolution remote sensing data (GF-2) with a Spatiotemporal Attention Neural Network (STANet) model to improve the accuracy of protective forest change detection. The study utilizes GF-2 remote sensing imagery and employs a spatiotemporal attention mechanism to incorporate spatial and temporal information, overcoming the limitations of traditional methods in processing long-term time-series remote sensing data. The results demonstrate that the combination of GF-2 imagery and the STANet model effectively detects protective forest changes in Aral City, achieving an F1-score of 83.64% and an accuracy of 78.52%, indicating significant detection capability. Spatial analysis based on the change detection results reveals notable changes in the protective forest area within the study region, with a decline in vegetation coverage in certain areas. This study suggests that the STANet method has strong application potential in protective forest change detection in arid regions, providing precise spatiotemporal change information for protective forest restoration and management. The findings offer a scientific basis for ecological restoration and sustainable development in Aral City, Xinjiang, and are of great significance for improving protective forest management and land use decision-making.