Protective forests are vital to ecological security in arid desert regions, but their spatial distribution is often inefficient. This study aims to optimize the spatial distribution of protective forests in Alaer City using a combination of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The optimization focuses on three objectives: economic benefits, ecological benefits, and food security. A neural network model is applied to analyze forest distribution suitability based on spatial factors. The results show that the optimized distribution significantly enhances GDP, carbon sequestration, water yield, and food production, while reducing soil erosion. The forest area is mainly concentrated along rivers, agricultural fields, and desert edges, with increased coverage at the Taklamakan Desert’s periphery improving wind and sand resistance. The FLUS model is validated with high accuracy (90.73%). This study provides a theoretical foundation for the sustainable development of protective forests, balancing ecological and economic goals in Alaer City.

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information to protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust watermarking method for remote sensing image protection. It incorporates an IR-FFM feature fusion module to enhance feature reuse across different layers and an SE-AM attention mechanism to emphasize critical watermark features. Additionally, a noise simulation sub-network is introduced to improve resistance against common and combined attacks. The proposed method achieves high imperceptibility and robustness while maintaining low computational cost. Extensive experiments on both remote sensing and natural image datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate the practicality and reliability of SIR-DCGAN for secure image distribution and copyright protection.

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.