Forest resources have important ecological and environmental values, and monitoring forest changes using remote sensing images is essential for resource management and ecological protection. However, current forest change detection methods fail to simultaneously integrate fine spatial information with temporal dynamic data, making them susceptible to pseudo changes induced by seasonal factors. In this paper, we propose a forest change detection method called STFNet that integrates multi-source spatiotemporal information. By combining fine spatial details of high-resolution images with dynamic information from time-series images, STFNet enhances the accuracy of forest change detection, alleviating the problem of information fusion difficulties caused by inconsistent granularity in spatiotemporal spectral features from different sources. In STFNet, we propose a cross-attention-based temporal differential feature fusion module (CATFF) to capture spatiotemporal dependencies within time-series images and a multiresolution contextual differential feature fusion module (MCDF) to achieve efficient spatial contexture fusion across multiresolution images. To validate our method, we conduct experiments using Gaofen and Sentinel-2 satellite images. Experimental results demonstrate that STFNet achieves excellent performance with an F1-score of 87.65%, outperforming state-of-the-art methods by at least 2.02%. Our ablation study further confirms the effectiveness of our method in leveraging time-series information to detect forest changes and suppress seasonal interference.

Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual style interferences stemming from varying acquisition times, such as radiation, weather, and phenology changes, often lead to false detections. Existing methods struggle to robustly measure background similarity in the presence of such discrepancies and lack quantitative validation for assessing their effectiveness. To address these limitations, we propose Representation Consistency Change Detection (RCCD), a novel deep learning framework that enforces global style and local spatial consistency of features across encoding and decoding stages for robust cross-visual style change detection. RCCD leverages large-kernel convolutional supervision for local spatial context awareness and global content-aware style transfer for feature harmonization, effectively suppressing interference from background variations. Extensive evaluations on S2Looking and LEVIR-CD+ datasets demonstrate RCCD’s superior performance, achieving state-of-the-art F1-scores. Furthermore, on dedicated subsets with large visual style differences, RCCD exhibits more substantial improvements, highlighting its effectiveness in mitigating interference caused by visual style errors. The code has been open-sourced on GitHub.