Abstract
Timely identification of forest disturbance agents is essential for effective ecosystem management and rapid response to natural and anthropogenic threats. However, most near-real-time (NRT) monitoring systems focus solely on detecting disturbance locations and timing of generic disturbances, lacking attribution of causal agents that is critical for operational decision making. This study presents a novel NRT framework to map major forest disturbance agents—wildfire, logging, and stress—across China using the Harmonized Landsat and Sentinel-2 (HLS) dataset. To address the scarcity of local training data, we introduced a transferring-guided sampling strategy that efficiently generated tile-specific local samples by leveraging disturbance archives from the conterminous United States and subsequent expert verification. Stage-based random forest models were subsequently constructed using 16 temporally dynamic features derived from HLS spectral trajectories to classify anomalies at varying disturbance stages. The system achieved an overall first-alert lag of 11.6 days and a level-off lag of 15.5 days, with corresponding overall accuracies of 77.5% and 84.0%. Wildfire disturbances exhibited the shortest detection lag and the highest accuracy, followed by logging and stress, reflecting differences in spectral separability among agents. Compared with the global DIST-ALERT product, the proposed framework achieved higher accuracy and fewer false detections, at a trade-off of a 3.7-day longer first-alert lag, while providing actionable information on disturbance causality. These results demonstrate the feasibility of operationally mapping disturbance agents in near real-time at a national scale. The proposed framework offers a transferable, data-efficient solution for rapid forest disturbance attribution and provides a foundation for global-scale NRT disturbance monitoring initiatives.