Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges

Abstract

Climate change and land use pressures are intensifying forest disturbances in many world regions, as reflected in the increasing frequency and severity of wildfires, widespread drought‐induced tree mortality, and extensive forest degradation. Hence, spatially explicit and timely information on disturbances is essential for safeguarding the ecological integrity and societal value of both managed and natural forest ecosystems. Satellite‐based remote sensing has long been central to forest monitoring, and recent advances in deep learning (DL) are further enhancing the extraction of information from remotely sensed data, thereby improving the accuracy and scalability in detecting, mapping, and attributing disturbance events. Such applications range from mapping logging activities and delineating burned areas to the complex task of classifying disturbances into different agents such as pests, fire, and logging. Despite this progress, DL‐based approaches also face significant challenges, including the demand for large annotated training datasets and limited generalization, which might hinder their integration into operational monitoring frameworks. Addressing these barriers will require interdisciplinary collaboration that bridges algorithmic innovation with domain knowledge. This review synthesizes recent advances in DL‐based forest disturbance research, situates them within the broader landscape of traditional remote sensing methods, and highlights emerging innovations with the potential to overcome current limitations. We conclude with perspectives on key research priorities for advancing the role of DL in global forest disturbance monitoring.