Severe forest disturbance events are becoming more common due to climate change and many forest managers rely heavily upon airborne surveys to map damage. However, when the damage is extensive, airborne assets are in high demand and it can take managers several weeks to account for the damage, delaying important management actions. While some satellite-based systems exist to help with this process, their spatial resolution or latency can be too large for the needs of managers, as evidenced by the continued use of airborne imaging. Here, we present a new, operational-focused system capable of leveraging high spatial and temporal resolution Sentinel-2 and Planet Dove imagery to support the mapping process. This system, which we have named Astrape (“ah-STRAH-pee”), uses recently developed techniques in image segmentation and machine learning to produce maps of damage in different forest types and regions without requiring ground data, greatly reducing the need for potentially dangerous airborne surveys and ground sampling needed to accurately quantify severe damage. Although some limited field work is required to verify results, similar to current operational systems, Astrape-produced maps achieved 78–86% accuracy with respect to damage severity when evaluated against reference data. We present the Astrape framework and demonstrate its flexibility and potential with four case studies depicting four different disturbance types—fire, hurricane, derecho and tornado—in three disparate regions of the United States. Astrape is capable of leveraging various sources of satellite imagery and offers an efficient, flexible and economical option for mapping severe damage in forests.

Effective management of forest insects and diseases requires detection of abnormal mortality, particularly among a single species, sufficiently early to enable effective management. Remote detection of individual trees crowns requires a spatial resolution not available from satellites such as Landsat or Sentinel-2. In the United States, there are currently few operational systems capable of effectively and affordably detecting and mapping tree mortality over broad landscapes using high-resolution imagery. Here, we introduce the Tree Condition and Analysis Program (TreeCAP), an open-source system that uses freely available imagery from the National Agriculture Imagery Program (NAIP) to create maps of tree condition (healthy or damaged). We demonstrate the potential applications of TreeCAP in four study sites: (1) beetle-killed pines in California, (2) emerald ash borer progression in Wisconsin, (3) hemlock wooly adelgid mortality in Pennsylvania, and (4) drought damage in Texas. We achieved an average overall accuracy of 87% across all study sites.