Storm events are significant disturbance agents that can cause considerable forest damage through windthrow. Assessment and mapping of the extent and severity of windthrow is critical to provide reliable information to forest managers to prioritize post-storm hazard reduction (including public safety and fire risk) and to guide restoration activities. Detailed on-ground assessments after windthrow are often impossible due to lack of access and safety concerns. In 2021, severe windstorms caused unprecedented and extensive windthrow in a temperate eucalypt forest in south-eastern Australia. The purpose of this study is to quantify the severity and extent of the damaged forest area as the change in percentage canopy cover using remotely sensed data. We assessed percentage canopy cover from high-resolution aerial images of 455 randomly selected plots in disturbed and undisturbed areas to train a model and machine learning framework to predict landscape scale canopy cover from Sentinel-2 images. A random forest model using all single bands and percentiles best predicted the canopy cover (R2 = 0.69). Sentinel-2 images were then used to predict canopy cover pre- and post-windthrow to assess and map the severity of windthrow as the change in percentage canopy cover. Of the total 63,471 ha of forest area assessed, 63% (39,987 ha) was impacted by windthrow, with 46% at low severity (<30% canopy cover loss), 11% at moderate (30–50% canopy cover loss) and 6% at high severity (>50% canopy cover loss). Our study provides the first quantitative mapping of windthrow severity mapping for a temperate eucalypt forest in Australia that demonstrates an effective remote assessment methodology and provides critical information to support post-windthrow management decisions.
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