National vulnerability models for wind and snow damage based on airborne laser scanning and National Forest Inventory data

In this study we created national vulnerability models for wind and snow damage based on a combination of remote sensing and Swedish National Forest Inventory (NFI) data and compared the performance of airborne laser scanning (ALS) driven versus field-measured information in the modeling of damage vulnerability. The empirical training data consisted of circa 42,000 field plots, containing information about recent wind and snow damage, monitored between 2010 and 2022 in the Swedish NFI. Occurrence of damage was predicted using variables calculated from national ALS data (2009–2023), other mapped products including forest attributes, soil and terrain related data and spatial variables on stand neighborhood, and weather data. In contrast to earlier large-scale damage models, we used direct ALS metrics to fully exploit ALS-derived forest structural information. Logistic regression was used for modeling, and separate models were created for southern and northern Sweden to consider the geographical differences in forest structure and climate conditions. Field data-based models performed slightly better than remote sensing (RS) -based models, resulting in an AUC of 0.8 for northern and 0.73 for southern Sweden. Corresponding results for RS-based models were 0.77 and 0.69. Best models included both forest structural variables (ALS or field-based), tree species, terrain, and weather information. We successfully demonstrated the combination of ALS and NFI data to map forest vulnerability to wind and snow damage, enabling evaluation of damage vulnerability from stand to national level. By locating areas with high damage vulnerability, forest owners can more easily adapt forest management to increasing climate impacts.