Improve the estimation of forest wind vulnerability through remote sensed data: a new methodology

Windstorms are the primary cause of damage to European forests. Although different mechanistic and probabilistic models have been developed to estimate the vulnerability of forests to wind, their practical application remains limited. This study presents a new, semi-automated methodology for deriving tree and forest characteristics over large areas through the analysis of Canopy Height Model (CHM) data. By integrating the semi-mechanistic model ForestGALES, the developed algorithm uses these data to calculate spatially explicit maps of Critical Wind Speed (CWS). The presented methodology is applied to a real case study to calculate the CWS of forests in the Italian Eastern Alps. Results show that adding detailed and spatially distributed forest cover information improves the CWS calculations, thereby enhancing the reliability of models to assess forest wind vulnerability. Forest practitioners can take advantage of this new methodology to enhance the resistance and resilience of their forests through specific management techniques. • R modules for assessing forest wind vulnerability on large areas are proposed. • Algorithms allow wind forest vulnerability calculation from LiDAR data. • Enables the evaluation of wind vulnerability in heterogeneous and remote forests. • Application in the Eastern Italian Alps shows the improvement of CWS accuracy. • Supports targeted forest management to enhance wind resistance and resilience.