The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage.
Context Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations.
Aims (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects?
Methods We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area.
Results Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement.
Conclusion Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.
Windthrow; Forest wind damage; Risk modelling; LiDAR; Remote sensing
Gopalakrishnan, R., Packalen, P., Ikonen, V. et al. The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland. Annals of Forest Science 77, 97 (2020). https://doi.org/10.1007/s13595-020-00992-8
For the read-only version of the full text:
The datasets generated during and/or analysed during the current study are not publicly available as they are not fully owned by the authors, but are available from the corresponding author on reasonable request.