Developing a computerized approach for optimizing individual tree removal to efficiently reduce crown fire potential

Thinning is a common silvicultural treatment being widely used to restore different types of overstocked forest stands in western U.S. because of its effect on changing fire behavior. Typically, thinning is applied at the stand level using prescriptions derived from sample plots that ignore variability in tree sizes and location within stands. Thinning prescriptions usually specify tree removal in terms of number of trees or basal area, resulting in a large number of cut-tree spatial patterns that meet the same prescription. However, the effect of each pattern on reducing crown fire potential can vary widely depending on the spatial distribution of leave-trees after treatment. Additionally, stand-level thinning prescriptions ignore cut-tree locations, which influence the economic efficiency of the thinning operations. Lastly, decisions on tree selection affect future competition levels of remaining trees, but the associated spatial and temporal effects on tree growth and crown fire potential over time are not considered in the development of thinning prescriptions. To address the limitations of current stand-level thinning practices, we designed a computerized approach to optimize individual tree removal and produce site specific thinning prescriptions that efficiently reduce crown fire potential. Based on stem map and tree attributes derived from light detection and ranging (LiDAR) technology and a distance-dependent individual tree growth model, current and future tree-level fuel connections between adjacent trees were predicted and used as measures of crown fire potential. The approach makes the spatial selection of cut- and leave-trees that most efficiently reduces crown fire initiation and propagation over time while ensuring cost efficiency of the thinning treatment. Application results on a forest stand in western Montana show that the optimal tree selection provided by the computerized approach can reduce crown fire potential more efficiently than current thinning practices represented by a manual selection of tree removal.

► Our computerized approach optimizes tree removal for reducing crown fire potential over time.
► The approach uses LiDAR-derived to obtain detailed terrain and tree-level information; location and attributes.
► We used models for estimating tree-level fuel connections, tree growth and skidding costs.
► Crown fire potential is reduced more efficiently by applying our approach than current thinning prescriptions.