Windthrows induced by strong winds pose a major threat to both transport infrastructure and road users. Therefore, an exposure analysis of trees along the federal trunk road network was carried out and exemplarily applied for the federal state of North Rhine-Westphalia, Germany. The aim of this project was the development of a GIS-based method on the basis of freely accessible high-resolution airborne LiDAR data as well as RGBI orthoimage data that identifies and parameterizes single trees. For the determination of a suitable method, several models with different settings and parameters were calculated, validated, and iteratively adjusted to gain the optimal settings for tree identification. The identification of trees was finally realized by applying both a minimum-curvature technique for single trees as well as a local maximum approach for dense vegetation, especially for trees with small crown diameters. False classification results corresponding to nonvegetation areas have been corrected by the use of the normalized difference vegetation index. With our method, we accomplish detection accuracies from 65% to 75% in very heterogeneous environments and 75% to 100% in more specific settings. All tree candidates with potential to affect the road infrastructure and road users were retrieved and joined to the respective road sections to deliver a fast and effective method for analyzation and visualization of vulnerable parts of the trunk road network due to tumbling trees.