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.

中文翻译：

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结合基于LiDAR的树木提取方法和GIS识别受风灾危害的基础设施

大风引起的抛掷对运输基础设施和道路使用者都构成了重大威胁。因此，对联邦主干道路网沿线的树木进行了暴露分析，并示例性地应用于了德国北莱茵-威斯特法伦州的联邦州。该项目的目的是在可自由访问的高分辨率机载LiDAR数据以及识别和参数化单个树木的RGBI正射影像数据的基础上，开发一种基于GIS的方法。为了确定合适的方法，对具有不同设置和参数的几个模型进行了计算，验证和迭代调整，以获得用于树识别的最佳设置。树木的识别最终通过对单棵树木应用最小曲率技术以及对茂密植被（尤其是具有小树冠直径的树木）采用局部最大值方法来实现。通过使用归一化差异植被指数，已纠正了与非植被区域相对应的错误分类结果。使用我们的方法，我们可以在非常异构的环境中实现65％到75％的检测精度，在更特定的环境中可以达到75％到100％的检测精度。检索了所有可能影响道路基础设施和道路使用者的候选树并将其加入相应的路段，以提供一种快速有效的方法，用于分析和可视化主干道路网中因翻倒树木而易受伤害的部分。通过使用归一化差异植被指数，已纠正了与非植被区域相对应的错误分类结果。使用我们的方法，我们可以在非常异构的环境中实现65％到75％的检测精度，在更特定的环境中可以达到75％到100％的检测精度。检索了所有可能影响道路基础设施和道路使用者的候选树并将其加入相应的路段，以提供一种快速有效的方法，用于分析和可视化主干道路网中因翻倒树木而易受伤害的部分。通过使用归一化差异植被指数，已纠正了与非植被区域相对应的错误分类结果。使用我们的方法，我们可以在非常异构的环境中实现65％到75％的检测精度，在更特定的环境中可以达到75％到100％的检测精度。检索了所有可能影响道路基础设施和道路使用者的候选树并将其加入相应的路段，以提供一种快速有效的方法，用于分析和可视化主干道路网中因翻倒树木而易受伤害的部分。