Automated Extraction of Forest Road Network Geometry from Aerial LiDAR

We developed an algorithm that was designed to create a spatial database of a forested transportation network using aerial LiDAR. The algorithm uses two main attributes, LiDAR intensity values and ground return density. The road extraction process was developed using aerial LiDAR from McDonald-Dunn Research Forest near Corvallis, Oregon, U.S.A. The road extraction process requires X, Y, Z coordinates, intensity values, canopy type, and the maximum road grade. To compare the results of the process, nine road segments were field surveyed with terrestrial LiDAR. The result of the road extraction process resulted in 80% true positives, 34% false positives, 20% false negatives, and 38% true negatives in identifying forest roads. The average absolute value difference in the road width between the two data sets were 1.1m, while the cut/fill slope differences were minimal (> 4%) and the difference in road cross slope was two percent. These results were comparable with other published studies that examined differences between LiDAR measurements and field measurements.

Raw project data is available by contacting [e-mail]

本研究开发了一款算法，旨在利用机载激光雷达（aerial LiDAR）构建森林交通网络空间数据库。该算法主要依托两类核心属性：激光雷达强度值与地面回波密度。本道路提取流程的开发，使用了美国俄勒冈州科瓦利斯附近麦当劳-邓恩研究林（McDonald-Dunn Research Forest）的机载激光雷达数据。该道路提取流程需输入X、Y、Z坐标、强度值、冠层类型（canopy type）以及最大道路坡度（road grade）。

为验证该提取流程的效果，研究团队使用地面激光雷达（terrestrial LiDAR）对9段道路进行了野外实测。在林区道路识别任务中，该道路提取流程的识别结果为：80%真阳性（true positives）、34%假阳性（false positives）、20%假阴性（false negatives）以及38%真阴性（true negatives）。两套数据的道路宽度平均绝对差值为1.1米；挖填方坡度（cut/fill slope）差异极小（>4%），道路横坡（road cross slope）差值为2%。该结果与已发表的其他针对激光雷达测量与野外实测差异的研究结论具有可比性。

原始项目数据可通过联系[e-mail]获取。