Detecting Forest Road Wearing Course Damage Using Different Methods of Remote Sensing

Abstract

Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of the road wearing course damage detected by four different remote sensing methods: close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning. At the beginning of verification, cross sections of the road surface were surveyed geodetically and then compared with the cross sections created in the DTMs which were acquired using the four methods mentioned above. The differences calculated between particular models and geodetic measurements show that close range photogrammetry achieved an RMSE of 0.0110 m and the RMSE of terrestrial laser scanning was 0.0243 m. Based on these results, we can conclude that these two methods are sufficient for the monitoring of the asphalt wearing course of forest roads. These methods allow precise and objective localization, size and quantification of the road damage. By contrast, mobile laser scanning with an RMSE of 0.3167 m does not reach the required precision for the damage detection of forest roads due to the vegetation that affects the precision of the measurements. Similar results are achieved by airborne laser scanning, with an RMSE of 0.1392 m. As regards the time needed, close range photogrammetry appears to be the most appropriate method for damage detection of forest roads.

Unpaved forest road quality assessment using airborne LiDAR data

“…Aside from airplanes, UAVs such as drones can carry laser scannners too, which can lead to reduced costs (Zhu 2013). UAVs have already been used for mapping construction work in forests (Buğday 2018), and can easily become a popular tool for road quality assessments as well in the near future if their overall precision can be improved relative to other methods (Hrůza 2018). Tests have been carried out to identify rut depths exceding 20 cm using UAV photogrammetry , and Mobile Laser Scanning, including both car and UAV-mounted sensors, can currently achieve a precision RMSE of around 5.5 cm (Jaakkola 2015).…”

“…The different road detection methods that are available vary greatly in their precision. In a comparative assessment of close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning, close range photogrammetry was found to perform best, with an RMSE of 0.0110 m, while that for terrestrial laser scanning was 0.0243 m and that for airborne laser scanning 0.1392 m (Hrůza et al 2018). However, besides precision, the time required to collect the data for a certain area and the costs of doing so are also important factors in large-scale road quality assessments.…”

The Evaluation of SfM Technique in the Determination of Surface Deformation on Skidding Roads Following Timber Harvesting

“…Recent studies on LIDAR and SfM based analysis of road surface deformation have demonstrated that SfM technique can generate data at centime sensitivity for surface deformation models (Akay, 2016;Seki et al, 2017;Hrůza et al, 2018;Şireli, 2019). Because road surface performance and deformation measurements offer great advantages for operators and researchers, automatic techniques are widely used instead of manual methods (Ahmed et al, 2011).…”

Reduction Method for Mobile Laser Scanning Data

“…To use the point cloud for option 2, the data should be georeferenced. Standard georeferencing of MLS data was based on the transformation from the scanner local coordinates to global coordinates using boresight parameters and navigation information from the on-board GPS and IMU [6,22].…”

“…A review of conventional MLS systems and their accuracy assessments can be found in studies by Hruza et al, Mikrut et al, and Barber et al [6][7][8]. They used RTK-GPS (Real Time Kinematic) measurements to collect reference data on two test sites to validate the geometric accuracy of the Streetmapper MLS system.…”