Search

This article presents the preliminary results of the possibilities for using an unmanned aerial system (UAS) for three-dimensional (3D) data collection for levees. The UAS is equipped with an ultra-light laser scanner and digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multi-rotor. Using such a platform allows obtaining the 3D data necessary for circular surveying works with regard to levee monitoring for the prevention of flooding. Such a platform can offer an alternative to typical direct surveying measurements performed during the monitoring of such objects. It can detect changes in the multi-temporal analysis and evaluate the condition of levees ought to continuity of surface measurement and huge density of data up to dozens points per square metre. Lidar data and images of the levees were provided for several hundred metres of test area. In presented research, several flying missions were carried out which provided data that allowed for an analysis presenting the influence of the technique for initial georeferencing, altitude over the terrain, scanning lines’ length, overlap between scanning lines on the accuracy of relative orientation and absolute accuracy of the digital terrain model (DTM) that was created. The Iterative Closest Point algorithm and measurements of the natural and signalized control points were used to georeference and validate the lidar data obtained from the UAS. As reference data, Global Navigation Satellite System in real-time kinematic measurements was used to evaluate the absolute accuracy of the DTMs generated for the test area. The images captured from the UAS were a different data source which was checked to evaluate their usefulness in the considered application. A final accuracy close to 0.1 m was obtained for the generation of the DTM. The research proved the potential of this new kind of lidar data that can be acquired using ultra-light laser scanners. [ABSTRACT FROM PUBLISHER]

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data. [ABSTRACT FROM AUTHOR]