Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on the Basis of Spatial Differences

Abstract

In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m³, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude.

DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations

“…Important limitations for the replication of the described method are the current international regulations for civil UAV operation, in particular, the flight altitude. However, by reducing it, similar or better accuracies (in relative terms) as those reported in the literature are to be expected at the expense of a smaller area coverage per flight (Appendix A, Table A2), and therefore, the need for longer flights [76]. This requires that the LiDAR reference and the pixel size of the UAV images maintain a relative accuracy of at least 2:1.…”

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

“…To determine the areal and volumetric differences among the DEMs, the false positive, false negative, no difference, absolute difference zones and absolute difference volume per area were calculated for each datasets. In this context, the false positive defines the zones where the evaluated-reference difference is positive, and the false negative defines the zones where the evaluated-reference difference is negative ( Figure 4) (Yurtseven, 2019). The slope and aspect -based areal differences were evaluated on a class basis.…”

“…The choice of the optimal grid resolution is an ongoing research topic and related to many different factors such as the point density, spatial accuracy of points, size of the area, processing power of the computer, geometry of the point patterns, complexity of the terrain, cartographic standards, and gridding or interpolation technique requirements (Hengl, 2006;Bater and Coops, 2009;Yurtseven, 2019). These factors can also affect slope and aspect mapping when determining DEM quality (Chang and Tsai, 1991).…”

Camera Calibration Using Multiple Unordered Coplanar Chessboards

“…Indeed, a significant number of applications of SfM/UAV photogrammetry with pre-calibrated cameras have relied on available chessboard-based calibration software (recent examples include Cucchiaro et al, 2018;Erenoğlu & Erenoğlu, 2018;Halik & Smaczýnski, 2018;Menge & Lohsträter, 2018;Probst et al, 2018;Ventura et al, 2018;Wierzbicki, 2018;Burnett et al, 2019;Javadnejad et al, 2019;Kölling et al, 2019;Luppichini et al, 2019;Mahmood et al, 2019;Musci et al, 2019;Yurtseven, 2019), often without particular considerations, e.g. concerning focusing distance and frame coverage.…”