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.

Highlights

  • Considering recent developments, different equipment and methods are used in the generation of information related to the surface of the ground and objects on it

  • According to the terrestrial laser scanner (TLS) data registration and georeferencing process results, the average, standard and maximum deviations were obtained as 2.6 mm, 1.6 mm and 6.6 mm, respectively

  • The data obtained by TLS and Global Navigation Satellite System (GNSS) were proven reliable to use as the reference

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Summary

Introduction

Considering recent developments, different equipment and methods are used in the generation of information related to the surface of the ground and objects on it. The first is to collect the data using ground-based surveying techniques At this stage, many different equipment can be used, such as Global Navigation Satellite System (GNSS) receivers, total station (TS), terrestrial laser scanner (TLS), etc. The other approach is to collect the spatial data using remote sensing techniques such as photogrammetry, radar-based and laser-based techniques [2,3]. The ground-based surveying techniques are more accurate than the remote sensing techniques, the data acquisition can be time-consuming when the applications require high-resolution information [4]. Other important error sources are the surface reflectance or reflectivity of the scanned object and the Ground Control Point (GCP) coordinate accuracy in georeferencing

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