Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity

Abstract

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

Assessment of RTK Quadcopter and Structure-from-Motion Photogrammetry for Fine-Scale Monitoring of Coastal Topographic Complexity

“…The targets we used had no marks at the center point where RTK-GNSS measurement is carried out, meaning that the target geolocation in the images can be prone to large horizontal uncertainties. The targets have been upgraded recently to include a chequerboard-like pattern, which has been recommended for pinpoint accuracy in RTK-GNSS measures and image positioning [71]. Nevertheless, we showed that error evaluation using ground targets can produce optimistic estimates, particularly when the error is evaluated at GCPs (used for photogrammetric processing).…”

Post‐wildfire coastal dunefield response using photogrammetry and satellite indices

“…Georeferencing was prioritised on control points in images where targets were centrally located to reduce edge distortions. There is uncertainty on the optimal camera calibration corrections for SfM surveys processed in MS due to variations in sensor capabilities and pre‐processing of JPEGs for geometric adjustments (Bertin et al, 2022; Cooper et al, 2021; Gonçalves et al, 2021; Senn et al, 2022). Camera optimisation parameters or internal orientation parameters (Table 7) were chosen after extensive testing and according to works using a similar sensor (Cooper et al, 2021; Zhou, Daakir, et al, 2020).…”

An Error Prediction Model for Construction Bulk Measurements Using a Customized Low-Cost UAS-LIDAR System

“…Designated calibration targets placed in a controlled environment can be used to compare the performance of bulk measurement with both technologies in the future. A terrestrial laser scan (TLS) of the test site was performed separately [57], where a Leica ScanStation P40 with a 3 mm (1 𝜎) accuracy at 50 m was used. In this validation of the test site, the TLS point cloud was used as a true reference for the comparison with a vertical profile of down-sampled UAS-LIDAR point cloud collected on the rock stairs at the test site.…”