From consumer to enterprise grade: How the choice of four UAS impacts point cloud quality. Issue 10 (10th June 2021)

• Record Type: Journal Article
• Authors: Stark, Manuel; Heckmann, Tobias; Piermattei, Livia; Dremel, Fabian; Kaiser, Andreas; Machowski, Patrick; Haas, Florian; Becht, Michael
• Is Part Of: Earth surface processes and landforms. Volume 46:Issue 10(2021)
• Page Start: 2019
• Page End: 2043
• Publication Date: 2021-06-10
• Subjects: error comparison -- spatial autocorrelation -- structure‐from‐motion photogrammetry -- topographic surveying -- uncrewed aerial system -- unmanned aerial systems; Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.5142
• Languages: English
• ISSNs: 0197-9337

Abstract

Abstract: Uncrewed aerial systems (UAS), combined with structure‐from‐motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes. However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (−0.4 to −1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends (i.e. differences increase with distance across the survey—doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally. Abstract : In this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. Differences in mean errors (−0.4 to −1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. Compared to the other UAS, the Mavic Pro data show trends (i.e., differences increase with distance across the survey—doming), and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude, and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration.