Chen, Chunpeng and Zhang, Ce and Wu, Wenting and Jiang, Wenhao and Tian, Bo and Zhou, Yunxuan (2022) Application of UAV-Based Photogrammetry Wthout Ground Control Points in Quantifying Intertidal Mudflat Morphodynamics. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium :. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium . IEEE, pp. 7767-7770. ISBN 9781665427937
Full text not available from this repository.Abstract
Accurate topography of intertidal mudflats of a fine resolution is fundamental data to further understand the coastal process and achieve targeted coastal management. However, the mapping of mudflat topography is hindered by poor accessibility of muddy environments and a short observation time window caused by periodic tides. Commonly as a revolutionary technique owing to its low cost, flexibility, and quality data, the unmanned aerial vehicle (UAV)-based SfM photogrammetry has been widely applied in coastal areas. The conventional UAV photogrammetric accuracy significantly depends on the number and distribution of ground control points (GCPs), limiting its mapping efficiency. With the increasingly available UAVs with onboard RTK, photogrammetry without GCPs is becoming a promising alternative. However, the ability of this advanced RTK-assisted UAV to capture centimeter-scale elevation changes in intertidal mudflats still remains unclear. For this reason, this paper aims to evaluate the potential of RTK-assisted UAVs in quantifying intertidal topographic changes. The results showed that the RTK-assisted UAV structure-from-motion (SfM) photogrammetry without GCPs could accurately capture fine-scale topographical features such as mudflat gradient and creeks with root-mean-squared errors (RMSE) of ± 3.3 cm, ± 2.8 cm, and ± 4.7 cm on X-, Y-, and vertical directions, respectively. Therefore, this study identified that RTK-assisted UAV photogrammetry could be used to quantify intertidal mudflat morphodynamics and calculate sediment deposition volume within the uncertainty in a more cost-effective way.