Abstract
Recent advances in structure-from-motion (SfM) techniques have proliferated the use of unmanned aerial vehicles (UAVs) in the monitoring of coastal landform changes, particularly when applied in the reconstruction of 3D surface models from historical aerial photographs. Here, we explore a number of depth map filtering and point cloud cleaning methods using the commercial software Agisoft Metashape Pro to determine the optimal methodology to build reliable digital surface models (DSMs). Twelve different aerial photography-derived DSMs are validated and compared against light detection and ranging (LiDAR)- and UAV-derived DSMs of a vegetated coastal dune system that has undergone several decades of coastline retreat. The different studied methods showed an average vertical error (root mean square error, RMSE) of approximately 1 m, with the best method resulting in an error value of 0.93 m. In our case, the best method resulted from the removal of confidence values in the range of 0–3 from the dense point cloud (DPC), with no filter applied to the depth maps. Differences among the methods examined were associated with the reconstruction of the dune slipface. The application of the modern SfM methodology to the analysis of historical aerial (vertical) photography is a novel (and reliable) new approach that can be used to better quantify coastal dune volume changes. DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyse long-term geomorphic changes in coastal dune areas that have undergone retreat.
Highlights
The reliability of three-dimensional data is a crucial component in the physical analysis of exogenous geomorphic processes
In our case, performing a good dense point cloud (DPC) cleaning process (0–3 confidence values removed from the DPC) was far more important than the filter adopted for the depth map reconstruction
Despite small differences in the computed Z errors were found among the adopted methods (RMSE values: 0.93–1.17 m), the largest disadvantage emerged from the removal of points from the DPC with confidence values ranging between 0 and 3, which resulted in a consistent restriction of the modeled area (Figure 7d)
Summary
The reliability of three-dimensional data is a crucial component in the physical analysis of exogenous geomorphic processes. These types of data can be gathered much more accurately thanks to recent improvements in and accessibility of modern light detection and ranging (LiDAR), differential global navigation satellite system (DGNSS), and unmanned aerial vehicle (UAV) systems [1]. The high frequency and dense spatial accuracy provided by modern topographic acquisition systems such as UAVs is still not able to provide historical (multi-decadal) datasets due to their relatively recent (previous 10 years) widespread availability. Reliable long-term 3D data would help improve geomorphic predictions such as shoreline variation models [4,5], and would
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
