Accuracy of the UAV-Based DEM of Beach–Foredune Topography in Relation to Selected Morphometric Variables, Land Cover, and Multitemporal Sediment Budget

Estuaries and Coasts https://doi.org/10.1007/s12237-020-00752-x Accuracy of the UAV-Based DEM of Beach–Foredune Topography in Relation to Selected Morphometric Variables, Land Cover, and Multitemporal Sediment Budget Joanna Rotnicka 1 2 2 3 1 & Maciej Dłużewski & Maciej Dąbski & Mirosław Rodzewicz & Wojciech Włodarski & Anna Zmarz 4 Received: 17 July 2019 / Revised: 12 January 2020 / Accepted: 23 April 2020 # The Author(s) 2020 Abstract Recent developments in unmanned aerial vehicles (UAVs) have resulted in high-resolution digital elevation models (DEMs) of vulnerable coastal environments, including beach–foredune topography. If performed repetitively, they can offer an excellent tool to determine the spatial and temporal changes in the sediment budget, which may be required for proper land management. However, the quality of a UAV, slope parameters, and vegetation significantly influence DEM accuracy. The aim of this study is to compare precise GPS-RTK transects across a section of the South Baltic coast in Poland with those obtained from a DEM based on high-resolution and high-accuracy images obtained by a wind-resistant, high-quality fixed-wing UAV during beyond visual line of sight operation (BVLOS). Different land cover classes, slope inclination, and general curvature, as well as surface roughness, were taken into consideration as possible factors influencing the uncertainty. The study revealed that marram grass greatly affects the accuracy of the UAV-derived model and that the uncertainty of the UAV-derived DEM increases together with increasing slope inclination and, to a lesser degree, with increasing general slope curvature. We showed that sediment budget determinations with the use of a UAV-based DEM are correct only where grass cover is sparse, in our study, up to 20% of the area. Keywords UAV imaging . DEM accuracy . Beach–foredune topography . Sediment budget Introduction Foredunes are characterized by diversified relief with annual changes ranging between a few centimeters and 1–2 m (Nolet et al. 2018). High-resolution digital elevation models (DEMs) Communicated by Nancy L. Jackson * Joanna Rotnicka 1 Institute of Geology, Faculty of Geographical and Geological Sciences, Adam Mickiewicz University in Poznań, ul. Bogumiła Krygowskiego 12, 61-680 Poznań, Poland 2 Department of Geomorphology, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmieście 30, 00-927 Warsaw, Poland 3 Faculty of Power and Aeronautical Engineering, Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, ul. Nowowiejska 24, 00-665 Warsaw, Poland 4 Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmieście 30, 00-927 Warsaw, Poland of foredunes repetitively performed offer an excellent opportunity to determine the spatial and temporal distribution of sediment budgets of such dynamic eolian landforms. Some previous studies have been performed to estimate a foredune sediment budget based on elevation data surveyed with GPS-RTK along transects oriented perpendicular to the coastline and crossing foredune ridges (Vespremeanu-Store and Preoteasa 2007; Delgado-Fernandez 2011), sometimes together with the analysis of aerial photography (Rader et al. 2018). Recently, the sediment budget has also been determined using multitemporal sets of LIDAR-derived DEMs (Keijsers et al. 2014; Darke et al. 2016; Le Mauff et al. 2018) or with the use of aerial images taken by rotary-wing light UAVs. However, such vehicles have limited wind resistivity (Nolet et al. 2018) and can only be used for small surfaces close to the take-off and landing sites under calm weather. Unmanned aerial vehicles (UAVs) allow the acquisition of high-resolution DEMs of vulnerable coastal environments, including beach–foredune topography. If performed repetitively, they can offer an excellent tool to determine the spatial and temporal changes in the sediment budget, which may be Estuaries and Coasts required for proper land management. The UAV-based photogrammetry of coastal areas has already proven to be a useful technique for scientific studies as well as for authorities to develop local management strategies (Scarelli et al. 2017). However, there are numerous disturbing factors responsible for DEM inaccuracy, most importantly, the quality of UAVs, slope inclination, and vegetation. The quality of DEMs is related to the spatial distribution and accuracy of the input elevation data. For example, LIDAR data can achieve an accuracy of 0.2 m root mean square error (RMSE) horizontally and less than 0.15 m vertically (Liu 2008). Thus, LIDAR-derived DEMs can display an optimal horizontal spatial resolution of 0.5 m, which is insufficient to detect small-scale geomorphic features (Taroli et al. 2012; Leon et al. 2014; Taroli 2014; Fabbri et al. 2017). In turn, UAVbased DEMs usually have vertical accuracy in the range of approximately 0.05 to 0.1 m and spatial resolution of approximately 0.1 m (Mancini et al. 2013; Laporte-Fauret et al. 2019). However, the accuracy of such DEMs is uncertain because the only benchmarks are ground control points installed in selected places. On the other hand, in-field GPS-RTK measurements provide unequivocal results, but they are usually performed along transect lines imposed by survey team protocols. The spatial resolution and vertical accuracy of the input elevation data and derived DEMs influence the accuracy of detailed geomorphological mapping and the determination of landform sediment budgets (Wheaton et al. 2009; Coveney and Fotheringham 2011; Leon et al. 2014; Le Mauff et al. 2018). Moreover, there is still a poorly known role of the sampling strategy in the input elevation data, the composition of the bare earth surface and the landcover type, topographic complexity, and spatial interpolation methods applied in DEM processing (Hodgson and Bresnahan 2004; Wechsler and Kroll 2006; Bater and Coops 2009). The related DEM uncertainty cannot be simply defined as a single global parameter of dispersion of a difference between predicted and measured ground elevation, i.e., RMSE (Oksanen and Sarjakoski 2006). This is because the uncertainty can be spatially variable depending on land cover type (Hodgson and Bresnahan 2004; Schmid et al. 2011; Zandbergen 2011; Leon et al. 2014) and terrain characteristics such as slope inclination, slope curvature, or surface roughness (Su and Bork 2006; Erdogan 2010; Oksanen and Sarjakoski 2006). Therefore, the proper characterization of the uncertainty, the recognition of its distribution, and spatial correlation structure provide a good basis for realistic DEM uncertainty propagation analysis (Shortridge 2001). Uncertainty is an inherent feature of any spatial data (Leon et al. 2014), but new technologies allow researchers to maximize the precision of geomorphological measurements. Conventional aeri (...truncated)