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

Abstract

Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale.

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

“…Further, filtering is not as perfect as if performed by a human operator and additional filtering by geometrical filters might be necessary (for example, to remove brown branches that remain unfiltered based on the color). Still, employing this filter first greatly simplifies the use of geometrical filters, which would likely fail on such a rugged terrain with vegetation cover [27]. When dealing with terrain suitable for the proposed algorithm, we, therefore, suggest using first the method proposed in this paper to remove green points (which, outside of areas of human habitation, certainly capture vegetation), and subsequently, employing commonly used geometric or structural filtering, or even manually removing the relatively few remaining vegetation points.…”

“…It is worth noting that although mass data collection methods are currently very popular in many fields of research involving complex terrain [26], such as coastal monitoring [27][28][29], volcano exploration [30], underground research [31,32], tree detection [33,34], the evolution of rock glaciers [35], monitoring of rock masses [36][37][38], or their geological analysis [39], vegetation filtering in such cases is typically handled by human operators as the performance of automated algorithms proposed for this purpose, so far, is generally poor as confirmed by Blanco et al [6]. Specifically, they usually have problems when encountering highly rugged and/or sloped terrain [15].…”

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).…”

Evaluation of Active and Passive UAV-Based Surveying Systems for Eulittoral Zone Mapping

“…Most works that use UAVs, utilize photogrammetric-based solutions for the task. For example, Taddia et al (2020) and Bertin et al (2022) used a UAV-borne camera to image and map a coastal section. The authors used structure-from-motion multi-view stereo (SfM-MVS) image matching techniques to generate photogrammetric point clouds.…”