The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV) The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy) The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys Such a comparison served to highlight an average difference in the vertical values of 005 m (RMS = 019 m) However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0011 m) The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0015 m, with RMS = 0220 m Larger values are encountered in areas where sudden changes in topography are present The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology

https://www.mdpi.com/2072-4292/5/12/6880/pdf

Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

3D point clouds of natural environments relevant to problems in geomorphology (rivers, coastal environments, cliffs, ...) often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology (e.g. the presence of bedforms or by grain size). Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point. The dimensionality characterizes the local 3D organization of the point cloud within spheres centered on the measured points and varies from being 1D (points set along a line), 2D (points forming a plane) to the full 3D volume. By varying the diameter of the sphere, we can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98%. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution of the classification. Scenes between 10 and one hundred million points can be classified on a common laptop in a reasonable time. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification result is given at each point, allowing the user to remove the points for which the classification is uncertain. The process can be both fully automated (minimal user input once, all scenes treated in large computation batches), but also fully customized by the user including a graphical definition of the classifiers if so desired. Working classifiers can be exchanged between users independently of the instrument used to acquire the data avoiding the need to go through full training of the classifier. Although developed for fully 3D data, the method can be readily applied to 2.5D airborne lidar data.

https://hal-insu.archives-ouvertes.fr/insu-00700970/document

3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology

3D point clouds of natural environments relevant to problems in geomorphology often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology. Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point, which characterizes the local 3D organization. We can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98 %. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification result is given at each point, allowing the user to remove the points for which the classification is uncertain. The process can be both fully automated, but also fully customized by the user including a graphical definition of the classifiers. Although developed for fully 3D data, the method can be readily applied to 2.5D airborne lidar data.

3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology

Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model

The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization.

Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods

Ephemeral aeolian sand strips are commonplace on beaches. Their formation during high energy sand transport events often precedes the development of protodunes and their dynamics present interesting feedback mechanisms with surface moisture patterns. However, due to their temporary nature, little is known of their formation, mobility or the specifics of their interaction with beach surface characteristics. Similarly surface moisture has an important influence on sediment availability and transport in aeolian beach systems, yet it is difficult to quantify accurately due to its inherent variability over both short spatial and temporal scales. Whilst soil moisture probes and remote sensing imagery techniques can quantify large changes well, their resolution over mainly dry sand, close to the aeolian transport threshold is not ideal, particularly where moisture gradients close to the surface are large. In this study we employed a terrestrial laser scanner to monitor beach surface moisture variability during a three and a half hour period after a rain event and investigated relationships between bedform development, surface roughness and surface moisture. Our results demonstrate that as the beach surface dries, sand transport increases, with sediment erosion occurring at the wet/dry surface boundary, and deposition further downwind. This dynamic structure, dependent upon changing surface moisture characteristics, results in the formation of a rippled sand strip and ultimately a protodune. Our findings highlight dynamic mobility relationships and confirm the need to consider transient bedforms and surface moisture across a variety of scales when measuring aeolian transport in beach settings. The terrestrial laser scanner provides a suitable apparatus with which to accomplish this.

Aeolian sand strip mobility and protodune development on a drying beach: examining surface moisture and surface roughness patterns measured by terrestrial laser scanning

Robert S. Squirrell

Papers

Aeolian sand strip mobility and protodune development on a drying beach: examining surface moisture and surface roughness patterns measured by terrestrial laser scanning