Monthly Archives: September 2020

Drone-based laser scanning for erosion monitoring

Fig. 1: ULS system set up and flight at a 48-ha test site in the Dolomites (Italy).

Within the ERODYN project, we are using a drone (the RIEGL RiCOPTER system) equipped with the VUX-1LR laser scanner to produce high resolution 3D point clouds and derive detailed digital terrain models for automated geomorphological analyses. The aim is to detect and quantify changes at eroded areas by repeat scans. Such unmanned aerial vehicle laser scanning systems (ULS) are a relatively new technology, and tests in mountain areas are scarce. Thus, questions arise regarding ULS applicability in mountainous settings, the achievable accuracy of the data, and its potential for shallow erosion activity mapping. Recent investigations of the ERODYN project show promising results and, despite some limitations, changes at (sub-)decimetre level can be reliably observed.

Read more:

Mayr, A.; Bremer, M.; Rutzinger, M. (2020): 3D point errors and change detection accuracy of unmanned aerial vehicle laser scanning data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2020, 765 – 772.

Mayr, A.; Bremer, M.; Rutzinger, M.; Geitner, C. (2019): Unmanned aerial vehicle laser scanning for erosion monitoring in Alpine grassland. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5, 405 – 412.

Fig. 2: Subset of the test site with surface deformation (3D point cloud distance) due to secondary erosion and deposition of eroded material. (a) Planimetric view of the point cloud coloured by deformation exceeding the LOD95 (level of detection at the 95%-confidence interval), with RGB coloured point cloud, shaded relief, and 5-m contours as background. (b) Oblique view of the RGB-coloured point cloud subset. (c) Oblique view of the point cloud subset coloured by deformation exceeding the LOD95.

Fig. 3: The level of detection (at the 95%-confidence interval; LOD95) indicates the magnitude of observable changes using multitemporal point clouds. The LOD95 is partly related to the registration error and the surface roughness. At some points, additional uncertainties related to laser footprint effects (modelled as a function of range, incidence angle, and beam divergence) affect the LOD95 negatively. Removing such erroneous points with a point error threshold improves the LOD95; though this comes at the cost of increasingly incomplete point clouds (depending on the threshold).

Our research groups at the University of Innsbruck:

Remote Sensing & Topographic LiDAR Research Group,
Soil and Landscape Ecology Research Group,

Some hardware and software used:

Software used for flight planning: UgCS,
Software for 3D point cloud processing: SAGA LIS,

How does vegetation influence the susceptibility of grassland slopes towards shallow erosion?

Vegetation plays (amongst other factors) an important role for the stability of slopes near their surface. Not only woody vegetation (i.e. shrubs and trees) but also herbaceous vegetation (dominating grasslands) can enhance the resistance of soil against shallow erosion. Being to some extent controllable via land management (in contrast to other factors, such as topography or the geological setting), vegetation and its impact on slope stability are of high interest for erosion prevention.

In a recent literature review in the journal Earth-Science Reviews, members of the ERODYN team summarize the current knowledge on this topic, and critically discuss the slope stabilization potential of herbaceous vegetation as compared to woody vegetation.

A second paper, just published in the Journal of Environmental Management, seeks to provide insights into the (surface-parallel) tensile strength of the topsoil in subalpine grasslands (< 10 cm depth). In an empirical study, tensile strength was measured in the field, and analysed with regard to potential impacts of soil and vegetation parameters. It turned out that densely interwoven roots and clonal structures often form a surprisingly strong “surface mat” as a small-scale reinforcement of the topsoil, possibly being important for the redistribution of stress. This surface-mat effect depends on the vegetation composition, and certain species provide stronger reinforcement than others. Species with well-developed root systems and a high capacity for clonal growth seem to be most advantageous, but also a balanced nitrogen supply and the plant and structural diversity appear to play a role.

Read more:

Löbmann, M.T.; Geitner, C.; Wellstein, C.; Zerbe, S. (2020): The influence of herbaceous vegetation on slope stability – A review. Earth-Science Reviews, 209, 103328.

Löbmann, M.T.; Tonin, R.; Stegemann, J.; Zerbe, S.; Geitner, C.; Mayr, A.; Wellstein, C. (2020): Towards a better understanding of shallow erosion resistance of subalpine grasslands. Journal of Environmental Management, 276, 111267.

For details on the measurement of the surface-mat effect, see also a previous study performed in montane grasslands: Löbmann et al. 2020, Catena.