Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are

Proglacial slopes provide suitable conditions for observing the co-development of abiotic and biotic systems. The frequency and magnitude of geomorphic processes and plant composition govern this interplay, which is described in the model of biogeomorphic succession. In high mountain environments, this model has only been tested in a limited number of studies. The study aimed to quantify small-scale sediment transport via erosion plots along a plant cover gradient and to investigate the influence of sediment transport on plant communities. We aimed to generate quantitative data to test existing biogeomorphic models. Small-scale biogeomorphic interactions were investigated on 30 test plots of 2 Â 3 m size on proglacial slopes of the Gepatschferner (Kaunertal) in the Austrian Alps during the snow-free summer months over three consecutive years. The experimental plots were established on slopes along a plant cover gradient. A detailed vegetation survey was carried out to capture biotic conditions, and specific sediment yield was measured at each plot.Species abundance and composition at each site reflected successional stages.Additional environmental parameters, such as terrain age, geomorphometry, grain size distribution, soil nutrients, and precipitation, were also included in the analyses. We observed two pronounced declines in geomorphic activity on plots with both above 30% and above 75% plant cover. Nonmetric multidimensional scaling showed distinct clusters of vegetation composition that mainly followed a successional gradient. Sites that were affected by high-magnitude geomorphic events showed different environmental conditions and species communities.Quantified process rates and observed species composition support the concept of biogeomorphic succession. The findings help to narrow down a biogeomorphic feedback window.

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius.

Soil erosion causes severe on- and off-site effects, including loss of organic matter, reductions in soil depth, sedimentation of reservoirs, eutrophication of water bodies, and clogging and smothering of spawning habitats. The involved sediment source-mobilization-delivery process is complex in space and time, depending on a multiplicity of factors that determine lateral sediment connectivity in catchment systems. Shortcomings of soil erosion models and connectivity approaches call for methodical improvement when it comes to assess lateral sediment connectivity in agricultural catchments. This study aims to (i) apply and evaluate different approaches, i.e., Index of Connectivity (IC), the Geospatial Interface for Water Erosion Prediction Project (GeoWEPP) soil erosion model, field mapping and (ii) test a connectivity-adapted version of GeoWEPP (i.e., “GeoWEPP-C”) in the context of detecting hot-spots for soil erosion and lateral fine sediment entry points to the drainage network in a medium-sized (138 km2) agricultural catchment in Austria, further discussing their applicability in sediment management in agricultural catchments. The results revealed that (a) GeoWEPP is able to detect sub-catchments with high amount of soil erosion/sediment yield that represent manageable units in the context of soil erosion research and catchment management; (b) the combination of GeoWEPP modeling and field-based connectivity mapping is suitable for the delineation of lateral (i.e., field to stream) fine sediment connectivity hotspots; (c) the IC is a useful tool for a rapid Geographic Information System (GIS)-based assessment of structural connectivity. However, the IC showed significant limitations for agricultural catchments and functional aspects of connectivity; (d) the process-based GeoWEPP-C model can be seen as a methodical improvement when it comes to the assessment of lateral sediment connectivity in agricultural catchments.

Measurements with an electronic Schmidt-hammer (RockSchmidt) were conducted on 23 sites of sorted stripes (periglacial patterned ground) on Juvflye, Jotunheimen (central South Norway). All were located above the current lower limit of alpine permafrost. Performing Schmidt-hammer exposure-age dating (SHD) based on application of a new local age-calibration equation for RRockvalues yielded SHD-ages between 7,975 ± 370 and 6,660 ± 355 years ago, which are closely comparable to results obtained previously from sorted circles at the same location. The age estimates are interpreted as 'composite' ages indicative of upfreezing of boulders, lateral sorting, and subsequent stabilisation. Formation of patterned ground essentially ceased with the onset of the regional Holocene Thermal Maximum (HTM). Neither sorted stripe sites at higher altitude, continuously underlain by permafrost during the entire Holocene, nor those at lower altitudes affected by re-aggradation of permafrost in the late Holocene show signs of significant recent morphodynamic activity. Likely explanations for early-to mid-Holocene stabilisation include (1) substantial changes of soil moisture conditions and related thermodynamics within the active layer affecting frost action, (2) loss of fine-grained substrate matrix from the coarse stripes and hence reduced frost susceptibility, and (3) exhaustion of supply of boulders from the fines-dominated areas. Whereas the sorted stripe data set as a whole did not reproduce the altitudinal gradient characteristic of sorted circles on Juvflye, the strength of the relationship between sorted stripe mean RRock-values and altitude increased with declining slope gradient. Although interpretation of SHD-ages for patterned ground remains challenging, this successful application of the electronic Schmidt-hammer, with its increased efficiency and technical improvements over the mechanical Schmidt-hammer, offers considerable potential for future SHD-studies in both morphodynamic and palaeoclimatic contexts.