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Since the end of the Little Ice Age (LIA, ~1830), the accelerated glaciers' shrinkage along mid-latitude high mountain areas promoted a quick readjustment of geomorphological processes with the onset of the paraglacial dynamic, making proglacial areas among the most sensitive Earth's landscapes to ongoing climate change. A potentially useful remote-sensing method for investigating such dynamic areas is the DEM (Digital Elevation Model) of Difference (DoD) technique, which quantifies volumetric changes in a territory between successive topographic surveys. After a detailed geomorphological analysis and comparison with historical maps of the Martello Valley (central Italian Alps), we applied the DoD for reconstructing post-LIA deglaciation dynamics and reported on the surface effects of freshly-onset paraglacial processes. The head of the valley is still glacierized, with three main ice bodies resulting from the huge reduction of a single glacier present at the apogee of the LIA. Aftermath: the glaciers lose 60% of their initial surface area, largely modifying local landforms and expanding the surface of the proglacial areas. The DoD analysis of the 2006–2015 timeframe (based on registered DEM derived from LiDAR—Light Detection and Ranging—data) highlights deep surface elevation changes ranging from +38 ± 4.01 m along the foot of rock walls, where gravitative processes increased their intensity, to −47 ± 4.01 m where the melting of buried ice caused collapses of the proglacial surface. This approach permits estimating the volume of sediments mobilized and reworked by paraglacial processes. Here, in less than 10 years, −23,675 ± 1165 m3 of sediment were removed along the proglacial area and transported down valley, highlighting the dynamicity of proglacial areas. [ABSTRACT FROM AUTHOR]

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems. [ABSTRACT FROM AUTHOR]

Documenting glacier surface flow velocity from a longer-term perspective is highly relevant to evaluate the past and current state of glaciers worldwide. For this purpose, satellite data are widely used to obtain region-wide coverage of glacier velocity data. Well-established image correlation methods allow for the automated measurement of glacier surface displacements from satellite data (optical and radar) acquired at different dates. Although computationally expensive, image correlation is nowadays relatively simple to implement and allows two-dimensional displacement measurements. Here, we present a data set of annual glacier surface flow velocity maps at the European Alps scale, covering the period 2015–2021 at a 50 m × 50 m resolution. This data set has been quantified by applying the normalized cross-correlation approach on Sentinel-2 optical data. Parameters of the cross-correlation method (e.g., window size, sampling resolution) have been optimized, and the results have been validated by comparing them with in situ data on monitored glaciers showing an RMSE of 10 m/yr. These data can be used to evaluate glacier dynamics and its spatial and temporal evolution (e.g., quantify mass fluxes or calving) or can be used as an input for model calibration/validation or for the early detection of regional hazards associated with glacier destabilization. Dataset:  https://doi.org/10.57745/XHQ7TL Dataset License: CC BY NC 4.0 [ABSTRACT FROM AUTHOR]

High-quality in situ measurements are essential for hazard assessment of debris flow events. However, precise data on debris flow triggering thresholds, accumulation volumes and spatial characteristics of large-scale events on catchment scale are scarce due to the rare occurrence of debris flows and the challenges of acquiring accurate data for a larger area. In this study, we present quantitative analyses of a single extreme debris flow event in the Horlachtal, Austria, triggered by local high-intensity short-duration precipitation events on 20 and 23 July 2022. Pre- and post-event airborne LiDAR (light detection and ranging) data with a high spatial resolution reveal that 156 different debris flow processes were initiated during these events, with accumulation volumes of up to approximately 40,000 m³. The calculated debris flow deposition volumes also show a power-law relationship with the total amount of rainfall in the respective debris flow catchments. The spatial appearance of the debris flows shows a concentration of processes in a particular area rather than a uniform distribution, suggesting a local nature of the triggering event. This is further supported by the measurements from three meteorological stations and four discharge gauges within the study area. The gridded area-wide INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall data further point to a local convective event on 20 July 2022, with a maximum rainfall intensity of 44 mm/h. [ABSTRACT FROM AUTHOR]

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies. [ABSTRACT FROM AUTHOR]