Umberto Morra di Cella

Accurate Land Use and Land Cover (LULC) mapping in high-resolution alpine environments is challenging due to complex terrain, heterogeneous vegetation, seasonal snow and ice cover, and the limited spectral information provided by conventional aerial imagery. Although foundation models such as the Segment Anything Model (SAM) effectively capture structural features, their class-agnostic design, limits fine-grained semantic discrimination and typically requires large annotated datasets. This study proposes a multi-modal deep learning framework for alpine LULC mapping using sparse annotations, which would fall under the category of weakly supervised learning. The framework employs a dual-encoder architecture that integrates RGB imagery, six-band multispectral imagery, and custom adapters for spectral indices, and Digital Surface Models (DSMs). A SAM-based encoder extracts geometric and contextual features from RGB imagery, while a dedicated encoder learns complementary spectral representations from multispectral data. To address the boundary uncertainty introduced by sparse supervision, we propose post inference hybrid refinement strategy that combines a Canopy Height Model (CHM) derived from the DSM to improve tree crown delineation with edge-based refinement for low vegetation classes, such as shrubs, and mathematical methods to refine road and building edge delineation with DSMs. Experimental results across fourteen alpine classes highlight that the framework achieves a validation mIoU of 0.777, macro-averaged over the fourteen classes. For the present sensor choice and classification scheme, no comparable multi-modal baseline exists. Thus, results are reported in absolute terms. The present multi-modal data fusion sets the baseline for future scalable alpine LULC mapping applications.

Recent changes in alpine vegetation are often attributed to climate warming, particularly community composition shifts towards more warmth-associated species, or thermophilization. Here we assess the link between thermophilization and warming across 53 European mountain summits. We combine long-term macroclimatic and microclimatic temperature time series with vegetation surveys in 724 permanent plots, monitored over 21 years, to evaluate a possible thermophilization signal and relate it to rates of change in 10 temperature metrics. We find evidence of both thermophilization of alpine plant communities and an increase in temperatures. However, although these two trends are related when averaged across mountain regions, their relationship is weak at the individual plot scale, especially when considering microclimatic temperature metrics. Instead, substrate conditions and particularly the availability of thermophilic colonizers in the surrounding vegetation have a major influence on plot-level thermophilization rates. We conclude that the response of plant communities to climate change strongly depends on the abiotic and biotic context, and intensified monitoring efforts are needed to reduce the resulting uncertainties.

Alpine glaciers are a vital resource for mountain regions. They provide water reserves, support energy production and tourism, and promote biodiversity. However, they are highly susceptible to climate change. In fact, they are recognised as being among the areas most affected by, and increasingly exposed to, natural hazards. The Rutor glacier in Aosta Valley, Italy, which has been the subject of repeated measurements since the 19th century and currently covers an area of around 8 km², is undergoing significant and continuous retreat. It thus serves as an exemplary case study of the impact of climate change on the Italian Alps. This ongoing research has made it possible to conduct multi-temporal analysis of the glacier. Within this framework, Politecnico di Torino, in collaboration with ARPA Valle d’Aosta, has developed a multidisciplinary research approach focused on the characterisation of alpine environments. This study illustrates the geomatic workflows and derived geospatial products that can be used to carry out a 4D monitoring of the extent and volume of the Rutor Glacier and estimate its mass balance over the past six years. A specific focus of the study is the propagation of errors in multi-temporal analyses used to quantify glacier melt, with particular attention to the precision of input 3D geospatial data and to the Limit of Detection of elevation differences, ultimately enabling the estimation of the uncertainty associated with the derived quantities and their temporal trends. Finally, advantages and limitations in the multi-temporal and multi-sensor monitoring of glaciers are presented and discussed.

Large woody debris (LWD), a key indicator of riparian vegetation disturbance and river corridor dynamic, plays a crucial role in habitat complexity, geomorphic dynamics and river management. Accurate mapping and monitoring of LWDs are therefore essential for river process analysis and ecosystem assessment, particularly in highly dynamic braided river systems. However, mapping and monitoring LWD remains challenging due to its variable morphology, spectral similarity, and dynamics of braided river. Advancements in artificial intelligence (AI) and unmanned aerial vehicle (UAV) remote sensing offer promising opportunities for addressing these applied geoscience challenges. In this study, we evaluate different AI techniques for the accurate detection of LWD in braided rivers. Specifically, using RGB-UAV imagery, we test two DL models, U-Net and DeepLabv3+, and compare them to other classifiers to identify the most accurate and transferable approach. The results indicate that the DeepLabv3+ method effectively captures the actual spatial distribution of LWD, and two-class classifications were more efficient than multi-class ones. Furthermore, the DL model demonstrated strong transferability when applied to a different spatiotemporal area, highlighting its utility for applied geoscience investigations and river management.

This is a correction for Kellerer-Pirklbauer et al (2024 Environ. Res. Lett. 19 034022).

Aims: We introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.Results: ReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.Conclusions: ReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.

Cryospheric long-term timeseries get increasingly important. To document climate-related effects on long-term viscous creep of ice-rich mountain permafrost, we investigated timeseries (1995–2022) of geodetically-derived Rock Glacier Velocity (RGV), i.e. spatially averaged interannual velocity timeseries related to a rock glacier (RG) unit or part of it. We considered 50 RGV from 43 RGs spatially covering the entire European Alps. Eight of these RGs are destabilized. Results show that RGV are distinctly variable ranging from 0.04 to 6.23 m a⁻¹. Acceleration and deceleration at many RGs are highly correlated with similar behaviour over 2.5 decades for 15 timeseries. In addition to a general long-term, warming-induced trend of increasing velocities, three main phases of distinct acceleration (2000–2004, 2008–2015, 2018–2020), interrupted by deceleration or steady state conditions, were identified. The evolution is attributed to climate forcing and underlines the significance of RGV as a product of the Essential Climate Variable (ECV) permafrost. We show that RGV data are valuable as climate indicators, but such data should always be assessed critically considering changing local factors (geomorphic, thermal, hydrologic) and monitoring approaches. To extract a climate signal, larger RGV ensembles should be analysed. Criteria for selecting new RGV-sites are proposed.