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Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries–Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective. [ABSTRACT FROM AUTHOR]

Land cover (LC) maps are crucial to environmental modeling and define sustainable management and planning policies. The development of a land cover mapping continuous service according to the new EAGLE legend criteria has become of great interest to the public sector. In this work, a tentative approach to map land cover overcoming remote sensing (RS) limitations in the mountains according to the newest EAGLE guidelines was proposed. In order to reach this goal, the methodology has been developed in Aosta Valley, NW of Italy, due to its higher degree of geomorphological complexity. Copernicus Sentinel-1 and 2 data were adopted, exploiting the maximum potentialities and limits of both, and processed in Google Earth Engine and SNAP. Due to SAR geometrical distortions, these data were used only to refine the mapping of urban and water surfaces, while for other classes, composite and timeseries filtered and regularized stack from Sentinel-2 were used. GNSS ground truth data were adopted, with training and validation sets. Results showed that K-Nearest-Neighbor and Minimum Distance classification permit maximizing the accuracy and reducing errors. Therefore, a mixed hierarchical approach seems to be the best solution to create LC in mountain areas and strengthen local environmental modeling concerning land cover mapping. [ABSTRACT FROM AUTHOR]

Hydrogeological hazards are increasingly causing damage worldwide due to climatic and socio-economic changes. Building resilient communities is crucial to reduce potential losses. To this end, one of the first steps is to understand how people perceive potential threats around them. This study aims at exploring how risk awareness of, and preparedness to, face hydrological hazards changes over time. A cohort study was carried out in two villages in the northeastern Italian Alps, Romagnano and Vermiglio, affected by debris flows in 2000 and 2002. Surveys were conducted in 2005 and 2018, and the results compared. The survey data show that both awareness and preparedness decreased over time. We attribute this change to the fact that no event had occurred in a long time and to a lack of proper risk communication strategies. The outcomes of this study contribute to socio-hydrological modelling by providing empirical data on human behaviour dynamics. [ABSTRACT FROM AUTHOR]

Hydrogeological hazards are increasingly causing damage in many regions of the world due to climatic and socio-economic changes. When it comes to disaster risk reduction at the local level, building a more resilient community is crucial to reduce potential losses. One of the first steps in promoting community resilience is to understand how people perceive potential threats around them. This study aims at exploring how risk perception of hydrological hazards changes over time. Two villages in the North-eastern Italian Alps – Romagnano and Vermiglio – are analysed. These two areas were affected by heavy rainfall events that caused debris flows in 2000 (both in Romagnano and Vermiglio) and 2002 (in Vermiglio only). A few years after these events, in 2005, surveys were conducted in both areas by Scolobig et al. (2012) to assess the non-trivial relationship between risk awareness and preparedness. The authors found that, even when individuals are aware of the hazard risk, they are not necessarily prepared to face it. To unravel the potential changes in risk perception over time, we conducted a longitudinal study in 2018, which builds on the questionnaire that was administered in 2005. Given the absence of extreme events for almost twenty years, we test the hypothesis that risk perception has significantly reduced. We repeated part of the original survey in both areas and compared the results to the outcomes of the 2005 survey. In addition, qualitative data collected through semi-structured interviews with local authorities complement the surveys and provide key information on changes in the political contexts, risk communication strategies, and demography at the community level. A preliminary analysis of survey data does not show major changes in the level of awareness, potentially challenging the hypothesised relationship between risk perception and time, but only an in-depth statistical analysis will provide additional insights to unravel this counter-intuitive result. Apart from highlighting factors potentially affecting risk perception, the results of this study also inform the design of future longitudinal surveys in the hazards risk perception field. Both outcomes can guide the study of socio-hydrological systems, inform risk assessment methods, and contribute to the development of better policies for disaster risk reduction. [ABSTRACT FROM AUTHOR]