Google Earth Engine has deeply changed the way in which Earth observation data are processed, allowing the analysis of wide areas in a faster and more efficient way than ever before. Since its inception, many functions have been implemented by a rapidly expanding community, but none so far has focused on the computation of phenological metrics in mountain areas with high-resolution data. This work aimed to fill this gap by developing an open-source Google Earth Engine algorithm to map phenological metrics (PMs) such as the Start of Season, End of Season, and Length of Season and detect the Peak of Season in mountain areas worldwide using high-resolution free satellite data from the Landsat collection and Sentinel-2. The script was tested considering the entire Alpine chain. The validation was performed by the cross-computation of PMs using the R package greenbrown, which permits land surface phenology and trend analysis, and the Moderate-Resolution Imaging Spectroradiometer (MODIS) in homogeneous quote and land cover alpine landscapes. MAE and RMSE were computed. Therefore, this algorithm permits one to compute with a certain robustness PMs retrieved from higher-resolution free EO data from GEE in mountain areas worldwide.

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.

Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LC_b). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LC_b, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration.

The widespread diffusion of the wild boar on the Italian territory and its consistent use for hunting have created the possibility to conduct multiple studies on the pathologies afflicting this ungulate. Nevertheless, in the last two decades, only some pathologies such as classical and African Swine Fever, Tuberculosis, Brucellosis from Brucella suis have benefited from substantial public funding and the consequent great interest from the scientific world, while less attention was addressed to parasitic diseases including sarcoptic mange. Therefore, to fill this gap, the purpose of this study was to contribute to the knowledge of sarcoptic mange in the wild boar population in Aosta Valley in the Northwest of Italy, including sympatric species as foxes. Due to past field surveys, it has been possible to find a possible role of snow metrics in the spread of this pathogen. Even if there are only empirical evidence and the mechanism remain unknown remote sensing analysis considering snow metrics were performed to provide to veterinarians, foresters, biologists, and ecologists new tools to better understand wield board dynamics and join to ordinary tool an instrument to enhance management and planning strategies. The snow metrics (SM) were derived from USGS NASA Landsat 8 L2A retrieved from Theia CNES platform and processed in Orfeo Toolbox LIS extension package. The relationship between SM and the disease spread was tested per each Aosta Valley municipality obtaining LISA maps for each hunting season. The results have showed that this parasite is present in an endemic form even if with rather low prevalence values, equal to 1.2% in the season hunting season 2013/2014, and equal to 7.5% in the hunting season 2014/2015. Moreover, within simultaneous given values of SM, sarcoptic mange seem to find good conditions for spreading.

Wildlife can represent a reservoir of zoonotic pathogens and a public health problem. In the present study, we investigated the spread of zoonotic pathogens (Salmonella spp., Yersinia enterocolitica, Listeria monocytogenes, Shiga-toxin-producing Escherichia coli (STEC), and hepatitis E virus (HEV)) considering the presence of virulence and antibiotic resistance genes in game meat from animals hunted in northwest Italy. During two hunting seasons (2020 to 2022), samples of liver and/or muscle tissue were collected from chamois (n = 48), roe deer (n = 26), deer (n = 39), and wild boar (n = 35). Conventional microbiology and biomolecular methods were used for the detection, isolation, and characterization of the investigated pathogens. Two L. monocytogenes serotype IIa strains were isolated from wild boar liver; both presented fosfomycin resistance gene and a total of 22 virulence genes were detected and specified in the text. Eight Y. enterocolitica biotype 1A strains were isolated from chamois (2), wild boar (5), and deer (1) liver samples; all showed streptogramin and beta-lactam resistance genes; the virulence genes found were myfA (8/8 strains), ymoA (8/8), invA (8/8), ystB (8/8), and ail (4/8). Our data underscore the potential role of wildlife as a carrier of zoonotic and antibiotic-resistant pathogens in northwest Italy and a food safety risk for game meat consumers.