Luigi Ranghetti

Mapping vegetation phenology in the high Arctic is critical for understanding ecosystem responses to rapid warming, but persistent cloud cover and low solar angles pose substantial challenges for optical satellite monitoring. We evaluated whether Landsat 8 can successfully map the start of growing season (SOS) at 78°N in Svalbard by developing spectral-based cloud detection algorithms tailored to Arctic conditions. From 17 images during the 2014 growing season, we generated cloud-free NDVI time series and extracted SOS dates at 30-m resolution across Adventdalen (Advent Valley). Validation against six field plots equipped with phenocameras demonstrated strong agreement (R² = 0.884, RMSE = 4.1 days, NSE = 0.869). The resulting SOS map revealed systematic spatial patterns ranging from late May to early August, with elevation explaining 6.6% of variance as the strongest single predictor. Combined topographic models (elevation, slope, distance from sea) explained 14.6% of variance, indicating that unmeasured factors particularly aspect, snow distribution, and vegetation composition exert dominant controls on phenological timing. These results confirm the viability of Landsat-based phenology mapping in the high Arctic. Critically, by establishing a validated processing framework this study provides the methodological foundation for application to the historical Landsat archive (Landsat 4/5 TM and Landsat 7 ETM+), subject to cross-sensor radiometric calibration. This opens unprecedented opportunities for reconstructing 30-m resolution phenological time series extending back to 1984, enabling quantification of four decades of vegetation responses to Arctic warming in regions experiencing some of Earth’s most rapid climate change.

Climate affects seasonality and plant phenology, which can influence seasonal body mass dynamics of herbivores in temperate environments. We investigated long‐term trends of seasonal body mass changes in male Alpine ibex Capra ibex . We used SEM to test direct and indirect relationships between body mass, mass changes and environmental and climatic variables. Individually recognizable Alpine ibex were weighed repeatedly between 2000 and 2022 in Gran Paradiso National Park (Italy). Autumn mass increased substantially over these two decades, up to 15% in some age classes. Over the same time frame, both summer mass gain and winter mass loss decreased, suggesting that heavier autumn body mass was due to the cumulative effects of reduced mass loss over several winters. The environmental factor with the strongest effects on winter mass changes was the starting date of vegetation green‐up at low altitude, where ibex gather after winter to feed on new growth vegetation. Early springs led to lower winter mass loss, likely because ibex relied on stored fat for a shorter period and had greater access to forage. High population density also increased winter mass loss. Environmental conditions and resource availability, possibly also influenced by density in winter and early spring, seem therefore to directly affect the body mass dynamics of male Alpine ibex, while the effect of summer conditions appears less relevant. By affecting seasonal body mass dynamics, climate change may have consequences for life history and population dynamics of mountain herbivores, for example via earlier access of young males to reproduction.

Non-photosynthetic vegetation (NPV) plays a key role in soil conservation, which in turn is important in sustainable agriculture and carbon farming. For mapping NPV image spectroscopy proved to outperform multispectral sensors. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is the forerunner of a new era of hyperspectral satellite missions, providing the proper spectral resolution for NPV mapping. This study takes advantage from both spectroscopy and machine-learning techniques. Exponential Gaussian Optimization was used for modelling known absorption bands (cellulose-lignin, pigments, water content and clays), resulting in a reduced feature space, which is split by a decision tree (DT) for mapping different field conditions (emerging, green and standing dead vegetation, crop residue and bare soil). DT training and validation exploited reference data, collected during PRISMA overpasses on a large farmland. Mapping results are accurate both at pixel and parcel level (O.A. > 90%; K > 0.9). Field status and crop rotation trajectories through time are derived by processing 12 images over 2020 and 2021. Results proved that PRISMA data are suitable for mapping field conditions at parcel scale with high confidence level. This is important in the perspective of other hyperspectral missions and is a premise toward quantitative estimates of NPV biophysical variable.

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R² = 0.82 and R² = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

Extrinsic and intrinsic factors may influence the activity budget of wild animals, resulting in a variation in the time spent in different activities among populations or individuals of the same species. In this study, we examined how extrinsic and intrinsic factors affect the behaviour of the alpine marmot (Marmota marmota), a hibernating social rodent inhabiting high-elevation prairies in the European Alps. We collected behavioural observations during scan sampling sessions on marked individuals at two study sites with different environmental characteristics. We used Bayesian hierarchical multinomial regression models to analyse the influence of both intrinsic (sex and age-dominance status) and extrinsic (environmental and climatic variables) factors on the above-ground activity budget. Marmots spent most of their time above ground foraging, and were more likely to forage when it was cloudy. Extrinsic factors such as the site, period of the season (June, July–August, and August–September), and time of the day were all related to the probability of engaging in vigilance behaviour, which reaches its peak in early morning and late afternoon and during July, the second period included in the study. Social behaviours, such as affiliative and agonistic behaviours, were associated mostly with sex and age-dominance status, and yearlings were the more affiliative individuals compared to other status. Overall, our results suggest that in alpine marmots, intrinsic factors mostly regulate agonistic and affiliative behaviours, while extrinsic factors, with the unexpected exception of temperature, affect the probabilities of engaging in all types of behavioural categories.

The rice growing district in northwestern Italy, where paddies were traditionally flooded throughout spring, was interested by a general decrease of standing water presence caused by the adoption of dry seeding crop practices, with consequences for water management and for the ecology of breeding waterbirds. This communication analyses changes in flooding dynamics in the last four years, estimating them from MODIS data and comparing results with previous knowledge of the same study area. Results highlighted an intensification of the phenomenon in the north-western regions (–3.3 ± 0.6% per year in the period 2013–2021) and the almost complete loss of flooded surfaces east to the Ticino river (reaching in 2021 5% of the flooded extension estimated in 2000). Such findings highlight the importance of monitoring this phenomenon – considered by other authors as the biggest anthropogenic change in surface water of all Europe since 2000 – in near real time from remotely sensed data to monitor dynamics and support sustainable management of water usage at the district level.