Mattia Rossi

In recent decades climate warming accelerated substantially and consequences were especially pronounced in alpine ecosystems. Among others, alpine grasslands characterized by long lasting snow cover e.g., snowsbeds, will be especially threatened, as they are subjected to substantial shifts in phenological timing and development due to earlier snowmelt. To assess the impact of advancing snowmelt on the phenology of alpine snowbed communities, we have monitored two study sites in the Italian Alps for 3-years. While both study sites are characterized by the same vegetation community (Salicetum herbaceae), altitude, and topography, they differed in the amount of winter precipitation and as a consequence in the timing of snowmelt. We monitored the phenological development of five shared plant species between both study sites by deriving the “green chromatic coordinate” (gcc) from images of the Phenocams, installed at both study sites and in-situ phenological assessment, following a standardized protocol. We then compared results between the early and the late snowmelt site and related the results of the community level to those of the species level. The gcc identified the start of growing season (SOS), peak of growing season (POS) and end of growing season (EOS) at both study sites but failed to grasp the interspecific and inter-site differences on the species level. We found that a three-week earlier snowmelt at the early snowmelt site (DOY 176 vs. 197) did not extend (25 vs. 26 days) but only advance the POS by 20 days and resulted in a approx. 10% lower gcc-values at the early snowmelt site. At the same time, the in-situ monitoring highlighted species-specific responses of the study species. Within the study period we could identify changes in the speed of the phenological development for each study species in at least one year, with the interannual differences being greater than the differences between the study sites. Nevertheless, when compared to the late snowmelt site, the phenological development at the early snowmelt site was slower for some species (e.g., Poa alpina -24.2%) and faster for others (e.g., Euphrasia minima +70.4%). This indicates the ability of these species to modulate their phenology in response to shifting snowmelt dates. Relating the results of both approaches to each other led to mixed results, as the species-based approach only partially supported the results of the community-based approach and vice versa, with R²-values ranging from 0.1 (Euphrasia minima) up to 0.61 (Veronica alpina). These results highlight the importance of multi-level approaches when trying to identify the effects of climate change on alpine vegetation communities, as species specific results in most cases don’t represent the results on a community level and results on community level mostly fail to address the single species’ responses in these complex and heterogeneous habitats.

Shallow landslides in alpine environments can constitute a serious threat to the exposed elements. The spatio-temporal occurrence of such slope movements is controlled by a combination of predisposing factors (e.g. topography), preparatory factors (e.g. wet periods, snow melting) and landslide triggers (e.g. heavy precipitation events).  For large study areas, landslide assessments frequently focus either on the static predisposing factors to estimate landslide susceptibility using data-driven procedures, or exclusively on the triggering events to derive empirical rainfall thresholds. For smaller areas, dynamic physical models can reasonably be parameterized to simultaneously account for static and dynamic landslide controls.  The recently accepted Proslide project aims to develop and test methods with the potential to improve the predictability of landslides for the Italian province of South Tyrol. It is envisaged to account for a variety of innovative input data at multiple spatio-temporal scales. In this context, we seek to exploit remote sensing data for the spatio-temporal description of landslide controlling factors (e.g. precipitation RADAR; satellite soil moisture) and to develop models that allow an integration of heterogeneous model inputs using both, data-driven approaches (regional scale) and physically-based models (catchment scale). This contribution presents the core ideas and methodical framework behind the Proslide project and its very first results (e.g. relationships between landslide observations and gridded daily precipitation data at regional scale). 

Grasslands are a predominant land cover form, responsible for ecosystem services such as slope stabilization, water and carbon storage or fodder provision for livestock. At the same time, altering climatic effects and human activities have influenced the natural growth pattern and condition of alpine grasslands over the past decades. Mountainous areas are projected to be particularly impacted by climatic changes and management practices. Nowadays, a wide variety and different installations of Earth observation systems are available to monitor and predict grassland growth and status, to evidence ecosystem services such as biodiversity, the fodder availability or to highlight the effectiveness of management practices.In this study Support Vector Regression (SVR) and Random Forest (RF) machine learning techniques were used to estimate the aboveground biomass, plant water content and the leaf area index (LAI). As input, we combined hyperspectral imagery from field spectrometers, optical Sentinel-2 data as well as SAR data from Sentinel-1. The models were tested targeting approximately 250 biomass and LAI samples taken from 2017 to 2020 on grasslands in the Mazia/Matsch valley, located in South Tyrol (Italy). The dataset was divided based on grassland type (meadow and pasture) the growth period (up to three growth periods a year for meadows), as well as the year, to analyze the modelled predictions based on the growing stage of the vegetation.The results obtained using the integration of the datasets are very promising in the meadow, with R2 reaching ranging from 0.5 to 0.8 for the biomass and from 0.6 to 0.8 for the LAI retrieval. At the same time, the division in growth phases shows a slightly higher correlation than during the first and second growing periods, indicating that the irregular growth after the last harvest of the year affects the capability of prediction of LAI and above-ground biomass. However, the predictability worsens on high biomass and LAI values before the harvest takes place, thus indicating an impact of the saturation in the optical data and revealing the need for additional data sources or an alternated weighting of the predictors in the models. The results on the pasture show that the prediction of LAI and biomass with optical and SAR data is difficult to achieve (mean R2 ranging from 0.3 to 0.4) given the natural heterogeneity in growth within the test area. Additional datasets such as cattle movement or the slope information could represent a valuable source of information for further LAI and biomass growth analyses in mountainous areas.This research is part of the 2019-2021 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.

Mountain plant species are changing their ranges in response to global warming. However, these shifts vary tremendously in rate, extent and direction. The reasons for this variation are yet poorly understood. A process potentially important for mountain plant re-distribution is a competition between colonizing species and the resident vegetation. Here, we focus on the impact of this process using the recent elevational shift of the sedge Carex humilis in the northern Italian Alps as a model system. We repeated and extended historical sampling (conducted in 1976) of the species in the study region. We used the historical distribution data and historical climatic maps to parameterize a species distribution model (SDM) and projected the potential distribution of the species under current conditions. We compared the historical and the current re-survey for the species in terms of the cover of important potential competitor species as well as in terms of the productivity of the resident vegetation indicated by the Normalized Difference Vegetation Index (NDVI). We found that Carex humilis has shifted its leading range margin upward rapidly (51.2 m per decade) but left many sites that have become climatically suitable since 1976 according to the SDM uncolonized. These suitable but uncolonized sites show significantly higher coverage of all dwarf shrub species and higher NDVI than the sites occupied by the sedge. These results suggest that resistance of the resident vegetation against colonization of migrating species can indeed play an important role in controlling the re-distribution of mountain plants under climate change.

Grasslands cover almost one third of the world’s terrestrial surface. In Alpine environments grassland vegetation fulfills various key environmental purposes such as water reservoir, slope stabilizer and carbon sink or fodder for livestock. At the same time Alpine regions are more affected by climatic changes than other geographic zones, potentially resulting in earlier green-up phases or an elevated exposure to drought events, hampering the growth and vitality of grassland vegetation. The scope of this study is to build an algorithm capable of biomass estimation using Support Vector Machine approach on hyperspectral and Synthetic Aperture Radar (SAR) data. To that purpose, field campaigns were carried out during 2017 and 2019 in Val Mazia (South Tyrol, Italy), where hyperspectral spectroradiometer samples were collected, as well as leaf area index (LAI), soil moisture, and above ground biomass measurements. Copernicus Sentinel-1 IW SAR backscattering data were used to complete the dataset.The spectroradiometer was used to simulate the hyperspectral data of the Italian Space Agency (ASI)’s PRISMA mission, launched on 22 March 2019. Since the number of bands is larger than the number of samples, a prediction approach based on machine learning risks to model noise. The following two solutions were tested and compared: (i) the number of bands was reduced by resampling the data to match specifications of Copernicus Sentinel-2 Multispectral Instrument (MSI), and (ii) the data was simulated using the PROSPECT model, increasing the sample size.In the first case correlation R2 of 0.37 was found. Discrepancies were observed for high biomass values, which could be explained by the small number of samples available shortly before harvest. To mitigate this effect, data were simulated for high biomass based on field average values and standard deviation within each date. R2 increased to 0.71 in this case, confirming the above mentioned hypothesis regarding the dataset representativeness.In the case of PROSPECT model, the parameters were found by iterating each one within ranges defined in the bibliography, until the spectral signatures matched the field observations. The resulting parameters were the input for data simulation. A genetic algorithm feature selection was run to reduce the number of features, discarding those with little or redundant information followed by an SVR model applied to the most sensitive bands resulting in an R2 of 0.53. These initial results will be used as a basis for future investigations to improve the prediction model, for example by extending the dataset with new field campaigns, including more simulated data at biomass peak, as made with Sentinel-2 resampled dataset, or by adding further input variables, such as leaf area index. Furthermore, the procedure will be performed for fresh biomass and water content estimations.The results obtained pave the way for future implementation of the tested algorithms on PRISMA hyperspectral and COSMO-SkyMed X-band SAR data in the future.This research is part of the ongoing project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.

Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.