Search

Summary: Policy changes are often implemented without the assessment of their full environmental impact. We investigate the hydrological effects of changes in forest area triggered by shifts in European agricultural policy, and changes in species composition caused by decisions of regional forest authorities. The scenarios were modelled for the Federal State of Brandenburg (Germany) on a 50m grid scale using the SWIM (Soil Water Integrated Model) model utilising spatially-explicit land-use patterns. The results suggest a negative impact of afforestation on abandoned arable land (9.4% total state area) on the regional water balance, causing an increase in mean annual evapotranspiration of 3.7% at 100% afforestation when compared to no afforestation. The relatively small annual change covers a much more pronounced seasonal effect, leading to an increase in evapotranspiration by 25.1% in spring with a strong feedback to other hydrological components. In contrast, a change in species composition in existing forest (29.2% total state area) from predominantly Scots Pine to Common Oak decreased the evapotranspiration by 3.4%, accompanied by a much weaker but apparent seasonal pattern. The changes show a high spatial heterogeneity that is masked by a linear mean response for the total state area. [Copyright &y& Elsevier]

This article presents an object-oriented classification approach to identifying orchards, vineyards and agricultural fields. This approach uses texture-related parameters obtained from very high spatial resolution data, in particular Quickbird images and orthophotos. A multi-resolution segmentation procedure was applied to delimit individual agricultural plots as segments. Textural information of the generated segments was then used to classify orchards, agricultural fields (grassland) and two wine cultivation systems (trellis and pergola). In this article three different texture-based approaches are compared to correctly classify the given plots: (1) a 'zonal mean maximum' of texture measurements, which consider the maximum value of four directions of texture measurements related to plots; (2) a relational sub-object approach based on a thematic derived texture filter technique that reflects individual row structures; and (3) a hybrid approach combining the two previous ones. In order to identify relevant parameters for each approach, the data mining software See5 is used. The hybrid approach increased overall accuracy by 8% and 6% for Quickbird (92% accuracy) and orthophotos (88% accuracy), respectively. The application of the same methodology to the orthophotos alone results in a lower accuracy but still one that is acceptable. This offers the possibility of also considering orthophotos for this kind of detection, especially when Quickbird data are not available. In this sense, the developed methodology can be considered as a new object-based landscape analysis technique suitable for the provision of accurate maps able to fulfil the requirements of scientists, planners and other end-users. [ABSTRACT FROM AUTHOR]

A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980–2018 was built for Trentino-South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations and covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m × 250 m resolution. The accuracy of the resulting dataset was evaluated by leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 ∘ C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight into the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino-South Tyrol and a valuable support for local and regional applications of climate variability and change. The dataset is publicly available at 10.1594/PANGAEA.924502 (Crespi et al., 2020). [ABSTRACT FROM AUTHOR]

Question: Which optical traits, retrieved from biophysical models applied to Sentinel‐2 images, enable an estimation of tree species diversity based on the Spectral Variation Hypothesis? Location: Coniferous mountain forest in the eastern Italian Alps. Methods: We analyzed the PROSPECT‐5 and Invertible Forest Reflectance Model (INFORM) biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel‐2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao's Q as heterogeneity index, validating them against Shannon's H values calculated as a tree species diversity index derived from in‐situ collected data. Results: We demonstrated differences among OTIs in terms of how well their variations can be linked to species diversity. In particular the variations of brown pigments (Cbrown), carotenoids (Car) and chlorophyll content (Cab) can be considered the most relevant OTIs for the application of the SVH when using the Rao's Q as a proxy for tree species diversity in our study site. Conclusions: This research underlined that the OTIs contribute differently in the SVH to estimate tree species diversity, highlighting significant positive correlations between tree species diversity and the spatial heterogeneity of the estimated pigment content (Cab, Car, Cbrown).The heterogeneity of various OTIs (optical trait indicators; e.g., Cab [chlorophyll content], and Car [carotenoid content]), derived from biophysical models (PROSPECT‐5 and INFORM) on different Sentinel‐2 images and calculated through the Rao's Q index, is related to the tree species diversity of a coniferous mountain forest in the eastern Italian Alps. [ABSTRACT FROM AUTHOR]

A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980 – 2018 was built for Trentino – South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations, covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m x 250 m resolution. The accuracy of the resulting dataset was evaluated by the leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 °C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight on the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino – South Tyrol and a valuable support for local and regional applications. The dataset is publicly available at https://www.pangaea.de/tok/2bea918566a8c18a728e098858856cd1fcc8dbe4, Crespi et al. (2020). [ABSTRACT FROM AUTHOR]

Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions. [ABSTRACT FROM AUTHOR]

Active and passive microwave remote sensing has evolved into a well-accepted and widely used method for the spatially continuous, coarse to medium resolution mapping of the surface soil moisture content (SMC). Presently, the exploitation of high-resolution data is less mature. Sentinel-1 is a high-resolution Synthetic Aperture Radar (∼5 by 20 m), which acts as the basis for mapping of SMC in this study. There aren't any other comparable SAR missions currently available whose data can be accessed and used for free. One of the applications for SMC measurements is connected to the relationship between SMC anomalies and natural hazards such as droughts, flooding, or land-slides. A requirement for the detection and quantification of an anomaly is a long time-series (often 10–30 years) to derive a reference value. Herein lies one of the issues of Sentinel-1 based SMC mapping – at the time of writing, the Sentinel-1 time-series spanned a period of approximately 3.5 years. We introduce an approach to overcome this problem and enable the Sentinel-1 based SMC anomaly detection. The method is based on a cross-calibration between Sentinel-1 SMC estimations and coarse resolution (∼30 km) modelled SMC from the Global Land Data Assimilation System (GLDAS), which covers the time-span from 1948 to today. As a result, we can derive the long-term averages for each Sentinel-1 pixel. Results show that the proposed method allows a very accurate reproduction of the average SMC (for any given pixel) – if computed for the S1 time-span, the RMSE between estimated (GLDAS based) and true average S1 SMC is 0.7 %-Vol. Furthermore, the comparison with an in-situ time-series shows the correct detection of negative and positive anomalies, respectively. The method presented here may allow the integration of S1 data into, e.g., drought monitoring or flood forecasting applications. • The sensing of surface soil moisture based on Copernicus Sentinel-1 high resolution SAR data. • The combination of Sentinel-1 surface soil moisture with GLDAS modelled soil moisture. • The computation of high spatial resolution soil moisture climatology. • The estimation of high resolution soil moisture anomalies based on Sentinel-1. [ABSTRACT FROM AUTHOR]

Abstract Climate change has already led to a wide range of impacts on our society, the economy and the environment. According to future scenarios, mountain regions are highly vulnerable to climate impacts, including changes in the water cycle (e.g. rainfall extremes, melting of glaciers, river runoff), loss of biodiversity and ecosystems services, damages to local economy (drinking water supply, hydropower generation, agricultural suitability) and human safety (risks of natural hazards). This is due to their exposure to recent climate warming (e.g. temperature regime changes, thawing of permafrost) and the high degree of specialization of both natural and human systems (e.g. mountain species, valley population density, tourism-based economy). These characteristics call for the application of risk assessment methodologies able to describe the complex interactions among multiple hazards, biophysical and socio-economic systems, towards climate change adaptation. Current approaches used to assess climate change risks often address individual risks separately and do not fulfil a comprehensive representation of cumulative effects associated to different hazards (i.e. compound events). Moreover, pioneering multi-layer single risk assessment (i.e. overlapping of single-risk assessments addressing different hazards) is still widely used, causing misleading evaluations of multi-risk processes. This raises key questions about the distinctive features of multi-risk assessments and the available tools and methods to address them. Here we present a review of five cutting-edge modelling approaches (Bayesian networks, agent-based models, system dynamic models, event and fault trees, and hybrid models), exploring their potential applications for multi-risk assessment and climate change adaptation in mountain regions. The comparative analysis sheds light on advantages and limitations of each approach, providing a roadmap for methodological and technical implementation of multi-risk assessment according to distinguished criteria (e.g. spatial and temporal dynamics, uncertainty management, cross-sectoral assessment, adaptation measures integration, data required and level of complexity). The results show limited applications of the selected methodologies in addressing the climate and risks challenge in mountain environments. In particular, system dynamic and hybrid models demonstrate higher potential for further applications to represent climate change effects on multi-risk processes for an effective implementation of climate adaptation strategies. Highlights • A conceptual framework of multi-risk components for mountain regions is discussed. • Five innovative methods for complex system representation are revised. • Pro and cons of their applications for climate change adaptation are discussed. • System dynamics and hybrid models show higher potential for further applications. • The methods comparison provides a roadmap for climate and multi-risk modellers. [ABSTRACT FROM AUTHOR]

Observed alternation of global and local meteorological patterns governs increasing drought impact, which puts at risk ecological balance and biodiversity of the alpine forest. Despite considerable attention, drought impact on forest ecosystems is still not entirely understood, and comprehensive forest drought monitoring has not been implemented. In this study, we proposed to bridge this gap exploiting a time-domain synergetic use of medium resolution MODSI NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index band 7) time series as well as on-station temperature and precipitation measures combined in the scPDSI (self-calibrated Palmer Drought Severity Index) datasets. Analysis employed the S-mode Principal Component Analysis (PCA) examined under multiple method settings and data setups. The investigation performed for South Tyrol (2001-2012) indicated prolonged meteorological drought condition between 2003 and 2007, as well as general drying tendencies. Corresponding temporal variability was identified for local mountain forest. The former response was fostered more often by NDII7, which is related to foliage water content, whereas NDVI was more prone to report on an overall downturn and implied drop in forest photosynthetic activity. Among tested approaches, the covariance-matrix based S-mode PCA of z-score normalized vegetation season NDVI and NDII7 time series ensured the most prominent identification of drought impact. Consistency in recognized temporal patterns confirms integrity of the approach and aptness of used remote-sensed datasets, suggesting great potential for drought oriented environmental analyses. [ABSTRACT FROM AUTHOR]

• Sediment transfer of rock glaciers is modelled on catchment scale. • A mixed-methods approach is presented including orthoimage correlation, DTM analysis, stochastic and physically-based process modelling. • A simple, matrix based integration approach is applied to generate a qualitatively-scaled index expressing the sediment transfer capacity of rock glaciers. • Independent geomorphic and environmental variables are tested on their expressive power to explain the sediment transfer capacity of rock glaciers. Rock glaciers are active sediment transport systems of high mountain periglacial environments. Driven by the internal deformation of their frozen core, they can convey substantial amounts of debris from their nourishing zone towards their front. Depending on the topographic conditions at the front slope, the released sediment is either overridden again by the advancing rock glacier or transferred downwards by rapid mass movements. The capacity of rock glacier environments to transfer sediment however has remained largely unexplored, in particular beyond the local scale. In this work, we consider a rock glaciers' sediment transfer capacity as a function of its transported volume rate and the travel range of mobilized material at its front. A GIS-based method, low in data-demand, is proposed to generate catchment-scale proxies for the above-mentioned input variables and to combine them to a qualitative index describing sediment transfer capacity of rock glacier fronts. The proposed method is applied in two basins, strongly characterized by periglacial dynamics (Ultental and Schnalstal in South Tyrol, Eastern Italian Alps). In a final step, computed sediment transfer classes are assessed on their geomorphological plausibility by building empirical relationships with independent environmental variables. [ABSTRACT FROM AUTHOR]

This paper presents an improved algorithm for the retrieval of Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery that has been specifically customized for mountain grasslands in the Alps. The main features of the proposed algorithm, which is based on the inversion of a radiative transfer model, are: i) a higher spatial resolution (250 m) with respect to the corresponding standard MODIS product and ii) tuning the model to the spectral characteristics of mountain grasslands. To quantify the effects of the features of the proposed algorithm, the approach is first applied to a MODIS reflectance data time series from 2007 up-scaled to a 1 km spatial resolution for better comparison with the standard MODIS LAI product. In the next step, the benefit of the higher spatial resolution is assessed by applying the algorithm to a series of MODIS satellite images with a spatial resolution of 250 m acquired over the central Alps in the period 2005–2007. LAI estimates were validated for both temporal consistency and accuracy using ground measurement time series collected at three different study sites in the investigated area. The results obtained demonstrate the capability of the proposed algorithm to follow the expected temporal and range dynamics of LAI in this challenging environment, showing an overall RMSE accuracy of 1.68 (m 2 /m 2 ). This approach thus opens a promising avenue for the exploitation of moderate resolution satellite data for novel and more accurate monitoring studies at a regional scale in mountain environments. [ABSTRACT FROM AUTHOR]

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results. [ABSTRACT FROM PUBLISHER]

In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats. [ABSTRACT FROM AUTHOR]

The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition. [ABSTRACT FROM AUTHOR]

A new algorithm for snow cover monitoring at 250 m resolution based on Moderate Resolution Imaging Spectroradiometer (MODIS) images is presented. In contrast to the 500 m resolution MODIS snow products of NASA (MOD10 and MYD10), the main goal was to maintain the resolution as high as possible to allow for a more accurate detection of snow covered area (SCA). This is especially important in mountainous regions characterized by extreme landscape heterogeneity, where maps at a resolution of 500 m could not provide the desired amount of spatial details. Therefore, the algorithm exploits only the 250 m resolution bands of MODIS in the red (B1) and infrared (B2) spectrum, as well as the Normalized Difference Vegetation Index (NDVI) for snow detection, while clouds are classified using also bands at 500 m and 1 km resolution. The algorithm is tailored to process MODIS data received in real-time through the EURAC receiving station close to Bolzano, Italy, but also standard MODIS products are supported. It is divided into three steps: first the data is preprocessed, including reprojection, calculation of physical reflectance values and masking of water bodies. In a second step, the actual classification of snow, snow in forested areas, and clouds takes place based on MODIS images both from Terra and Aqua satellites. In the third step, snow cover maps derived from images of both sensors of the same day are combined to reduce cloud coverage in the final SCA product. Four different quality indices are calculated to verify the reliability of input data, snow classification, cloud detection and viewing geometry. Using the data received through their own station, EURAC can provide SCA maps of central Europe to end users in near real-time. Validation of the algorithm is outlined in a companion paper and indicates good performance with accuracies ranging from 94% to around 82% compared to in situ snow depth measurements, and around 93% compared to SCA derived from Landsat ETM+ images. [ABSTRACT FROM AUTHOR]

• Remote sensing proven a key approach for diversity estimates. • CHM LiDAR shown to be a key instrument for estimating tree species diversity. • Height Variation Hypothesis developed to assess tree species diversity. • Height Heterogeneity shown to be related to tree species diversity. An indirect method for estimating biodiversity from Earth observations is the Spectral Variation Hypothesis (SVH). SVH states that the higher the spatial variability of the spectral response of an optical remotely sensed image, the higher the number of available ecological niches and hence, the higher the diversity of tree species in the considered area. Here for the first time we apply the concept of the SVH to Light Detection and Ranging (LiDAR) data to understand the relationship between the height heterogeneity (HH) of a forest and its tree species diversity, a concept we have named the 'Height Variation Hypothesis' (HVH). We tested HVH in two different European forest types: a coniferous mountain forest in the eastern Italian Alps and a mixed temperate forest in southern Germany. We used the heterogeneity index Rao's Q to estimate HH using a Canopy Height Model (CHM) at different resolutions derived from LiDAR data, and linear regression models and relation analysis to assess the relationships between HH and three species diversity indices derived from in situ collected data: Shannon's H , Simpson's S and species richness. The relationships were calculated for all plots in both study areas, and separately for plots with a defined Canopy Closure (CC > 70%, CC > 80%, CC > 90%) to understand the effect of forest density on the relationship between HH and tree species diversity. Our results showed that HH is related to the tree species diversity of the forest ecosystems reaching (in the case of Shannon's H) values of R 2 = 0.63 for the coniferous mountain forest and R 2 = 0.56 for the mixed temperate forest, particularly when calculated with a CHM resolution of 2.5 m. The associations also increased with increasing canopy closure suggesting that HVH is scale and forest density dependent. Our results also underlined that the abundance-based diversity measures are more highly correlated with HH than with species richness. Finally, our findings suggest that the HVH is a valuable tool for assessing tree species diversity in forest ecosystems, and could also be useful for overall biodiversity estimates. [ABSTRACT FROM AUTHOR]

• Data-driven models allow to estimate the rock glacier status (intact vs. relict). • Generated classification rules assigned the status to 235 unclassified rock glaciers. • Vegetation cover and elevation were found to be the most relevant predictors. • Logistic regression performed slightly better than machine learning models. • For most of the unclassified rock glaciers all models agreed on the status. Ice presence in rock glaciers is a topic that is likely to gain importance in the future due to the expected decrease in water supply from glaciers and the increase of mass movements originating in periglacial areas. This makes it important to have at ones disposal inventories with complete information on the state of rock glaciers. This study presents a method to overcome incomplete information on the status of rock glaciers (i.e. intact vs. relict) recorded in regional scale inventories. The proposed data-driven modelling framework can be used to estimate the likelihood that rock glaciers contain frozen material. Potential predictor variables related to topography, environmental controls or the rock glacier appearance were derived from a digital terrain model (DTM), satellite data and gathered from existing data sets. An initial exploratory data analysis supported the heuristic selection of predictor variables. Three classification algorithms, namely logistic regression (GLM), support vector machine (SVM) and random forest (RF), were trained on the basis of the available information on the status of rock glaciers within the territory of South Tyrol (Eastern Italian Alps). The resulting classification rules led to assign a binary label – intact or relict – to 235 unclassified rock glaciers present in the inventory. All models were validated quantitatively on spatially-independent test samples (spatial cross validation) and achieved highly satisfactory performance scores. Hereby, the less flexible statistically-based classifier (GLM) performed slightly better than the more flexible machine learning algorithms (SVM and RF). Spatial permutation-based variable importance assessment revealed that elevation and vegetation cover (based on NDVI) were the most relevant predictors. For more than 80% of the unclassified rock glaciers, all of the three models agreed on the spatially predicted rock glacier status. Only for a minor portion (12.3%), one model differed from the remaining two. [ABSTRACT FROM AUTHOR]

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (

This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Snow avalanches are hazardous phenomena in many mountain environments and can pose a serious threat to people and their properties. Reliable information on the location and timing of past avalanches (i.e. inventory data) is not only decisive to identify susceptible terrain, but also to gain insights into typical avalanche dynamics. The creation of a regional scale snow avalanche inventory relies on reports generated in the field or on the basis of aerial photographs. Particularly for large areas, such manual mapping approaches are known to be time consuming, spatially restricted to a specific area of interest (e.g. nearby populated areas) and subjective. In consequence, the resulting avalanche information is likely to be biased towards an underrepresentation of events in remote terrain e.g., high altitude areas.This research targets to counteract these pitfalls by testing a novel regional-scale snow avalanche detection approach in the province of South Tyrol, Italy. The methodical framework takes full advantage of already available data (e.g. historical avalanche data, Sentinel-2 imagery) and can be subdivided into two main steps: (1) Statistically based delineation of terrain typically susceptible to avalanches and (2) remote sensing based detection of changes within the previously defined avalanche prone areas i.e. unsupervised binary classification of bi-temporal Sentinel-2 images: avalanche vs. no avalanche. The produced maps and validation metrics indicate that the approach has the potential to gain systematic information on recently occurred snow avalanches for large areas at a reasonable spatial and temporal resolution. A closer look at the results also revealed potential drawbacks of the developed automatized procedure. This poster contribution will allow insights into the methodical framework, first results and associated pitfalls. [ABSTRACT FROM AUTHOR]

Snow is a key environmental parameter in mountains, and in this changing climatereductions in snow are expected. Traditionally, future estimates of snow are based ondedicated snow/hydrological models forced by climate projections, which, however, arecomputationally intensive and which decouple hydrology from climate forcing. Recently,regional climate models (RCM) have been used as an alternative, although snow is only anauxiliary parameter in RCMs and not as accurately represented as compared to dedicatedsnow models. Nonetheless, RCMs encompass the climate-hydrology feedbacks, cover largeareas, and have become available in moderate horizontal resolutions. Here, wecompare observed snow cover (from MODIS satellites) over the whole Alps to the fiveEURO-CORDEX RCMs that provide snow cover as output. The comparison is done usingthe evaluation run of the RCMs, which is forced by ECMWF ERA-INT, and atthe RCM native resolution of 0.11∘ that corresponds to approximately 8.5km by12.5km in the Alps. We found a spatially and seasonally heterogenous bias in snowcover, which can be partly explained by the temperature bias and the orographicsmoothing in the EURO-CORDEX RCMs. The highest biases can be found inmid-altitudes (1000-1500m) and in spring and fall, which correspond to the spatialtransition zones and temporal transition times. Moreover, there are large differencesbetween the various RCMs: DMI-HIRHAM5 produces unrealistically low valuesof snow cover (at least in the alps), KNMI-RACMO22E generally overestimatessnow cover, while the others (CLMcom-CCLM4-8-17, IPSL-INERIS-WRF331F,SMHI-RCA4) have the lowest bias. Next, we applied a statistical bias correction,which results in a model ensemble view of future bias corrected snow cover for thewhole Alps until 2100 for the RCP 4.5 and 8.5 scenarios, with 12 and 13 models(GCM-RCM combinations, excluding DMI-HIRHAM5), respectively. These arethe first results of CliRSnow, a project that aims at providing bias corrected anddownscaled projections of snow cover for the whole Alpine region until 2100. Thisproject has received funding from the European Union’s Horizon 2020 research andinnovation programme under the Marie Sklodowska-Curie grant agreement No 795310. [ABSTRACT FROM AUTHOR]

Rock glacier inventories have been created for many mountainous regions in Europe and outside in the past decades. These GIS-based catalogues are an important source of information to investigate periglacial landscapes, such as exploring associations between permafrost phenomena and environmental conditions. In this context, a crucial information stored in rock glacier inventories are the activity status and the related ice content of the mapped landforms ("active", "inactive", "intact" or "relict", and "fossil"). The assignment of the activity status is mostly done by individual operators based on morphological evidences found in the field, on aerial images and on digital terrain models, in some cases by field measurements with different methods. Even though this expert-based approach has proven to be highly valuable, it is also known to be time consuming and somewhat subjective concerning especially the definition of the status of the rock glaciers. Recent technological advances in satellite remote sensing techniques - coupled to new algorithms for multiple variable analysis - offer new possibilities for improving rock glacier classification. This study presents a novel, two-step approach to divide first moving from not moving rock glaciers, and subsequently to separate ice-cored from ice-free rock glaciers. This approach combines remote sensing with statistical computing and proposes an integration scheme that enables to combine the advantages of these both methodologies. The study was conducted in South Tyrol (Northern Italy), a region for which a comprehensive rock glacier inventory was made available by the local administration.In the first step, a stack of multi-temporal SAR Sentinel 1A/B-images were processed to obtain coherence changes within each rock glacier present in the inventory. Exploiting the relation between coherence and displacement, an unsupervised classification has been developed. Rock glaciers were divided into "non-moving" forms (i.e. those without detectable displacement and thus including inactive, intact, relict or fossil categories) and "moving" ones (i.e. those with detectable displacement and corresponding to active rock glaciers). This first step takes into account only the kinematic aspects, without considering the permafrost ice content.The second step is based on a supervised classification of the rock glacier status using statistically-based and machine-learning algorithms. A set of environmental variables, which in literature were found to be indicative about the ice content of rock glaciers, were used to build several multiple-variable models. This operation produces a binary classification of rock glaciers: those featuring a relevant ice content (i.e. including both active and intact types) and those with only sparse to null ice (i.e. relict and fossil types).Finally, the integration of the two steps described above leads to the following classification: "currently moving" and "high ice content" = "active"; "not moving" and "high ice content" = "intact"; "not moving" and "sparse ice content" = "relict"; "not moving" and "no ice content" = "fossil". [ABSTRACT FROM AUTHOR]

Landslides constitute serious geohazards in many hilly and mountainous regions and pose an important landscape-forming process in South Tyrol, Italy. It is well recognized that water influx, originating from precipitation or snow melt, represents a crucial preparatory and triggering factor of slope instability. Therefore, various attempts have been made to examine rainfall and snow melt patterns to identify critical thresholds for landslide occurrence (regional scale) or landslide behavior (local scale).This study focused on a single large (1.69 km2) and complex earth slide – earth flow located in the Italian Dolomites (Corvara, South Tyrol), which shows surface displacement rates of a few cm/a in the accumulation zone up to several m/a in the most active parts of the track and source zones. The main aim of this research was to gain deeper insights into the spatio-temporal movement patterns of the Corvara landslide. The investigation focused on (i) the analysis of time-series data (2013 – 2018) derived from differential global navigation satellite system (DGNSS) measurements at 16 GPS stations and on (ii) the assessment of statistical relationships between measured velocity rates and selected cumulative time windows (n=19; from 3 days to 3 years) of antecedent precipitation and snow melt.The results indicate strong variations of the movement rates in space and time with a slight decrease in measured landslide velocities over the years. Neighboring GPS stations tended to move simultaneously (i.e. higher correlations of movement rates (up to 0.9) among nearby stations) while no seasonality in landslide behavior was detected. In most cases, only weak to moderate correlations (~ 0.05 – 0.44) between velocity rates and cumulative antecedent precipitation were identified. No geomorphically meaningful associations were found by correlating measured movement rates with snow melt patterns.In summary, the present research revealed that the movement of the Corvara landslide is complex in space and time and does not follow a clear seasonal pattern. The analysis provided valuable evidence that the spatio-temporal behavior of the Corvara landslide cannot be explained (or even predicted) by scrutinizing time series of surface data (GPS, rainfall and snow cover). Deeper insights into the past and current process dynamics might require intensive surveys of the spatially heterogeneous and highly variable subsurface conditions. [ABSTRACT FROM AUTHOR]

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables. [ABSTRACT FROM AUTHOR]