95 research outputs found
SAR and Optical satellite sensors to detect phenology in Alpine areas
No full textTemperature variation influences the length of the growing season in Alpine areas, causing shift in phenological
phases of vegetation, with a reduction of ecosystems resilience. Notably, changes of the start and end of the growing
season might have a significant impact on fragile mountain ecosystems. Optical remote sensing, through spectral
vegetation indices, has been extensively used for monitoring vegetation dynamics. However, there are challenges
of processing optical data, namely clouds and their shadows, which interferes with remote sensing studies. As
opposed to optical images, Synthetic Aperture Radar (SAR) can provide a systematic data source of land surface
and land cover changes that are insensitive to cloud contamination. Moreover, the free data policy adopted for
the Copernicus programme allows us to use time-series from Sentinel-1 and Sentinel-2 with 20 and 10 m of spatial
resolution, respectively. In this study we analysed the vegetation phenology in the alpine areas of South-Tyrol
(Italy) by constructing time-series from both SAR and optical sensors, validating subsequently our results with a
network of ground stations at different altitude (i.e. phenocams, NDVI sensors and temperature - soil moisture
data loggers). After a noise removal using different techniques, several filters were applied to SAR and optical
time-series. From the modelled values we extracted the Start of Season (SOS), Maximum and End of Season
(EOS), and we validated our results using information from NDVI sensors and phenocams. From our study we
can assume that multitemporal SAR signal and specifically the VH polarization can be used to detect phenological
dynamics in grassland and multitemporal SAR signal is well correlated to the NDVI from Sentinel 2 and ground
observations of vegetation indices
Detection of vegetation changes in cold regions using a combination of radar and optical satellite data
No full textThe extremely sensitive ecosystem of the cold regions is going through a rapid climate change, and the projections
show an increase in temperature. Considering that the temperature influences the length of the growing
season as well as composition, biomass and plant distribution, studying the vegetation is crucial to analyse the
consequences of seasonal variability in mountain and Arctic areas. Notably, changes in the start and end of the
growing season may have a considerable impact on these ecosystems. Unfortunately, the method in which data are
generally collected, such as field data, does not allow a large spatial or temporal scale analysis of these ecological
responses. Conversely, the remote sensing measurement of environmental parameters by satellite sensors facilitates
this type of analysis, and has proved to be crucial in ecological studies. There are, however, challenges of processing
optical data, namely clouds and their shadows, which interferes with remote sensing studies. Cloud detection
in the Arctic and alpine areas is especially demanding since cloud-contaminated conditions are frequent. Hence,
optical data require corrections according to different environmental conditions in order to be enabled in vegetation
studies. As opposed to optical images, radar data can provide a systematic data source of land surface and cover
changes that are insensitive to cloud cover and hour of acquisition, even though signal processing is challenging.
The overall aim of the present project is to monitor vegetation seasonal dynamics in mountainous and Arctic
region by synergic use of optical and radar satellite data. We expect that a combination of radar and optical data
allow more accurate analysis of these changes. To analyse these seasonal variations three different study areas have
been chosen: Adventdalen valley (Svalbard archipelago), Dovrefjell National Park (Southern part of Norway) and
Val di Mazia-Matscher Tal (Südtirol, Italy). Specifically, the research in these areas will require the accomplishment
of the following tasks: determine the onset/end of the growing seasons; map vegetation communities; biomass
estimation. To reach the aim of this study, we will use time-series from Sentinel 1 and Sentinel 2 satellites. To
assess the accuracy of satellite processed products, we use proximal sensing information and field measurements
Looking at the Water-Energy-Food nexus through the lens of Ecosystem Services: a new perspective
No full textGlobal demand for Water, Energy, and Food (WEF)
has risen significantly in recent decades, a trend that
is expected to continue. The interconnected nature
of WEF systems, along with global change drivers,
highlights the need for an integrated resource management
approach known as ‘nexus thinking.’ This
approach emphasizes the synergies and trade-offs
among WEF policies to achieve sustainable outcomes.
Accordingly, this perspective paper argues that a truly
integrated nexus approach necessitates placing ESs at
the center of the WEF nexus framework. It begins
by defining ESs and discussing their role within this
nexus. Subsequently, it illustrates how implementing
nexus thinking could be enhanced and operationalized
by incorporating an ESs-based perspective.
Finally, it provides recommendations for future steps
to foster this integration
Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation
No full textClimate 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
Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis
No full textForests cover about 30% of the Earth surface, they are among the most biodiverse terrestrial ecosystems and represent the bulk of many ecological processes and services. The assessment of biodiversity is an important and essential goal to achieve but it can results difficult, time consuming and expensive when based on field data. Remote sensing covers large areas and provides consistent quality and standardized data, which can be used to estimate species diversity. One method to estimate species diversity from remote sensing data is based on the Spectral Variation Hypothesis (SVH), which assumes that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of the considered area. SVH has been tested using different spectral heterogeneity (SH) indices and measures, recently the Rao's Q index has been proposed as a new spectral variation measure to be applied to remote sensing data. In this paper, we tested the SVH in an alpine coniferous forest to estimate tree species diversity. We evaluated the performance of the Rao's Q index and compared it with another widely used SH index, the Coefficient of Variation (CV), validating them against values of Shannon's H (used as species diversity index) derived from in-situ collected data. A NDVI time-series (for 2016 and 2017) derived from the Sentinel-2A and 2B and Landsat 8 OLI satellites has been used to test the effect of the spatial grain of both the sensors and to understand the seasonality of the SVH. The results showed that the SVH is season and sensor dependent. For both years and satellites, the relation between Rao's Q and field data reached the highest R2 between June and July, decreasing towards winter and spring similarly to the NDVI time-series. This relationship could be given because, when NDVI reaches its highest values, it is able to capture small variation in reflectance of different leaf traits typical of specific trees. The relation between field and spectral diversity reached a value of R2 = 0.70 (2017) and R2 = 0.48 (2016) for Sentinel-2 and of R2 = 0.42 (2017) and R2 = 0.47 (2016) for Landsat 8. CV showed the same NDVI temporal tendency. However, the relation between field-derived Shannon's H and CV was on average lower than that we found for Rao's Q. This research underlined the goodness of the Rao's Q index, the relevance of the NDVI in the study of the SVH and the importance of the multi-temporal approac
Proximal sensing phenology data to validate Sentinel-2 in cold regions
No full textThe High Arctic and alpine areas are going through a rapid climate change. Temperature variation influences
the length of the growing season, causing shift in phenological phases of vegetation, with a reduction of ecosystems
resilience. Notably, changes of the start and end of the growing season might have a significant impact on fragile
mountain and arctic ecosystems.
Optical remote sensing, through spectral vegetation indices, has been extensively used for monitoring vegetation
dynamics. Moreover, with a 10 m spatial resolution of Sentinel-2A and 2B it is now possible to study phenology at
a level of vegetation community. However, there are challenges of processing optical data, namely clouds and their
shadows, which interferes with remote sensing studies. Cloud detection in the Arctic and alpine areas is especially
time consuming since cloud-contaminated conditions are frequent. Hence, optical data require corrections according
to different environmental conditions in order to be enabled in vegetation studies.
To assess the accuracy of satellite processed products, a field validation is necessary. Nevertheless, due to
remote location, extreme climatic conditions, short growing season and high cost of sampling, field-based manual
phenological observations in mountain regions are often problematic. To overcome traditional approaches, proximal
sensors were proven to be a key method to validate phenological phases. Indices derived from digital cameras and
NDVI sensors offer the opportunity of recording data with high spatial and temporal resolution in remote areas,
that can consequently be used in optical satellite validation. In mountain regions, where phenological phases are
strictly dependent on altitude and exposition, a network of proximal sensors needs to cover a wide spatial and
vertical gradient.
In this study, we present a network of phenocams and NDVI sensors in Adventdalen (Svalbard archipelago) and
alpine areas of Dovrefjell National park (Norway) and South-Tyrol (Italy). The aim of the project is to validate
phenological phases derived from Sentinel-2A and 2B. After a pre-processing phase, seasonal parameters will be
mapped, as the onset and end of the growing season. In particular, the aim of the project is:
∙ to validate a phenolgy map of a clear sky NDVI time-series;
∙ to analyse proximal sensor products of phenology on different vegetation communities, altitude and expositio
Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data
No full textAn 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 un-
derstand 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 R2 = 0.63 for the coniferous mountain forest and R2 = 0.56 for the mixed temperate forest, par-
ticularly when calculated with a CHM resolution of 2.5 m. The associations also increased with increasing ca-
nopy 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
High-resolution daily series (1980 - 2018) and monthly climatologies (1981 - 2010) of mean temperature and precipitation for Trentino - South Tyrol (north-eastern Italian Alps)
No full textThe dataset contains the gridded daily series and the gridded monthly climatologies of mean temperature and precipitation at 250-m spatial resolution for the region Trentino-South Tyrol (north-eastern Italy). The daily series span the period 1980 - 2018, while the climatologies were computed over the 30-year period 1981 - 2010. The whole dataset was obtained by an interpolation procedure based on a dense and quality-checked archive of in-situ observations. The input data were derived from the regional meteorological station networks managed by Meteotrentino and the Hydrological Office of the Province of Bolzano. The gridded mean temperature represents the average of minimum and maximum daily temperature values
Mapping particulate matter in alpine regions with satellite and ground-based measurements: An exploratory study for data assimilation
No full textIntegration of hydro-climatological model and remote sensing for glacier mass balance estimation
No full textThe accurate monitoring and understanding of glacier dynamics are of high relevance for climate science and water-resources management. The glacier parameters are typically estimated by data assimilation methods which inject field measurements into the numerical simulations with the aim of improving the physical model estimates. However, these methods often are not able to capture and model the complexity of the estimation problem. To solve this problem, this paper proposes a method that integrates remote sensing (RS) data, in-situ observations and a physical-based model to accurately estimate the Glacier Mass Balance (GMB). The RS data are used to represent the physical properties of the glaciers by characterizing their topography and spectral properties. Instead of assimilating the observations into the model, the in-situ measurements are used to perform a data-driven correction of the GMB estimates derived from the physically-based simulations in the informative RS feature space. The method is applied to the Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) hydro-climatological model. In the experimental analysis, the multispectral images used to define the feature space are high-resolution Sentinel-2 images. The method is validated on three glaciers in Tyrol (Hintereis, Kasselwand and Varnagt glaciers), in 2015 and 2016. The obtained results show the effectiveness of the method in improving the GMB estimates
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