PEOPLEMattia Callegari

de en it

Mattia Callegari

Senior Researcher
Institute for Earth Observation

T +39 0471 055 384

mattia.callegari@eurac.edu

https://orcid.org/0000-0003-1520-1975

download or scan my contact information (vCard Format)

About

Snow and glacier monitoring with optical and SAR data
SAR data processing
SAR Interferometry
Machine learning

Short bio

Mattia Callegari is a senior research at the Institute for Earth observation since 2016. He holds a master's degree in Telecommunications Engineering with the University of Trento, Italy with a thesis regarded snow-pack parameters retrieval in Alpine areas from Synthetic Aperture Radar and remote sensed optical data. In 2015 he received the Ph.D. degree on Alpine cryosphere dynamics monitoring using remote sensing techniques and ground-based data at the Department of Earth and Environmental Sciences at the University of Pavia, Italy. At Eurac Research his main research activities focus on snow and glacier monitoring with satellite optical and SAR images, SAR data processing and SAR interferometry.

Scientific Publications

Presentation2024

Cubes & Clouds – A Massive Open Online Course for Cloud Native Open Data Sciences in Earth Observation

Zellner PJ, Balogun RO, Eckardt R, Hodam H, Dolezalova T, Meißl S, Eberle J, Claus M,

Mattia Callegari, Alexander Jacob, Michele Claus

Progetto: MOOC EO Open Data Science

Earth Observation scientists are confronted with unprecedent data volumes that are constantly growing as the number of satellite missions increases, as well as spatial and temporal resolution do. Traditional working modes, such as downloading satellite data and processing it on a local computer, do not apply anymore and EO science is moving quickly towards cloud technologies and open science practices. Even though these new technologies and practices are evolving quickly and are becoming community standard, there is not much educational material available to educate the next generation of EO scientists.

The Massive Open Online Course Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation teaches the concepts of data cubes, cloud platforms and open science in the context of earth observation. It targets Earth Science students and researchers who want to increase their technical capabilities onto the newest standards in EO computing, as well as Data Scientists who want to dive into the world of EO and apply their technical background to a new field. Before starting, prerequisites are a general knowledge of EO and python programming. The course explains the concepts of data cubes, EO cloud platforms and open science by applying them to a typical EO workflow from data discovery, data processing up to sharing the results in an open and FAIR (Findable, Accessible, Interoperable, Reusable) way. An engaging mixture of videos, animated content, lectures, hands-on exercises and quizzes transmits the content. After finishing the participant will understand the theoretical concepts of cloud native EO processing and have gained practical experience by conducting an end-to-end EO workflow. The participant will be capable of independently using cloud platforms to approach EO related research questions and be confident in how to share research by adhering to the concepts of open science.
The hands on exercises are carried out on the EO cloud platform Copernicus Data Space Ecosystem and are leveraging the Copernicus Data available through the STAC Catalogue and the cloud processing API openEO. In the final exercise the participants collaborate on a community mapping project adhering to open science and FAIR standards. A snow cover map is jointly created where every participants maps a small area of the alps and submits it to a STAC catalogue and web viewer. Ultimately, the map grows in space and time and every participant has contributed proving they are capable of EO cloud computing and open science practices.
The talk will guide through the topics covered in the MOOC and show how they are presented in the EOCollege e-learning platform, the links to the exercises carried out on CDSE will be explored and the open science aspect will be shown in the community mapping project and the invitation to collaborate on the courses completely open GitHub repository.

DOI Fonte

DOI: 10.5194/egusphere-egu24-21733

Inbook2024

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Notarnicola C, Callegari M, Castelli M, Cuozzo G, De Gregorio L, Greifeneder F, Jacob A, Marin C,

Giovanni Cuozzo, Ruth Sonnenschein, Mattia Callegari, Massimiliano Pittore, Alexander Jacob, Claudia Notarnicola, Mariapina Castelli, Carlo Marin, Marc Zebisch

Mountain regions are biodiversity hotspots, support a great diversity of habitats and species, and provide numerous ecosystem services for lowland areas. At the same time, mountain areas are very sensitive environments because of their peculiarities, and are considered as sentinel of climate changes. In fact, despite large uncertainties (Kohler et al., 2014; Stocker et al., 2013), the temperature increase in mountains doubles the global average (Kohler et al., 2014) and several parameter changes (e.g., albedo, snow cover) intensify with altitude (Notarnicola, 2020; Pepin et al., 2015). The importance of mountains is acknowledged and supported by numerous global initiatives. The United Nations included mountain ecosystem conservation in its Sustainable Development Goals and in 2019 the GEO Mountain called for the identification of Essential Climate Variables (ECVs) and Biodiversity Variables (EBVs) for high altitudes (Adler, 2018a, 2018b; Shahgedanova et al., 2021; Thornton et al., 2021). Moreover, GEO Mountain is aiming at supporting the different UN frameworks and has a relevant role in several policy agendas such as the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES), the Sendai Framework for Disaster Risk Reduction, the Paris Agreement, and assessments of the Intergovernmental Panel on Climate Change (IPCC). With the advent of the Copernicus era, Earth Observation (EO) tools in terms of imagery, platforms, and software offer a unique opportunity to continuously monitor the mountain environment and provide valuable information for its protection and safety. This chapter addresses the current and future challenges of EO observations in the different sectors, going from the cryosphere to water resources and agriculture, to ecosystems, and to gravity-driven natural hazards. Current data and processing platforms are presented to provide an overview of the plethora of information and tools now made available to researchers and practitioners. In this chapter, we selected these sectors based on our experience in several national and international projects, where EO data play a major role. Moreover, the analysis of these sections can provide a connection thread on how the changes in snow and glaciers impact the water resources and then consequently agriculture and ecosystems. It is out of the scope of this chapter to draw a complete picture of all the sectors that can be affected by climate changes.

DOI

DOI: 10.1016/B978-0-12-822095-5.00014-0

Conference proceedings article2024

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

Crippa C, Cuozzo G, Callegari M, Notarnicola C

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Chiara Crippa

Progetto: PNRR iNEST - Ecosistema interconnesso dell'innovazione del Nord Est

We propose a tool that integrates spaceborne radar data from Sentinel-1 and multispectral data from Sentinel-2 satellites to detect fresh debris accumulation or rock exposure on alpine glaciers. The tool leverages the VH cross-polarized backscatter difference between Sentinel-1 radar images to pinpoint clusters of pixels exhibiting prominent changes over time. It then integrates the NDSI information from Sentinel-2 multispectral images to discriminate between potential causes of the observed backscatter variations, such as snow cover changes, rock exposure or debris accumulation. The correlation between time series of VH values further allows to distinguish between localized phenomena like landslides and extensive ones like glacial surface changes due to snow metamorphism or accumulation. The tool is implemented in Google Earth Engine (GEE) and leverages open-source libraries and datasets, making it readily adaptable to other glacial environments.

DOI

DOI: 10.1117/12.3037075

Conference proceedings article2023

MOOC EOODS - Massive Open Online Course for Earth Observation and Open Data Science: A course to educate the next generation of EO researchers in data cubes, cloud platforms, and open science

Zellner PG, Eckardt R, Meißl S, Dolezalova T, Eberle J, Claus M, Callegari M, Jacob A,

Mattia Callegari, Alexander Jacob, Michele Claus

Progetto: MOOC EO Open Data Science

The Massive Open Online Course – Earth Observation Open Data Science (MOOC EOODS) teaches the concepts of data cubes, cloud platforms, and open science in the context of Earth Observation (EO). The course is designed to bridge the gap towards the recent cloud native advancements in EO by offering a MOOC as an integrated and open learning experience relying on a mixture of animated lecture content and hands-on exercises hosted on the EO themed e-learning platform EO College.

DOI

DOI: 10.1109/IGARSS52108.2023.10282262

Conference proceedings article2023

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Pettinato S, Santi E, Paloscia S, Baroni F, Pilia S, Santurri L, Palchetti E, Bovenga F,

Giovanni Cuozzo, Mattia Callegari, Valentina Premier, Claudia Notarnicola, Carlo Marin

This research intends to exploit the potentialities of multimission SAR data at X-, C- and L-band to monitor the snowpack and alpine soil. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism were monitored and different algorithms are proposed for the retrieval of these parameters. Several methods and techniques have been used depending on the parameter to be returned: machine learning approaches as Artificial Neural Networks and Random Forest were implemented in case of snow parameter retrieval; interferometric techniques are also involved in case of snow and soil displacement as rock glaciers. Microwave models are used to better explain the snow and soil behavior in the microwave range (AIEM, Oh, SFT and DMRT-QCA), and SNOWPACK physical model for corroborate experimental data. Moreover, experimental activities have been conducted in two selected sites in Northern Italy, which are characterized, in some cases, by the presence of permafrost and they are covered by alpine snow during the winter and spring period. Preliminary and consistent results have been obtained in terms of snow parameters and soil displacement. This comprehensive approach enhances the ability to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

DOI

DOI: 10.1109/IGARSS52108.2023.10281448

Journal article2023

Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins

Shrestha S, Zaramella M, Callegari M, Greifeneder F, Borga M

Mattia Callegari

This study aims to evaluate the potential of ERA5 precipitation and temperature reanalysis
for snow water equivalent (SWE) simulation by considering the role of catchment spatial scale in
controlling the errors obtained by comparison with corresponding SWE simulations from ground
stations. This is obtained by exploiting a semi-distributed snowpack model (TOPMELT) implemented
over the upper Adige River basin in the Eastern Italian Alps, where 16 sub-catchments of varying sizes
are considered. The comparison is carried out from 1992 to 2019. The findings show that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity
rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity
rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5
temperature, resulted in delayed snowmelt and increased fictitious snow-cover days over the study
area. The quantile mapping (QM) technique was used to remove errors in ERA5 variables. It was
shown that ERA5 could struggle to resolve the orographic enhancement in precipitation, which may
be particularly important during high-SWE years. This reduces the positive precipitation bias during
those years, thus reducing comparatively the ability of the quantile mapping technique to correct
for bias homogeneously during all years. This study highlighted the importance of temperature
correction over precipitation correction in SWE simulation, particularly for smaller basins.

DOI

DOI: 10.3390/cli11070154

Conference proceedings article2023

SCIA Project: Development of algorithms for generating products related to Cryosphere by exploiting Prisma Hyperspectal Data

De Gregorio L, Callegari M, Colombo R, Cremonese E, Di Mauro B, Garzonio R, Giardino C, Marin C,

Mattia Callegari, Claudia Notarnicola, Carlo Marin

The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

DOI

DOI: 10.1109/IGARSS52108.2023.10283123

Journal article2023

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Bertone A, Seppi R, Callegari M, Cuozzo G, Dematteis N, Krainer K, Marin C, Notarnicola C,

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Carlo Marin

The kinematic acceleration of rock glaciers observed in recent decades shows that the behavior of these landforms is related to climate change. Velocity variations on yearly to seasonal time scales are frequently investigated, but velocity changes measured on shorter time scales (i.e., on hourly resolutions) are as yet poorly investigated. We used a ground based synthetic aperture radar to investigate, on an hourly time scale, the displacement of a rock glacier located in Val Senales (European Alps, northern Italy). We observed velocity fluctuations occurring at a very regular pace, characterized by phases of sharp acceleration (up to 0.9 mm/hr) lasting 4–11 hr followed by long phases of stagnation lasting 13–20 hr. This study describes an unprecedented observation of an hourly velocity rhythm of an active rock glacier and opens up new perspectives in the analysis and interpretation of rock glacier kinematics.

DOI Fonte

DOI: 10.1029/2023GL102796

Journal article2022

Combined Use of Sentinel-1 and Sentinel-2 for Glacier Mapping: an Application over Central East Alps

Barella R, Callegari M, Marin C, Klug C, Sailer R, Galos S, Dinale R, Gianinetto M,

Mattia Callegari, Claudia Notarnicola, Carlo Marin

The systematic monitoring of glaciers is essential to both evaluate water resources availability and to better understand the effects of climate change. The increased speed of glacier changes observed in the last years requires a more frequent update of the glacier inventories than in the past; however, the high human supervision required by the state-of-the-art techniques is discouraging their systematic application over large areas. This paper proposes a novel approach to exploit the large volume of data provided by Copernicus Sentinel missions for detecting glacier outlines, including debris-covered glaciers. In detail, our method exploits the Sentinel-1 and Sentinel-2 multi-temporal images to build a composite image representing the glaciers conditions during the yearly maximum ablation period. The Sentinel-2 multispectral images are classified with a support vector machine (SVM) and composed to a mosaic that represents the information of the maximum glacier ablation. At the same time, the Sentinel-1 time series are exploited to build a multitemporal coherence composite that represents all the snow covered and glaciated areas together with all the moving surfaces. This information is used together with the Sentinal-2 composite to detect the debris-covered part of the glaciers. The proposed method was tested in the Central-East Alps and presented an overall accuracy of 92% with respect to a reference inventory over South Tyrol and an agreement of 90% with respect to the latest glacier inventory of the Alps from Sentinel-2. The proposed approach enables to assist glacier experts in identifying glacier outlines over large areas and in short time.

DOI

DOI: 10.1109/JSTARS.2022.3179050

Conference proceedings article2022

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

Pettinato S, Bovenga F, Santi E, Paloscia S, Baroni F, Belmonte A, Refice A, Argentiero I,

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Carlo Marin

Progetto: Applicazioni SAR multifrequenza alla criosfera

This work aims to consider the use of different SAR operating at X-, C- and L-band in order to improve the monitoring of snow covered areas and soil in Alpine areas. A preliminary dataset of X-, C- and L-band is acquired and analysed in order to exploit the potentialities in snow and permafrost monitoring. The intensity and phase of the SAR signal at different frequencies shows sensitivity for estimating Snow Water Equivalent and permafrost characteristics.

DOI Fonte

DOI: 10.23919/EuMC54642.2022.9924399

Conference proceedings article2021

A low-cost portable automatic system for snow surface roughness measurements based on digital photography

Barella R, Marin C, Callegari M, Gianinetto M, Moranduzzo T, Notarnicola C

Mattia Callegari, Claudia Notarnicola, Carlo Marin

In this paper, we present a system for snow roughness measurement. The system is made up of digital camera and a black Forex panel. The whole system has been designed for being low cost and portable; and will be distributed as an open-source Python software. The system is based on the coregistration of the photos taken at the panel on the field with a reference scaled image with pixel size of 0.05 mm. In detail, the coregistration process is done by computing the homography, whose parameters are estimated exploiting automatic key-points matching. Among all methods for key-points extraction, we used the scale invariant feature transform (SIFT) because of its robustness and recognized efficiency. After key-points extraction, the matching has been performed using a brute force matching (BFM) approach. The proposed system has been tested under different conditions, showing to be an effective and easy approach to snow surface roughness measurement, as well as robust.

DOI

DOI: 10.1109/IGARSS47720.2021.9553989

Journal article2020

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Marin C, Bertoldi G, Premier V, Callegari M, Brida C, Hürkamp K, Tschiersch J, Zebisch M,

Mattia Callegari, Giacomo Bertoldi, Valentina Premier, Claudia Notarnicola, Marc Zebisch, Carlo Marin

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

DOI Fonte

DOI: 10.5194/tc-14-935-2020

Technical
Questi cookie sono necessari per il funzionamento del sito e non possono essere disattivati.

Service	Disattivo/Attivo	Terms Link
Youtube		https://support.google.com/youtube/answer/7671399?...
Vimeo		https://vimeo.com/terms
Flourish		https://flourish.studio/terms/
Datawrapper		https://www.datawrapper.de/terms
Power BI		https://www.microsoft.com/en/servicesagreement/
Facebook		https://www.facebook.com/policies/cookies/
Instagram		https://help.instagram.com/1896641480634370?ref=ig
X		https://help.twitter.com/en/rules-and-policies/twi...
Google Maps		https://maps.google.com/help/terms_maps/
LinkedIn		https://www.linkedin.com/legal/cookie-policy

Presentation2024

Cubes & Clouds – A Massive Open Online Course for Cloud Native Open Data Sciences in Earth Observation

Zellner PJ, Balogun RO, Eckardt R, Hodam H, Dolezalova T, Meißl S, Eberle J, Claus M,

Mattia Callegari, Alexander Jacob, Michele Claus

Progetto: MOOC EO Open Data Science

DOI Fonte

DOI: 10.5194/egusphere-egu24-21733

Mattia Callegari

About

Scientific Publications

Cubes & Clouds – A Massive Open Online Course for Cloud Native Open Data Sciences in Earth Observation

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

MOOC EOODS - Massive Open Online Course for Earth Observation and Open Data Science: A course to educate the next generation of EO researchers in data cubes, cloud platforms, and open science

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins

SCIA Project: Development of algorithms for generating products related to Cryosphere by exploiting Prisma Hyperspectal Data

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Combined Use of Sentinel-1 and Sentinel-2 for Glacier Mapping: an Application over Central East Alps

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

A low-cost portable automatic system for snow surface roughness measurements based on digital photography

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Science Shots Eurac Research Newsletter

Cosa facciamo

Dove siamo

Istituti & Center

Seguici

Preferenze Cookie

Contenuti esterni

Scientific Publications

Cubes & Clouds – A Massive Open Online Course for Cloud Native Open Data Sciences in Earth Observation

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

MOOC EOODS - Massive Open Online Course for Earth Observation and Open Data Science: A course to educate the next generation of EO researchers in data cubes, cloud platforms, and open science

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins

SCIA Project: Development of algorithms for generating products related to Cryosphere by exploiting Prisma Hyperspectal Data

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Combined Use of Sentinel-1 and Sentinel-2 for Glacier Mapping: an Application over Central East Alps

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

A low-cost portable automatic system for snow surface roughness measurements based on digital photography

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions