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  1. Article ; Online: One decade (2011–2020) of European agricultural water stress monitoring by MSG-SEVIRI: workflow implementation on the Virtual Earth Laboratory (VLab) platform

    Bayat, Bagher / Montzka, Carsten / Graf, Alexander / Giuliani, Gregory / Santoro, Mattia / Vereecken, H.

    International Journal of Digital Earth. 2022 Dec. 31, v. 15, no. 1 p.730-747

    2022  

    Abstract: Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on ... ...

    Abstract Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on cloud-based platforms, for ingesting the satellite data, performing the targeted processes, and generating the desired products. In this study, an operational workflow is proposed, based on monthly Evaporative Stress Index (ESI) anomaly, and implemented in cloud-based online Virtual Earth Laboratory (VLab) platform, as a demonstration, to monitor European agricultural water stress. To this end, daily time-series of actual and reference evapotranspiration (ETₐ and ET₀), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, were used to execute the proposed workflow successfully on VLab. The execution of the workflow resulted in obtaining one decade (2011–2020) of European monthly agricultural water stress maps at 0.04˚ spatial resolution and corresponding stress reports for each country. To support open science, all the workflow outputs are stored in GeoServer, documented in GeoNetwork, and made available through MapStore. This enables creating a dashboard for better visualization of the results for end-users. The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.
    Keywords evapotranspiration ; remote sensing ; time series analysis ; water stress ; ET ; SEVIRI ; ESI ; water stress workflow ; Europe ; VLab demonstration
    Language English
    Dates of publication 2022-1231
    Size p. 730-747.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ZDB-ID 2410527-2
    ISSN 1753-8955
    ISSN 1753-8955
    DOI 10.1080/17538947.2022.2061617
    Database NAL-Catalogue (AGRICOLA)

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  2. Article: Towards a knowledge base to support global change policy goals

    Nativi, Stefano / Santoro, Mattia / Giuliani, Gregory / Mazzetti, Paolo

    International journal of digital earth. 2020 Feb. 1, v. 13, no. 2

    2020  

    Abstract: In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO ...

    Abstract In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.
    Keywords computer software ; data collection ; decision making ; ecosystems ; global change ; issues and policy ; poverty ; sustainable development
    Language English
    Dates of publication 2020-0201
    Size p. 188-216.
    Publishing place Taylor & Francis
    Document type Article
    ZDB-ID 2410527-2
    ISSN 1753-8955 ; 1753-8947
    ISSN (online) 1753-8955
    ISSN 1753-8947
    DOI 10.1080/17538947.2018.1559367
    Database NAL-Catalogue (AGRICOLA)

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  3. Article: The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition

    Santoro, Mattia / Mazzetti, Paolo / Nativi, Stefano

    Remote Sensing. 2020 June 02, v. 12, no. 11

    2020  

    Abstract: Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition ... ...

    Abstract Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition from data to knowledge, often requiring to process big datasets through complex physical or empirical (learning-based AI) models. Although cloud technologies provide valuable solutions for addressing several of the Big Earth Data challenges, model sharing is still a complex task. The usual approach of sharing models as services requires maintaining a scalable infrastructure which is often a very high barrier for potential model providers. This paper describes the Virtual Earth Laboratory (VLab), a software framework orchestrating data and model access to implement scientific processes for knowledge generation. The VLab lowers the entry barriers for both developers and users. It adopts mature containerization technologies to access models as source code and to rebuild the required software environment to run them on any supported cloud. This makes VLab fitting in the multi-cloud landscape, which is going to characterize the Big Earth Data analytics domain in the next years. The VLab functionalities are accessible through APIs, enabling developers to create new applications tailored to end-users.
    Keywords computer software ; data analysis ; data collection ; decision making ; infrastructure ; issues and policy ; landscapes ; models ; remote sensing
    Language English
    Dates of publication 2020-0602
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs12111795
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  4. Article: Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base

    Mazzetti, Paolo / Nativi, Stefano / Santoro, Mattia / Giuliani, Gregory / Rodila, Denisa / Folino, Antonietta / Caruso, Susie / Aracri, Giovanna / Lehmann, Anthony

    Environmental science & policy. 2022 May, v. 131

    2022  

    Abstract: During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the ... ...

    Abstract During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the local and the global level. Therefore, there is a strong need that policy-making and decision-making processes for a sustainable development be based on the best available knowledge about Earth system and environment. The recent advance of information technologies enables running complex models that use the large amount of Earth Observation datasets available. However, data and model interoperability are still limited to the syntactic level allowing to access and process datasets independently of their structural characteristics (data format, coordinate reference systems, service interface, …) but with no clear reference to their content (the semantic level) and context of use (the pragmatic level). This poses heavy limitations to the reusability of scientific processes and related workflows. The paper presents a general framework to address this issue through the design of a Knowledge Base supporting data and model semantic (and pragmatic) interoperability. In this framework, a general ontology represents the knowledge generation process for policy relevant decision-making, while multiple vocabularies formalize the semantics of data and models, identifying different types of observables, process variables, and indicators/indices. To evaluate the proposed approach to semantic interoperability of data and models, the Knowledge Base has been integrated with an advanced model-sharing framework, and a proof-of-concept has been developed for the assessment of one of the indicators of the Sustainable Development Goals defined by the United Nations.
    Keywords Earth system science ; data collection ; decision making ; environmental science ; humans ; issues and policy ; politics ; sustainable development
    Language English
    Dates of publication 2022-05
    Size p. 93-104.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 1454687-5
    ISSN 1462-9011
    ISSN 1462-9011
    DOI 10.1016/j.envsci.2021.12.023
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  5. Article: A Revised Snow Cover Algorithm to Improve Discrimination between Snow and Clouds: A Case Study in Gran Paradiso National Park

    Richiardi, Chiara / Blonda, Palma / Rana, Fabio Michele / Santoro, Mattia / Tarantino, Cristina / Vicario, Saverio / Adamo, Maria

    Remote Sensing. 2021 May 18, v. 13, no. 10

    2021  

    Abstract: Snow cover plays an important role in biotic and abiotic environmental processes, as well as human activities, on both regional and global scales. Due to the difficulty of in situ data collection in vast and inaccessible areas, the use of optical ... ...

    Abstract Snow cover plays an important role in biotic and abiotic environmental processes, as well as human activities, on both regional and global scales. Due to the difficulty of in situ data collection in vast and inaccessible areas, the use of optical satellite imagery represents a useful support for snow cover mapping. At present, several operational snow cover algorithms and products are available. Even though most of them offer an up-to-daily time scale, they do not provide sufficient spatial resolution for studies requiring high spatial detail. By contrast, the Let-It-Snow (LIS) algorithm can produce high-resolution snow cover maps, based on the use of both the normalized-difference snow index (NDSI) and a digital elevation model. The latter is introduced to define a threshold value on the altitude, below which the presence of snow is excluded. In this study, we revised the LIS algorithm by introducing a new parameter, based on a threshold in the shortwave infrared (SWIR) band, and by modifying the overall algorithm workflow, such that the cloud mask selection can be used as an input. The revised algorithm has been applied to a case study in Gran Paradiso National Park. Unlike previous studies, we also compared the performance of both the original and the modified algorithms in the presence of cloud cover, in order to evaluate their effectiveness in discriminating between snow and clouds. Ground data collected by meteorological stations equipped with both snow gauges and solarimeters were used for validation purposes. The changes introduced in the revised algorithm can improve upon the overall classification accuracy obtained by the original LIS algorithm (i.e., up to 89.17 from 80.88%). The producer’s and user’s accuracy values obtained by the modified algorithm (89.12 and 95.03%, respectively) were larger than those obtained by the original algorithm (76.68 and 93.67%, respectively), thus providing a more accurate snow cover map.
    Keywords algorithms ; altitude ; case studies ; cloud cover ; data collection ; digital elevation models ; humans ; national parks ; remote sensing ; snow ; snowpack
    Language English
    Dates of publication 2021-0518
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs13101957
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  6. Article: Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation

    Giuliani, Gregory / Mazzetti, Paolo / Santoro, Mattia / Nativi, Stefano / Van Bemmelen, Joost / Colangeli, Guido / Lehmann, Anthony

    International journal of applied earth observation and geoinformation. 2020 June, v. 88

    2020  

    Abstract: Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental Science-Policy Platform on ... ...

    Abstract Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Landmark Assessment Report highlighted that human activities are considerably degrading land and threating the well-being of approximately 3.2 billion people. In order to reduce and ideally reverse this prevailing situation, national capacities should be strengthened to enable effective assessments and mapping of their degraded lands as recommended by the United Nations Sustainable Development Goals (SDGs). The indicator 15.3.1 (“proportion of land that is degraded over total land area”) requires regular data production by countries to inform and assess it through space and time. Earth Observations (EO) can play an important role both for generating the indicator in countries where it is missing, as well complementing or enhancing national official data sources. In response to this issue, this paper presents an innovative, scalable and flexible approach to monitor land degradation at various scales (e.g., national, regional, global) using various components of the Global Earth Observation System of Systems (GEOSS) platform to leverage EO resources for informing SDG 15.3.1. The proposed approach follows the Data-Information-Knowledge pattern using the Trends.Earth model (http://trends.earth) and various data sources to generate the indicator. It also implements additional components for model execution and orchestration, knowledge management, and visualization. The proposed approach has been successfully applied at global, regional and national scales and advances the vision of (1) establishing data analytics platforms that can potentially support countries to discover, access and use the necessary datasets to assess land degradation; and (2) developing new capacities to effectively and efficiently use EO-based resources.
    Keywords biodiversity ; data analysis ; data collection ; ecosystems ; humans ; information management ; land degradation ; models ; people ; quality of life ; satellites ; space and time ; spatial data ; sustainable development ; vision
    Language English
    Dates of publication 2020-06
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 1569-8432
    DOI 10.1016/j.jag.2020.102068
    Database NAL-Catalogue (AGRICOLA)

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  7. Article: Contributing to the GEO Model Web implementation: A brokering service for business processes

    Santoro, Mattia / Paolo Mazzetti / Stefano Nativi

    Environmental modelling & software. 2016 Oct., v. 84

    2016  

    Abstract: In the Earth system science domain, most current digital infrastructures are able to support data access rather than provide answers to complex questions – noticeably, supporting the ability to address the what-if questions posed by users. To this ... ...

    Abstract In the Earth system science domain, most current digital infrastructures are able to support data access rather than provide answers to complex questions – noticeably, supporting the ability to address the what-if questions posed by users. To this purpose, integrated modeling is an indispensable element. This work follows a recognizable gap characterized by high-level steps towards model integration: the Science-to-Information Technology barrier. This manuscript introduces an innovative approach to address such a gap in the “Model Web” framework initiative promoted by GEO (Group on Earth Observation) on its GEOSS (Global Earth Observation System of Systems) program. The methodological approach consists of five technologically neutral steps to build executable workflows from abstract business processes. A high-level architecture, based on the brokering pattern, is introduced to implement the proposed approach. By extending the GEOSS brokering framework, a proof-of-concept implementation is presented, argued, and ultimately experimentations and conclusions are discussed.
    Keywords business enterprises ; computer software ; Earth system science ; environmental models ; infrastructure
    Language English
    Dates of publication 2016-10
    Size p. 18-34.
    Publishing place Elsevier Ltd
    Document type Article
    ISSN 1364-8152
    DOI 10.1016/j.envsoft.2016.06.010
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  8. Article: Architecture of a Process Broker for Interoperable Geospatial Modeling on the Web

    Bigagli, Lorenzo / Santoro, Mattia / Mazzetti, Paolo / Nativi, Stefano

    ISPRS international journal of geo-information. 2015 Apr. 20, v. 4, no. 2

    2015  

    Abstract: The identification of appropriate mechanisms for process sharing and reuse by means of composition is considered a key enabler for the effective uptake of a global Earth Observation infrastructure, currently pursued by the international geospatial ... ...

    Abstract The identification of appropriate mechanisms for process sharing and reuse by means of composition is considered a key enabler for the effective uptake of a global Earth Observation infrastructure, currently pursued by the international geospatial research community. Modelers in need of running complex workflows may benefit from outsourcing process composition to a dedicated external service, according to the brokering approach. This work introduces our architecture of a process broker, as a distributed information system for creating, validating, editing, storing, publishing and executing geospatial-modeling workflows. The broker provides a service framework for adaptation, reuse and complementation of existing processing resources (including models and geospatial services in general) in the form of interoperable, executable workflows. The described solution has been experimentally applied in several use scenarios in the context of EU-funded projects and the Global Earth Observation System of Systems.
    Keywords information systems ; infrastructure ; models
    Language English
    Dates of publication 2015-0420
    Size p. 647-660.
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    Note Journal Article
    ZDB-ID 2655790-3
    ISSN 2220-9964
    ISSN 2220-9964
    DOI 10.3390/ijgi4020647
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  9. Article: Essential earth observation variables for high-level multi-scale indicators and policies

    Lehmann, Anthony / Mazzetti, Paolo / Santoro, Mattia / Nativi, Stefano / Masò, Joan / Serral, Ivette / Spengler, Daniel / Niamir, Aidin / Lacroix, Pierre / Ambrosone, Mariapaola / McCallum, Ian / Kussul, Nataliia / Patias, Petros / Rodila, Denisa / Ray, Nicolas / Giuliani, Grégory

    Environmental science & policy. 2022 May, v. 131

    2022  

    Abstract: Several holistic approaches are based on the description of socio-ecological systems to address the sustainability challenge. Essential Variables (EVs) have the potential to support these approaches by describing the status of the Earth system through ... ...

    Abstract Several holistic approaches are based on the description of socio-ecological systems to address the sustainability challenge. Essential Variables (EVs) have the potential to support these approaches by describing the status of the Earth system through monitoring and modeling. The different classes of EVs can be organized along the environmental policy framework of Drivers, Pressures, States, Impacts and Responses. The EV concept represents an opportunity to strengthen monitoring systems by providing observations to seize the fundamental dimensions of the Earth system The Group on Earth Observation (GEO) is a partnership of 113 nations and 134 participating organizations in 2021 that are dedicated to making Earth Observation (EO) data available globally to inform about the state of the environment and enable data-driven decision processes. GEO is building the Global Earth Observation System of Systems, a set of coordinated and independent EO, information and processing systems that interoperate to provide access to EO for users in the public and private sectors. The progresses made in the development of various classes of EVs are described with their main policy targets, Internet links and key references The paper reviews the literature on EVs and describes the main contributions of the EU GEOEssential project to integrate EVs within the work plan of GEO in order to better address selected environmental policies and the SDGs. A new GEO-EVs community has been set to discuss about the current status of the EVs, exchange knowledge, experiences and assess the gaps to be solved in their communities of providers and users. A set of four traits characterizing an EV was put forward to describe the entire socio-ecological system of planet Earth: Essentiality, Evolvability, Unambiguity, and Feasibility. A workflow from the identification of EO data sources to the final visualization of SDG 15.3.1 indicators on land degradation is demonstrated, spanning through the use of different EVs, the definition of the knowledge base on this indicator, the implementation of the workflow in the VLab (a cloud-based processing infrastructure), the presentation of the outputs on a dedicated dashboard and the corresponding narrative through a story map. The concept of EV started in the climate sphere and spread to other domains of the earth system but less so in socio-economic activities. More work is therefore needed to converge on a common definition and criteria in order to complete the implementation of EVs in all GEO focus areas. EVs should screen the entire Earth's social-ecological system, providing a trusted and long-term foundation for interdisciplinary approaches such as ecological footprinting, planetary boundaries, disaster risk reduction, and nexus frameworks, as well as many other policy frameworks such as the SDGs
    Keywords Earth system science ; Internet ; climate ; environmental policy ; infrastructure ; land degradation ; social environment ; socioeconomics
    Language English
    Dates of publication 2022-05
    Size p. 105-117.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 1454687-5
    ISSN 1462-9011
    ISSN 1462-9011
    DOI 10.1016/j.envsci.2021.12.024
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  10. Article: Arctic observations and sustainable development goals – Contributions and examples from ERA-PLANET iCUPE data

    Noe, Steffen M. / Tabakova, Ksenia / Mahura, Alexander / Lappalainen, Hanna K. / Kosmale, Miriam / Heilimo, Jyri / Salzano, Roberto / Santoro, Mattia / Salvatori, Rosamaria / Spolaor, Andrea / Cairns, Warren / Barbante, Carlo / Pankratov, Fidel / Humbert, Angelika / Sonke, Jeroen E. / Law, Kathy S. / Onishi, Tatsuo / Paris, Jean-Daniel / Skov, Henrik /
    Massling, Andreas / Dommergue, Aurélien / Arshinov, Mikhail / Davydov, Denis / Belan, Boris / Petäjä, Tuukka

    Environmental science & policy. 2022 June, v. 132

    2022  

    Abstract: Integrative and Comprehensive Understanding on Polar Environments (iCUPE) project developed 24 novel datasets utilizing in-situ observational capacities within the Arctic or remote sensing observations from ground or from space. The datasets covered ... ...

    Abstract Integrative and Comprehensive Understanding on Polar Environments (iCUPE) project developed 24 novel datasets utilizing in-situ observational capacities within the Arctic or remote sensing observations from ground or from space. The datasets covered atmospheric, cryospheric, marine, and terrestrial domains. This paper connects the iCUPE datasets to United Nations’ Sustainable Development Goals and showcases the use of selected datasets as knowledge provision services for policy- and decision-making actions. Inclusion of indigenous and societal knowledge into the data processing pipelines enables a feedback mechanism that facilitates data driven public services.
    Keywords data collection ; decision making ; environmental science ; issues and policy ; sustainable development ; Arctic region
    Language English
    Dates of publication 2022-06
    Size p. 323-336.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 1454687-5
    ISSN 1462-9011
    ISSN 1462-9011
    DOI 10.1016/j.envsci.2022.02.034
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