Reference Model Guided System Design and Implementation for Interoperable Environmental Research Infrastructures

Zhao, Zhiming; Martin, Paul; Grosso, Paola; Los, Wouter; Laat, Cees de; Jeffrey, Keith; Hardisty, Alex; Vermeulen, Alex; Castelli, Donatella; Legré, Yannick; Kutsch, Werner L.

doi:10.1109/escience.2015.41

Cited by 24 publications

(29 citation statements)

References 14 publications

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“…There exist first very good environmental research infrastructures (RIs) for the implementation of data science and existing forest health networks for assembly a MUSO-FH-MN called ENVRIplus (http://www.envriplus.eu/). ENVRIplus bringing together environmental and earth system research infrastructures, research networks and projects together to create a more coherent, interdisciplinary, standardizied and interoperable cluster of environmental research infrastructures across europe [249].…”

Section: Resultsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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Section: Resultsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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“…Environmental research infrastructures (RIs) support their respective research communities by integrating large-scale sensor/observation networks (mainly in-situ) with data curation services, analytical tools and common operational policies (Zhao et al 2015) to conduct top-level research in their respective fields. ENVRIplus is a big network of in-situ research infrastructures covering most of the domains of Earth system scienceatmospheric, marine, biosphere and solid Earth to work together, capitalize the progress made in various disciplines and strengthen interoperability amongst Research Infrastructures and domains.…”

Section: Research Infrastructuresmentioning

confidence: 99%

“…ENVRIplus is a big network of in-situ research infrastructures covering most of the domains of Earth system scienceatmospheric, marine, biosphere and solid Earth to work together, capitalize the progress made in various disciplines and strengthen interoperability amongst Research Infrastructures and domains. It intends to address this interoperability challenge as it relates to the design, implementation and operation of environmental science RIs, focusing on key issues of data identification and citation, curation, cataloguing, processing, optimization, and provenance, supported by a generic cross-infrastructure reference model (Zhao et al 2015).…”

Section: Research Infrastructuresmentioning

confidence: 99%

GEOEssential – mainstreaming workflows from data sources to environment policy indicators with essential variables

Lehmann

Nativi

Mazzetti

et al. 2019

International Journal of Digital Earth

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When defining indicators on the environment, the use of existing initiatives should be a priority rather than redefining indicators each time. From an Information, Communication and Technology perspective, data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations. GEOEssential is following an end-user driven approach by defining Essential Variables (EVs), as an intermediate value between environmental policy indicators and their appropriate data sources. From international to local scales, environmental policies and indicators are increasingly percolating down from the global to the local agendas. The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps. To this aim, GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows, which are stored in a dedicated knowledge base, generating executable workflows. To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential (i.e. biodiversity, ecosystems, extractives, night light, and food-water-energy nexus), a Dashboard is built as a visual front-end. This is a valuable instrument to track progresses towards environmental policies.

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“…The research data infrastructure must therefore not only provide the necessary tools for data discovery, access and manipulation but should also facilitate and enhance the collaboration between scientists of different backgrounds (Zhao et al, 2015). The use of a transdisciplinary research approach (Hadorn et al, 2008) is key to solving many problems related to the impact of climate change on the environment and agriculture.…”

Section: Introductionmentioning

confidence: 99%

An interoperable research data infrastructure to support climate service development

2018

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Abstract. Accessibility, availability, re-use and re-distribution of scientific data are prerequisites to build climate services across Europe. From this perspective the Institute of Biometeorology of the National Research Council (IBIMET-CNR), aiming at contributing to the sharing and integration of research data, has developed a research data infrastructure to support the scientific activities conducted in several national and international research projects. The proposed architecture uses open-source tools to ensure sustainability in the development and deployment of Web applications with geographic features and data analysis functionalities. The spatial data infrastructure components are organized in typical client-server architecture and interact from the data provider download data process to representation of the results to end users. The availability of structured raw data as customized information paves the way for building climate service "purveyors" to support adaptation, mitigation and risk management at different scales.This work is a bottom-up collaborative initiative between different IBIMET-CNR research units (e.g. geomatics and information and communication technology -ICT; agricultural sustainability; international cooperation in least developed countries -LDCs) that embrace the same approach for sharing and re-use of research data and informatics solutions based on co-design, co-development and co-evaluation among different actors to support the production and application of climate services. During the development phase of Web applications, different users (internal and external) were involved in the whole process so as to better define user needs and suggest the implementation of specific custom functionalities. Indeed, the services are addressed to researchers, academics, public institutions and agencies -practitioners who can access data and findings from recent research in the field of applied meteorology and climatology.

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Reference Model Guided System Design and Implementation for Interoperable Environmental Research Infrastructures

Cited by 24 publications

References 14 publications

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

GEOEssential – mainstreaming workflows from data sources to environment policy indicators with essential variables

An interoperable research data infrastructure to support climate service development

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