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Earth Observation Data in Environmental Data Spaces

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 5132

Special Issue Editors

CREAF—Centre for Ecological Research and Forestry Applications, 08193 Barcelona, Spain
Interests: remote sensing; land cover; sustainable development; citizen science
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Departament de Geografia, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
Interests: data; metadata; web semantics; remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, Aston University, Birmingham B4 7ET, UK
Interests: data quality; citizen science; metadata

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Guest Editor
CREAF—Centre for Ecological Research and Forestry Applications, 08193 Barcelona, Spain
Interests: remote sensing; data spaces; research infrastructures; forestry applications

Special Issue Information

Dear Colleagues,

A data space is defined as an infrastructure that enables data transactions between different data ecosystem parties on the basis of a governance framework. Data spaces deploy data-sharing tools and services for processing data in an interoperable way. They include a data governance structure that is compatible with relevant legislation and that stimulates the application of FAIR principles. Underlying data spaces is trust, which is gained by having data quality and provenance documentation, authentication and authorization services, and clear license schemas enabling the reuse of data. Although the concept has become popular, its implementation in the Earth observation domain is still in its infancy.

This Special Issue focuses on the design principles of data spaces for Earth observation data as well as the standards, practices, and implementations that enable better data sharing of remote sensing and in situ environmental observations. In Europe, the Green Deal Data Space and the Agriculture Data Space are two examples of data spaces promoted by the European Commission. This Special Issue is also seeking to publish discussions on other emerging cyberinfrastructures that share a similar aim. Potential topics include, but are not limited to, the following:

  • Data spaces for remote sensing data distribution and processing;
  • Evolving spatial data infrastructures into data spaces;
  • Architecture and components constituting a data space;
  • Standards for developing data spaces;
  • Data processing facilities and protocols;
  • Tools and web services useful in data spaces;
  • Integrating heterogeneous data sources into data spaces;
  • Semantics enabling the integration of data;
  • Artificial intelligence and machine learning in data spaces;
  • Relation between data spaces and digital twins;
  • Catalogues for data spaces;
  • STAC and COG to enable remote sensing data spaces;
  • Authentication, authorization, and transactions in data spaces.

Dr. Joan Masó
Dr. Alaitz Zabala Torres
Dr. Lucy Bastin
Dr. Kaori Otsu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data quality
  • provenance
  • governance
  • processing
  • data space
  • semantics

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Published Papers (2 papers)

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Research

39 pages, 4245 KiB  
Article
Standards for Data Space Building Blocks
by Francesca Noardo, Rob Atkinson, Lucy Bastin, Joan Maso, Ingo Simonis, Alejandro Villar, Marie-Françoise Voidrot and Piotr Zaborowski
Remote Sens. 2024, 16(20), 3824; https://doi.org/10.3390/rs16203824 - 14 Oct 2024
Viewed by 468
Abstract
Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not [...] Read more.
Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years. Full article
(This article belongs to the Special Issue Earth Observation Data in Environmental Data Spaces)
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20 pages, 8858 KiB  
Article
Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach
by Mohammadali Hemati, Masoud Mahdianpari, Hodjat Shiri and Fariba Mohammadimanesh
Remote Sens. 2024, 16(5), 831; https://doi.org/10.3390/rs16050831 - 28 Feb 2024
Cited by 5 | Viewed by 3501
Abstract
Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial [...] Read more.
Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha−1 was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems. Full article
(This article belongs to the Special Issue Earth Observation Data in Environmental Data Spaces)
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