Priority list of biodiversity metrics to observe from space
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review
PubMed
33986541
DOI
10.1038/s41559-021-01451-x
PII: 10.1038/s41559-021-01451-x
Knihovny.cz E-resources
- MeSH
- Biodiversity MeSH
- Ecosystem * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.
Albert Ludwigs University of Freiburg Freiburg Germany
Biodiversity Centre Finnish Environment Institute Helsinki Finland
College of Marine Science University of South Florida St Petersburg FL USA
Computational Landscape Ecology Helmholtz Centre for Environmental Research Leipzig Germany
Department of Biological Geological and Environmental Sciences University of Bologna Bologna Italy
Department of Earth and Environmental Science Macquarie University Sydney New South Wales Australia
Department of Geography and Environmental Studies Wollo University Dessie Ethiopia
Earth Observation Center Oberpfaffenhofen Germany
Earth Science Division NASA Washington DC USA
European Space Research Institute European Space Agency Frascati Italy
Flemish Institute for Technological Research Mol Belgium
GBIF Secretariat Copenhagen Denmark
Geography Department Humboldt University of Berlin Berlin Germany
George Mason University Fairfax VA USA
German Centre for Integrative Biodiversity Research Leipzig Germany
Humboldt Universität zu Berlin Berlin Germany
Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam the Netherlands
Institute of Biology Martin Luther University Halle Wittenberg Halle Germany
Institute of Geographical Sciences Freie Universität Berlin Berlin Germany
Institute of Geography and Geology University of Wuerzburg Würzburg Germany
Remote Sensing Center for Earth System Research University of Leipzig Leipzig Germany
Remote Sensing Laboratories Department of Geography University of Zurich Zurich Switzerland
Technische Universität Braunschweig Braunschweig Germany
UN Environment World Conservation Monitoring Centre Cambridge UK
Unilever Europe B 5 Rotterdam the Netherlands
Wageningen Environmental Research Wageningen University and Research Wageningen the Netherlands
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The Spectral Species Concept in Living Color
