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Cited by 278

The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.

Priority list of biodiversity metrics to observe from space

Skidmore, Andrew K., Coops, Nicholas C., Neinavaz, Elnaz, Ali, Abebe, Schaepman, Michael E., Paganini, Marc, Kissling, W. Daniel, Vihervaara, Petteri, Darvishzadeh, Roshanak, Feilhauer, Hannes, Fernandez, Miguel, Fernández, Néstor, Gorelick, Noel, Geijzendorffer, Ilse, Heiden, Uta, Heurich, Marco, Hobern, Donald, Holzwarth, Stefanie, Muller-Karger, Frank E., Van De Kerchove, Ruben, Lausch, Angela, Leitão, Pedro J., Lock, Marcelle C., Mücher, Caspar A., O’Connor, Brian, Rocchini, Duccio, Roeoesli, Claudia, Turner, Woody, Vis, Jan Kees, Wang, Tiejun, Wegmann, Martin, Wingate, Vladimir·2021·Nature Ecology & Evolution
DOI 10.1038/s41559-021-01451-x
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Abstract

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.

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Figures

Fig. 1 Ranking and scoring approach for example remote sensing products. Tropical tree species distribution image adapted from ref. 18 under a Creative Commons license CC BY 4.0; vegetation canopy height image adapted with permission from ref. 25 , Elsevier; functional composition of temperate forest image adapted from ref. 46 under a Creative Commons license CC BY 4.0; land cover image adapted from ref. 34 under a Creative Commons license CC BY 4.0; fraction of absorbed photosynthetically active radiation image adapted from ref. 27 , Copernicus Global Land Service (contains modified Copernicus Service information [2020]).
Fig. 1 Ranking and scoring approach for example remote sensing products. Tropical tree species distribution image adapted from ref. 18 under a Creative Commons license CC BY 4.0; vegetation canopy height image adapted with permission from ref. 25 , Elsevier; functional composition of temperate forest image adapted from ref. 46 under a Creative Commons license CC BY 4.0; land cover image adapted from ref. 34 under a Creative Commons license CC BY 4.0; fraction of absorbed photosynthetically active radiation image adapted from ref. 27 , Copernicus Global Land Service (contains modified Copernicus Service information [2020]).CC-BY · From the PDF
Fig. 2 Flow chart for the scoring and ranking of remote sensing biodiversity products. Note that SDGs are the UN Sustainable Development Goals, Aichi targets refer to the Convention on Biological Diversity Aichi targets, and GEOSS is the Global Earth Observation System of Systems.
Fig. 2 Flow chart for the scoring and ranking of remote sensing biodiversity products. Note that SDGs are the UN Sustainable Development Goals, Aichi targets refer to the Convention on Biological Diversity Aichi targets, and GEOSS is the Global Earth Observation System of Systems.CC-BY · From the PDF
Fig. 3 Example prioritization of three remote sensing biodiversity products. See Table 1 for definition of the ranking factors (that is, 1 = good, 3 = poor) and Supplementary Table 2 for merged remote sensing biodiversity products list and details of the expert ranking. Biological effects of fire disturbance image reproduced from ref. 62 under a Creative Commons license CC BY 3.0; land cover image adapted from ref. 34 under a Creative Commons license CC BY 4.0; fraction of absorbed photosynthetically active radiation (FAPAR) image adapted from ref. 26 , Copernicus Global Land Service (contains modified Copernicus Service information [2020]).
Fig. 3 Example prioritization of three remote sensing biodiversity products. See Table 1 for definition of the ranking factors (that is, 1 = good, 3 = poor) and Supplementary Table 2 for merged remote sensing biodiversity products list and details of the expert ranking. Biological effects of fire disturbance image reproduced from ref. 62 under a Creative Commons license CC BY 3.0; land cover image adapted from ref. 34 under a Creative Commons license CC BY 4.0; fraction of absorbed photosynthetically active radiation (FAPAR) image adapted from ref. 26 , Copernicus Global Land Service (contains modified Copernicus Service information [2020]).CC-BY · From the PDF

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Research works printed but not linked: 2

  1. Morsdorf, F. et al. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 83–104 (Springer International, 2020).
  2. National Academies of Sciences, Engineering, and Medicine Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018).

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Metadata

Authors
Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., Fernandez, M., Fernández, N., Gorelick, N., Geijzendorffer, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., Van De Kerchove, R., Lausch, A., Leitão, P. J., Lock, M. C., Mücher, C. A., O’Connor, B., Rocchini, D., Roeoesli, C., Turner, W., Vis, J. K., Wang, T., Wegmann, M., & Wingate, V.
ORCID
Venue
Nature Ecology & Evolution
Publisher
Springer Science and Business Media LLC
Published
2021-05-13
Volume
5
Issue
7
Pages
896-906
ISSN
2397-334X
DOI
10.1038/s41559-021-01451-x
Cited by
278
References
118
Language
en
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