Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Priority list of biodiversity metrics to observe from space

Nature Ecology & Evolution volume 5, pages 896–906 (2021)Cite this article

Subjects

An Author Correction to this article was published on 25 October 2021

An Author Correction to this article was published on 19 July 2021

An Author Correction to this article was published on 24 May 2021

This article has been updated

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Ranking and scoring approach for example remote sensing products.
Fig. 2: Flow chart for the scoring and ranking of remote sensing biodiversity products.
Fig. 3: Example prioritization of three remote sensing biodiversity products.

Change history

References

  1. Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  2. Paganini, M., Leidner, A. K., Geller, G., Turner, W. & Wegmann, M. The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens. Ecol. Conserv. 2, 132–140 (2016).

    Article  Google Scholar 

  3. What are EBVs? GEO BON https://geobon.org/ebvs/what-are-ebvs/ (2020).

  4. Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).

    Article  PubMed  Google Scholar 

  6. Navarro, L. M. et al. Monitoring biodiversity change through effective global coordination. Curr. Opin. Environ. Sustain. 29, 158–169 (2017).

    Article  Google Scholar 

  7. Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).

    Article  Google Scholar 

  8. Lausch, A. et al. Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sens. 10, 1120 (2018).

    Article  Google Scholar 

  9. Barga, R., Gannon, D. & Reed, D. The client and the cloud democratizing research computing. IEEE Internet Comput. 15, 72–75 (2011).

    Article  Google Scholar 

  10. Muller-Karger, F. E. et al. Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems. Ecol. Appl. 28, 749–760 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. O’Connor, B. et al. Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sens. Ecol. Conserv. 1, 19–28 (2015).

    Article  Google Scholar 

  12. Geijzendorffer, I. R. et al. Bridging the gap between biodiversity data and policy reporting needs: an essential biodiversity variables perspective. J. Appl. Ecol. 53, 1341–1350 (2016).

    Article  Google Scholar 

  13. Rohde, S., Hostmann, M., Peter, A. & Ewald, K. C. Room for rivers: an integrative search strategy for floodplain restoration. Landsc. Urban Plan. 78, 50–70 (2006).

    Article  Google Scholar 

  14. Belward, A. The Global Observing System for Climate: Implementation Needs Report No. GCOS-200 (Global Climate Observing System, 2016).

  15. Bojinski, S. et al. The concept of essential climate variables in support of climate research, applications, and policy. Bull. Am. Meteorol. Soc. 95, 1431–1443 (2014).

    Article  Google Scholar 

  16. Wu, J. G. Effects of changing scale on landscape pattern analysis: scaling relations. Landsc. Ecol. 19, 125–138 (2004).

    Article  Google Scholar 

  17. Lake, P. S. Disturbance, patchiness, and diversity in streams. J. N. Am. Benthol. Soc. 19, 573–592 (2000).

    Article  Google Scholar 

  18. Graves, S. J. et al. Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data. Remote Sens. 8, 161 (2016).

    Article  Google Scholar 

  19. Schlerf, M., Atzberger, C. & Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ. 95, 177–194 (2005).

    Article  Google Scholar 

  20. Xue, Y. F., Wang, T. J. & Skidmore, A. K. Automatic counting of large mammals from very high resolution panchromatic satellite imagery. Remote Sens. 9, 878 (2017).

    Article  Google Scholar 

  21. Zhao, M. S., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).

    Article  Google Scholar 

  22. Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).

    Article  Google Scholar 

  23. Curran, P. J., Dungan, J. L. & Peterson, D. L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies. Remote Sens. Environ. 76, 349–359 (2001).

    Article  Google Scholar 

  24. Homolova, L., Maenovsky, Z., Clevers, J., Garcia-Santos, G. & Schaeprnan, M. E. Review of optical-based remote sensing for plant trait mapping. Ecol. Complex. 15, 1–16 (2013).

    Article  Google Scholar 

  25. Khosravipour, A., Skidmore, A. K. & Isenburg, M. Generating spike-free digital surface models using LiDAR raw point clouds: a new approach for forestry applications. Int. J. Appl. Earth Obs. Geoinf. 52, 104–114 (2016).

    Google Scholar 

  26. Verger, A. & Descals, A. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)—300 m Version 1; Algorithm Theoretical Basis Document (ATBD), Issue 1.00 (Framework Service Contract No. 199494-JRC) (Copernicus Global Land Operations CGLOPS-1, 2020).

  27. Copernicus Global Land Service: FAPAR Copernicus https://land.copernicus.eu/global/about (2020).

  28. Schmidt, K. S. et al. Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogramm. Eng. Remote Sens. 70, 703–715 (2004).

    Article  Google Scholar 

  29. Arvor, D., Durieux, L., Andres, S. & Laporte, M. A. Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogramm. Remote Sens. 82, 125–137 (2013).

    Article  Google Scholar 

  30. Lucas, R., Rowlands, A., Brown, A., Keyworth, S. & Bunting, P. Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS J. Photogramm. Remote Sens. 62, 165–185 (2007).

    Article  Google Scholar 

  31. Skidmore, A. K. An expert system classifies eucalypt forest types using Landsat thematic mapper data and a digital terrain model. Photogramm. Eng. Remote Sens. 55, 1449–1464 (1989).

    Google Scholar 

  32. Tuanmu, M. N. & Jetz, W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).

    Article  Google Scholar 

  33. Lausch, A. et al. Understanding and quantifying landscape structure—a review on relevant process characteristics, data models and landscape metrics. Ecol. Model. 295, 31–41 (2015).

    Article  Google Scholar 

  34. Buchhorn, M. et al. Copernicus global land cover layers—Collection 2. Remote Sens. 12, 1044 (2020).

    Article  Google Scholar 

  35. Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: a call for caution. Glob. Ecol. Biogeogr. 26, 930–941 (2017).

    Article  Google Scholar 

  36. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Pekel, J., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Ye, H. et al. Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model. J. Mt. Sci. 16, 323–336 (2019).

    Article  Google Scholar 

  39. Curran, P. J. & Steele, C. M. MERIS: the re-branding of an ocean sensor. Int. J. Remote Sens. 26, 1781–1798 (2005).

    Article  Google Scholar 

  40. Garrigues, S., Allard, D., Baret, F. & Weiss, M. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sens. Environ. 105, 286–298 (2006).

    Article  Google Scholar 

  41. Wu, S. B. et al. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations. ISPRS J. Photogramm. Remote Sens. 171, 36–48 (2021).

    Article  Google Scholar 

  42. Salcedo-Sanz, S. et al. Machine learning information fusion in Earth observation: a comprehensive review of methods, applications and data sources. Inf. Fusion 63, 256–272 (2020).

    Article  Google Scholar 

  43. Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).

    Article  PubMed  Google Scholar 

  44. Healy, C., Gotelli, N. J. & Potvin, C. Partitioning the effects of biodiversity and environmental heterogeneity for productivity and mortality in a tropical tree plantation. J. Ecol. 96, 903–913 (2008).

    Article  Google Scholar 

  45. Richards, J. A., Woodgate, P. W. & Skidmore, A. K. An explanation of enhanced radar backscattering from flooded forests. Int. J. Remote Sens. 8, 1093–1100 (1987).

    Article  Google Scholar 

  46. Morsdorf, F. et al. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 83–104 (Springer International, 2020).

  47. Gratani, L. & Bombelli, A. Correlation between leaf age and other leaf traits in three Mediterranean maquis shrub species: Quercus ilex, Phillyrea latifolia and Cistus incanus. Environ. Exp. Bot. 43, 141–153 (2000).

    Article  Google Scholar 

  48. Kitayama, K. & Aiba, S. I. Ecosystem structure and productivity of tropical rain forests along altitudinal gradients with contrasting soil phosphorus pools on Mount Kinabalu, Borneo. J. Ecol. 90, 37–51 (2002).

    Article  Google Scholar 

  49. Nagler, P. L., Glenn, E. P. & Hinojosa-Huerta, O. Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico. Remote Sens. Environ. 113, 1473–1485 (2009).

    Article  Google Scholar 

  50. Brassard, B. W., Chen, H. Y. H., Bergeron, Y. & Pare, D. Differences in fine root productivity between mixed- and single-species stands. Funct. Ecol. 25, 238–246 (2011).

    Article  Google Scholar 

  51. Reich, P. B., Walters, M. B. & Ellsworth, D. S. Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol. Monogr. 62, 365–392 (1992).

    Article  Google Scholar 

  52. Huston, M. A. & Wolverton, S. The global distribution of net primary production: resolving the paradox. Ecol. Monogr. 79, 343–377 (2009).

    Article  Google Scholar 

  53. Jones, M. O., Jones, L. A., Kimball, J. S. & McDonald, K. C. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011).

    Article  Google Scholar 

  54. Garonna, I., de Jong, R. & Schaepman, M. E. Variability and evolution of global land surface phenology over the past three decades (1982–2012). Glob. Change Biol. 22, 1456–1468 (2016).

    Article  Google Scholar 

  55. Niklas, K. J. et al. ‘Diminishing returns’ in the scaling of functional leaf traits across and within species groups. Proc. Natl Acad. Sci. USA 104, 8891–8896 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Walker, B., Kinzig, A. & Langridge, J. Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2, 95–113 (1999).

    Article  Google Scholar 

  57. Bai, Y. F. et al. Grazing alters ecosystem functioning and C:N:P stoichiometry of grasslands along a regional precipitation gradient. J. Appl. Ecol. 49, 1204–1215 (2012).

    Article  CAS  Google Scholar 

  58. Schmeller, D. S. et al. An operational definition of essential biodiversity variables. Biodivers. Conserv. 26, 2967–2972 (2017).

    Article  Google Scholar 

  59. Potter, C. et al. Recent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite record. Ecosystems 8, 808–824 (2005).

    Article  Google Scholar 

  60. Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690–3707 (2008).

    Article  Google Scholar 

  61. Russell-Smith, J., Ryan, P. G. & Durieu, R. A LANDSAT MSS-derived fire history of Kakadu National Park, monsoonal northern Australia, 1980–94: seasonal extent, frequency and patchiness. J. Appl. Ecol. 34, 748–766 (1997).

    Article  Google Scholar 

  62. Van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735 (2010).

    Article  CAS  Google Scholar 

  63. Nidumolu, U. B., De Bie, C., Van Keulen, H. & Skidmore, A. K. Enhancement of area-specific land-use objectives for land development. Land Degrad. Dev. 15, 513–525 (2004).

    Article  Google Scholar 

  64. Chen, F. et al. Fast automatic airport detection in remote sensing images using convolutional neural networks. Remote Sens. 10, 443 (2018).

    Article  Google Scholar 

  65. Weng, Q. H. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sens. Environ. 117, 34–49 (2012).

    Article  Google Scholar 

  66. Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A. & Davis, C. H. Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14, 549–553 (2017).

    Article  Google Scholar 

  67. Skidmore, A. K., Turner, B. J., Brinkhof, W. & Knowles, E. Performance of a neural network: mapping forests using GIS and remotely sensed data. Photogramm. Eng. Remote Sens. 63, 501–514 (1997).

    Google Scholar 

  68. Joshi, C. et al. Indirect remote sensing of a cryptic forest understorey invasive species. For. Ecol. Manag. 225, 245–256 (2006).

    Article  Google Scholar 

  69. Defries, R. S. et al. Mapping the land surface for global atmosphere–biosphere models—toward continuous distributions of vegetation’s functional properties. J. Geophys. Res. Atmos. 100, 20867–20882 (1995).

    Article  Google Scholar 

  70. Cunliffe, A. M., Brazier, R. E. & Anderson, K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 183, 129–143 (2016).

    Article  Google Scholar 

  71. Asner, G. P., Wessman, C. A. & Schimel, D. S. Heterogeneity of savanna canopy structure and function from imaging spectrometry and inverse modeling. Ecol. Appl. 8, 1022–1036 (1998).

    Article  Google Scholar 

  72. Peterseil, J. et al. Evaluating the ecological sustainability of Austrian agricultural landscapes—the SINUS approach. Land Use Policy 21, 307–320 (2004).

    Article  Google Scholar 

  73. Saura, S., Bodin, O. & Fortin, M. J. Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).

    Article  Google Scholar 

  74. De Jong, R., de Bruin, S., de Wit, A., Schaepman, M. E. & Dent, D. L. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens. Environ. 115, 692–702 (2011).

    Article  Google Scholar 

  75. Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).

    Article  PubMed  Google Scholar 

  76. Baldeck, C. A. & Asner, G. P. Improving remote species identification through efficient training data collection. Remote Sens. 6, 2682–2698 (2014).

    Article  Google Scholar 

  77. Fassnacht, F. E. et al. Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 186, 64–87 (2016).

    Article  Google Scholar 

  78. Lausch, A. et al. Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives. Ecol. Indic. 70, 317–339 (2016).

    Article  Google Scholar 

  79. Shi, Y. F., Wang, T. J., Skidmore, A. K. & Heurich, M. Important LiDAR metrics for discriminating forest tree species in Central Europe. ISPRS J. Photogramm. Remote Sens. 137, 163–174 (2018).

    Article  Google Scholar 

  80. Wilkes, P. et al. Using discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest types. Methods Ecol. Evol. 7, 700–712 (2016).

    Article  Google Scholar 

  81. Hyyppa, J. et al. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int. J. Remote Sens. 29, 1339–1366 (2008).

    Article  Google Scholar 

  82. Transon, J., d’Andrimont, R., Maugnard, A. & Defourny, P. Survey of hyperspectral earth observation applications from space in the Sentinel-2 context. Remote Sens. 10, 157 (2018).

    Article  Google Scholar 

  83. Guanter, L. et al. The EnMAP spaceborne imaging spectroscopy mission for Earth observation. Remote Sens. 7, 8830–8857 (2015).

    Article  Google Scholar 

  84. Qi, W. L. & Dubayah, R. O. Combining Tandem-X InSAR and simulated GEDI LiDAR observations for forest structure mapping. Remote Sens. Environ. 187, 253–266 (2016).

    Article  Google Scholar 

  85. Ramoelo, A., Cho, M., Mathieu, R. & Skidmore, A. K. Potential of Sentinel-2 spectral configuration to assess rangeland quality. J. Appl. Remote Sens. 9, 094096 (2015).

    Article  Google Scholar 

  86. Madonsela, S. et al. Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species. Int. J. Appl. Earth Obs. Geoinf. 58, 65–73 (2017).

    Google Scholar 

  87. Bush, A. et al. Connecting Earth observation to high-throughput biodiversity data. Nat. Ecol. Evol. 1, 0176 (2017).

    Article  Google Scholar 

  88. Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).

    Article  PubMed  Google Scholar 

  89. Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. New Phytol. 228, 485–493 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  90. McManus, K. M. et al. Phylogenetic structure of foliar spectral traits in tropical forest canopies. Remote Sens. 8, 196 (2016).

    Article  Google Scholar 

  91. Urbano, F. et al. Wildlife tracking data management: a new vision. Phil. Trans. R. Soc. B Biol. Sci. 365, 2177–2185 (2010).

    Article  Google Scholar 

  92. Cubaynes, H. C., Fretwell, P. T., Bamford, C., Gerrish, L. & Jackson, J. A. Whales from space: four mysticete species described using new VHR satellite imagery. Mar. Mammal. Sci. 35, 466–491 (2019).

    Article  Google Scholar 

  93. Yang, Z. et al. Spotting East African mammals in open savannah from space. PLoS ONE 9, e115989 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Neumann, W. et al. Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Mov. Ecol. 3, 8 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Weiss, J. R., Smythe, W. D. & Lu, W. W. Science Traceability. In Proc. IEEE Aerospace Conference 292–299 (IEEE, 2005).

  96. National Academies of Sciences, Engineering, and Medicine Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018).

  97. Verstraete, M. M., Diner, D. J. & Bezy, J. L. Planning for a spaceborne Earth observation mission: from user expectations to measurement requirements. Environ. Sci. Policy 54, 419–427 (2015).

    Article  Google Scholar 

  98. Skidmore, A. K. et al. Agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).

    Article  CAS  PubMed  Google Scholar 

  99. Masek, J. G. et al. North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 112, 2914–2926 (2008).

    Article  Google Scholar 

  100. O’Connor, B., Bojinski, S., Roosli, C. & Schaepman, M. E. Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. Ecol. Inform. 55, 101033 (2020).

  101. Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Vihervaara, P. et al. How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Glob. Ecol. Conserv. 10, 43–59 (2017).

    Article  Google Scholar 

  103. Walters, M. et al. Essential Biodiversity Variables UNEP/CBD/SBSTTA/17/INF/7 (Convention on Biological Diversity, 2013).

  104. Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389 (2017).

    Article  CAS  PubMed  Google Scholar 

  105. Coll, M. et al. Ecological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystems. Ecol. Indic. 60, 947–962 (2016).

    Article  Google Scholar 

  106. Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).

    Article  PubMed  Google Scholar 

  107. Gibert, J. P., Dell, A. I., DeLong, J. P. & Pawar, S. Scaling-up trait variation from individuals to ecosystems. Adv. Ecol. Res. 52, 1–17 (2015).

    Article  Google Scholar 

  108. Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. Adv. Ecol. Res. 46, 89–210 (2012).

    Article  Google Scholar 

  109. Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).

    Article  Google Scholar 

  110. Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).

    Article  CAS  PubMed  Google Scholar 

  111. Díaz, S. et al. Functional traits, the phylogeny of function, and ecosystem service vulnerability. Ecol. Evol. 3, 2958–2975 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).

    Article  CAS  PubMed  Google Scholar 

  113. Schmeller, D. et al. Building capacity in biodiversity monitoring at the global scale. Biodivers. Conserv. 26, 2765–2790 (2017).

    Article  Google Scholar 

  114. Belward, A. S. & Skoien, J. O. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J. Photogramm. Remote Sens. 103, 115–128 (2015).

    Article  Google Scholar 

  115. Vogel, D. Private global business regulation. Annu. Rev. Polit. Sci. 11, 261–282 (2008).

    Article  Google Scholar 

  116. Tranquilli, S. et al. Lack of conservation effort rapidly increases African great ape extinction risk. Conserv. Lett. 5, 48–55 (2012).

    Article  Google Scholar 

  117. Buchanan, G. M. et al. Free satellite data key to conservation. Science 361, 139–140 (2018).

    CAS  PubMed  Google Scholar 

  118. Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).

    Article  Google Scholar 

  119. Wulder, M. A. et al. Virtual constellations for global terrestrial monitoring. Remote Sens. Environ. 170, 62–76 (2015).

    Article  Google Scholar 

  120. Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).

    Article  Google Scholar 

  121. Czyz, E. A. et al. Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecol. Evol. 10, 7419–7430 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evol. 2, 976–982 (2018).

    Article  PubMed  Google Scholar 

  123. Cavender-Bares, J. et al. Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity. Remote Sens. 8, 221 (2016).

    Article  Google Scholar 

  124. Surface Biology and Geology (SBG) NASA Science https://science.nasa.gov/earth-science/decadal-sbg (2020).

Download references

Acknowledgements

This project has received support from the European Space Agency GlobDiversity project, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 834709), the NextGEOSS project (grant agreement 730329, H2020-EU.3.5.5) and e-Shape (grant agreement 820852, H2020-EU.3.5.5). The project workshops were supported by the GEO BON Secretariat at iDiv (DFG-FZT 118, project 202548816) (Leipzig, Germany), the European Space Agency (Frascati, Italy) and the University of Twente (Enschede, the Netherlands). W.D.K. acknowledges financial support from the Faculty of Science, Research Cluster Global Ecology, University of Amsterdam. F.E.M.-K. received support from NASA grants NNX14AP62A, 80NSSC20K0017 and NA19NOS0120199. The contribution of M.E.S. is supported by the UZH URPP GCB. P.V. acknowledges the IBC-Carbon Project funded by the Strategic Research Council (SRC) at the Academy of Finland (grant number 312559) and the Finnish Ecosystem Observatory.

Author information

Authors and Affiliations

  1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands

    Andrew K. Skidmore, Elnaz Neinavaz, Abebe Ali, Roshanak Darvishzadeh, Marcelle C. Lock & Tiejun Wang

  2. Department of Earth and Environmental Science, Macquarie University, Sydney, New South Wales, Australia

    Andrew K. Skidmore & Marcelle C. Lock

  3. Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada

    Nicholas C. Coops

  4. Department of Geography and Environmental Studies, Wollo University, Dessie, Ethiopia

    Abebe Ali

  5. Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland

    Michael E. Schaepman & Claudia Roeoesli

  6. European Space Research Institute (ESRIN), European Space Agency, Frascati, Italy

    Marc Paganini

  7. Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, the Netherlands

    W. Daniel Kissling

  8. Biodiversity Centre, Finnish Environment Institute (SYKE), Helsinki, Finland

    Petteri Vihervaara

  9. Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany

    Hannes Feilhauer

  10. Remote Sensing Center for Earth System Research, University of Leipzig, Leipzig, Germany

    Hannes Feilhauer

  11. NatureServe, Arlington, VA, USA

    Miguel Fernandez

  12. George Mason University, Fairfax, VA, USA

    Miguel Fernandez

  13. German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany

    Néstor Fernández

  14. Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Néstor Fernández

  15. Google, Zurich, Switzerland

    Noel Gorelick

  16. Tour du Valat, Arles, France

    Ilse Geijzendorffer

  17. Earth Observation Center (EOC), Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany

    Uta Heiden & Stefanie Holzwarth

  18. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park Administration, Grafenau, Germany

    Marco Heurich

  19. Albert Ludwigs University of Freiburg, Freiburg, Germany

    Marco Heurich

  20. GBIF Secretariat, Copenhagen, Denmark

    Donald Hobern

  21. College of Marine Science, University of South Florida, St Petersburg, FL, USA

    Frank E. Muller-Karger

  22. Flemish Institute for Technological Research (VITO), Mol, Belgium

    Ruben Van De Kerchove

  23. Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany

    Angela Lausch

  24. Geography Department, Humboldt University of Berlin, Berlin, Germany

    Angela Lausch

  25. Technische Universität Braunschweig, Braunschweig, Germany

    Pedro J. Leitão

  26. Humboldt-Universität zu Berlin, Berlin, Germany

    Pedro J. Leitão

  27. Wageningen Environmental Research, Wageningen University & Research, Wageningen, the Netherlands

    Caspar A. Mücher

  28. UN Environment World Conservation Monitoring Centre, Cambridge, UK

    Brian O’Connor

  29. Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy

    Duccio Rocchini

  30. Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic

    Duccio Rocchini

  31. Earth Science Division, NASA, Washington DC, USA

    Woody Turner

  32. Unilever Europe B.V., Rotterdam, the Netherlands

    Jan Kees Vis

  33. Institute of Geography and Geology, University of Wuerzburg, Würzburg, Germany

    Martin Wegmann

  34. Land Systems and Sustainable Land Management, Geographisches Institut, Universität Bern, Bern, Switzerland

    Vladimir Wingate

Authors
  1. Andrew K. Skidmore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicholas C. Coops
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elnaz Neinavaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abebe Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael E. Schaepman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marc Paganini
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. W. Daniel Kissling
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Petteri Vihervaara
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Roshanak Darvishzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hannes Feilhauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Miguel Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Néstor Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Noel Gorelick
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ilse Geijzendorffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Uta Heiden
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Marco Heurich
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Donald Hobern
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Stefanie Holzwarth
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Frank E. Muller-Karger
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Ruben Van De Kerchove
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Angela Lausch
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Pedro J. Leitão
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Marcelle C. Lock
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Caspar A. Mücher
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Brian O’Connor
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Duccio Rocchini
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Claudia Roeoesli
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Woody Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Jan Kees Vis
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Tiejun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Martin Wegmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Vladimir Wingate
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.K.S. contributed to conceptualization, supervision, validation, visualization and analysis, as well as writing of the original draft preparation, review and editing. E.N. and A.A. contributed to conceptualization, investigation, analysis, writing, reviewing and editing. N.C.C., M.E.S., W.D.K. and R.D. contributed to conceptualization, visualization and analysis, as well as writing, reviewing and editing. M.P., P.V., H.F., M.F., N.F., N.G., I.G., U.H., M.H., D.H., S.H., F.E.M.-K., R.V.D.K., A.L., P.J.L., M.C.L., C.A.M., B.O., D.R., C.R., W.T., J.K.V., T.W., M.W. and V.W. contributed to conceptualization, analysis and reviewing the draft.

Corresponding author

Correspondence to Andrew K. Skidmore.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Jeannine Cavender-Bares and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Skidmore, A.K., Coops, N.C., Neinavaz, E. et al. Priority list of biodiversity metrics to observe from space. Nat Ecol Evol 5, 896–906 (2021). https://doi.org/10.1038/s41559-021-01451-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01451-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing