Published November 2, 2020 | Version v1
Journal article Open

From local spectral species to global spectral communities: a benchmark for ecosystem diversity estimate by remote sensing

Authors/Creators

  • 1. Alma Mater Studiorum University of Bologna, Department of Biological, Geological and Environmental Sciences, via Irnerio 42, 40126 Bologna, Italy / Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Applied Geoinformatics and Spatial Planning, Kamýcka 129, Praha, Suchdol 16500, Czech Republic
  • 2. University of Udine, Department of Agri-Food, Animal and Environmental Sciences (DI4A), via delle scienze 206, 33100 Udine (UD), Italy / University of Trieste, Department of Life Sciences, via Giorgieri 5, 34100 Trieste (TS), Italy
  • 3. Biogeography, BayCEER, University of Bayreuth, UniversitaetsstraA˜Ye ¨ 30, 95440 Bayreuth, Germany
  • 4. Alma Mater Studiorum University of Bologna, Department of Biological, Geological and Environmental Sciences, via Irnerio 42, 40126 Bologna, Italy
  • 5. UMR-TETIS, IRSTEA Montpellier, Maison de la T´el´ed´etection, 500 rue JF Breton, 34093 Montpellier Cedex 5, France
  • 6. Department of Geoinformation for Environmental Planning, Technical University of Berlin, StraA˜Ye ¨ des 17. Juni 145, 10623 Berlin, Germany
  • 7. Ecology and Vegetation Physiology Group (EcoFiv), Universidad de los Andes, Cr. 1E No 18A, Bogotà, Colombia
  • 8. Department of Geography, University of California Los Angeles, Los Angeles, California 90095-1524, USA
  • 9. Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, Via E. Mach 1, 38010 S Michele all'Adige (TN), Italy
  • 10. Department of Biological Sciences, Murray State University, Murray, KY 42071, USA
  • 11. UR "Ecologie et Dynamique des Syst`emes Anthropis´es" (EDYSAN, UMR 7058 CNRS-UPJV), Universit´e de Picardie Jules Verne, 1 Rue des Louvels, 80037 Amiens Cedex 1, France
  • 12. Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Applied Geoinformatics and Spatial Planning, Kamýcka 129, Praha, Suchdol 16500, Czech Republic
  • 13. Azim Premji University, PES Institute of Technology Campus, Pixel Park, B Block, Electronics City, Hosur Road, Bangalore 560100, India
  • 14. University of Bern, Institute of Plant Sciences, GMBA office Altenbergrain 21, 3013 Bern, Switzerland
  • 15. Free University of Bolzano/Bozen, Faculty of Science and Technology, Piazza Universita/Universit ´ atsplatz ¨ 1, 39100 Bolzano, Bozen, Italy / Atmospheric Physics Group, Department of Civil Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123, Trento, Italy
  • 16. Department of Remote Sensing, Remote Sensing and Biodiversity Research Group, University of Wuerzburg, Wuerzburg, Germany

Description

In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches. Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called “spectral species”. Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a single plant species in the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to derive α- (local relative abundance and richness of spectral species) and β-diversity (turnover of spectral species) maps over wide geographical areas.

Files

ECOINF-showcase.pdf

Files (5.6 MB)

Name Size Download all
md5:d46bf93eb900fc64b07c80b50dbb5765
5.6 MB Preview Download