Published December 13, 2020 | Version v1
Journal article Open

rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Authors/Creators

  • 1. BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy/ Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha - Suchdol, Czech Republic;
  • 2. BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy
  • 3. Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
  • 4. Inria Bordeaux - Sud-Ouest, Talence, France;
  • 5. Georges Lemaître Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
  • 6. Faculty of Science and Technology, Free University of Bolzano/Bozen, Piazza Universitá/Universitätsplatz 1, Bolzano, Italy
  • 7. Department of Life Sciences, University of Trieste, Trieste, Italy
  • 8. EcoBio (Ecosystèmes, Biodiversité, Évolution) - UMR 6553, Université de Rennes, CNRS, Rennes, France
  • 9. Department of Mathematics, University of Trento, Povo, Italy;
  • 10. School of Geography, University of Nottingham, Nottingham, UK;
  • 11. Department of Mathematics, University of Zurich, Zurich, Switzerland / Department of Computational Science, University of Zurich, Zurich, Switzerland;
  • 12. Plant Ecology and Nature Conservation Group, Wageningen University, Wageningen, The Netherlands
  • 13. Unit of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Italy; / Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
  • 14. UR "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN, UMR 7058 CNRS-UPJV), Université de Picardie Jules Verne, Amiens, France
  • 15. Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha - Suchdol, Czech Republic;
  • 16. 7DAGRI Department of Agriculture, Food, Environment and Forestry, University of Florence, Firenze, Italy
  • 17. Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
  • 18. Department of Environmental Biology, University of Rome "La Sapienza'", Rome, Italy
  • 19. Remote Sensing of Environmental Dynamics Laboratory, DISAT, Universitá degli Studi MilanoBicocca, Milano, Italy
  • 20. Department of Geography, Earth System Science, University of Zurich, Zurich, Switzerland
  • 21. Department of Geography, Remote Sensing Laboratories, University of Zurich, Zurich, Switzerland
  • 22. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA;
  • 23. Department of Mathematics, University of Zurich, Zurich, Switzerland
  • 24. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands; / Department of Environmental Science, Macquarie University, Sydney, NSW, Australia
  • 25. Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Firenze, Italy
  • 26. CNR-IIA C/O Physics Department "M. Merlin" University of Bari, Bari, Italy
  • 27. Department of Remote Sensing, University of Wuerzburg, Würzburg, Germany

Description

Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package ‐ rasterdiv ‐ to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

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