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Published February 14, 2023 | Version v1
Journal article Open

Integrals of life: tracking ecosystem spatial heterogeneity from space through the area under the curve of the parametric Rao's Q index

  • 1. BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
  • 2. Evolutionary Ecology and Genetics Group, Earth & Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium
  • 3. UMR CNRS 7058, Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN), Université de Picardie Jules Verne, 1 rue des Louvels, F–80037 Amiens Cedex 1, France
  • 4. Department of Life Sciences, University of Trieste, via Giorgieri 10, 34127 Trieste, Italy
  • 5. Department of Life, Health and Environmental Sciences, University of L'Aquila, Piazzale Salvatore Tommasi 1, 67100, L'Aquila, Italy | BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
  • 6. Department of Mathematics and Institute of Computational Science, University of Zürich, Zürich, Switzerland
  • 7. Department of Chemistry, Physics, Mathematics and Natural Sciences, University of Sassari, Sassari, Italy
  • 8. Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha - Suchdol, Czech Republic
  • 9. Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Krygowskiego 10, 61-680 Poznan, Poland
  • 10. University of Camerino, Camerino, Italy
  • 11. DAFNE Department, Tuscia University, 01100 Viterbo, Italy
  • 12. Department of Environmental Biology, Sapienza University, Piazzale Moro, 5, 00185, Rome, Italy
  • 13. Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
  • 14. Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, J. Liivi 2, 50409 Tartu, Estonia
  • 15. Libera Università di Bolzano - Freie Universität Bozen, Bolzano/Bozen, Italy
  • 16. Department of Agricultural and Environmental Sciences (DiSAA), Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy
  • 17. BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy | LifeWatch Italy, Lecce | Plant Data – Interuniversity Research Centre for Plant Biodiversity and Big Data, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
  • 18. BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy | Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha - Suchdol, Czech Republic

Description

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors : they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of the rasterdiv R package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time.

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