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

Title data

Rocchini, Duccio ; Salvatori, Nicole ; Beierkuhnlein, Carl ; Chiarucci, Alessandro ; de Boissieu, Florian ; Förster, Michael ; Garzon-Lopez, Carol X. ; Gillespie, Thomas W. ; Hauffe, Heidi C. ; He, Kate S. ; Kleinschmit, Birgit ; Lenoir, Jonathan ; Malavasi, Marco ; Moudrý, Vítĕzslav ; Nagendra, Harini ; Payne, Davnah ; Šímová, Petra ; Torresani, Michele ; Wegmann, Martin ; Féret, Jean-Baptiste:
From local spectral species to global spectral communities : A benchmark for ecosystem diversity estimate by remote sensing.
In: Ecological Informatics. Vol. 61 (March 2021) . - No. 101195.
ISSN 1574-9541
DOI: https://doi.org/10.1016/j.ecoinf.2020.101195

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Biodiversity; Ecological informatics; Modelling; Remote sensing; Satellite imagery
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Biogeography
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Biogeography > Chair Biogeography - Univ.-Prof. Dr. Carl Beierkuhnlein
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 550 Earth sciences, geology
500 Science > 580 Plants (Botany)
500 Science > 590 Animals (Zoology)
Date Deposited: 08 Mar 2021 10:09
Last Modified: 08 Mar 2021 10:09
URI: https://eref.uni-bayreuth.de/id/eprint/63712