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Remote sensing of spectral diversity: A new methodological approach to account for spatio-temporal dissimilarities between plant communities


Rossi, Christian; Kneubühler, Mathias; Schütz, Martin; Schaepman, Michael E; Haller, Rudolf M; Risch, Anita C (2021). Remote sensing of spectral diversity: A new methodological approach to account for spatio-temporal dissimilarities between plant communities. Ecological Indicators, 130:108106.

Abstract

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In
particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be
assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented
by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at
multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first
introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao’s
quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral
diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.

Abstract

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In
particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be
assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented
by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at
multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first
introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao’s
quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral
diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Social Sciences & Humanities > General Decision Sciences
Life Sciences > Ecology, Evolution, Behavior and Systematics
Physical Sciences > Ecology
Uncontrolled Keywords:Ecology, Ecology, Evolution, Behavior and Systematics, General Decision Sciences
Language:English
Date:1 November 2021
Deposited On:02 Sep 2021 12:54
Last Modified:03 Sep 2021 20:00
Publisher:Elsevier
ISSN:1470-160X
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.ecolind.2021.108106

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