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Disentangling the role of remotely sensed spectral heterogeneity as a proxy for North American plant species richness

Community Ecology volume 15, pages 37–43 (2014)Cite this article

Abstract

Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.

Abbreviations

MODIS:

Moderate Resolution Imaging Spectrometer

NDVI:

Normalized Difference Vegetation Index

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Author information

Affiliations

  1. Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach, 1 38010, S. Michele all’Adige (TN), Italy

    D. Rocchini, L. Delucchi & M. Neteler

  2. Department of Botany, Oklahoma State University, Stillwater, OK, 74078, USA

    L. Dadalt & M. W. Palmer

Authors
  1. D. Rocchini
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  2. L. Dadalt
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  3. L. Delucchi
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  4. M. Neteler
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  5. M. W. Palmer
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Correspondence to D. Rocchini.

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Rocchini, D., Dadalt, L., Delucchi, L. et al. Disentangling the role of remotely sensed spectral heterogeneity as a proxy for North American plant species richness. COMMUNITY ECOLOGY 15, 37–43 (2014). https://doi.org/10.1556/ComEc.15.2014.1.4

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Keywords

  • Area effects
  • Species diversity
  • Spectral variation hypothesis
  • Variance partitioning.