Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems

Hamed Gholizadeh, John A. Gamon, Arthur I. Zygielbaum, Ran Wang, Anna K. Schweiger, Jeannine Cavender-Bares

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Hyperspectral data, with their detailed spectral information at different wavelengths, offer multiple ways to assess biodiversity. One approach, known as the “spectral variation hypothesis” (SVH), proposes that biodiversity is linked to spectral diversity. However, SVH-based approaches, which we refer to as “spectral diversity metrics” can be confounded by soil exposure and are sensitive to the spatial resolution of the data. To address these issues, we 1) investigated the impact of soil exposure on spectral diversity, 2) identified optimal bands for mapping biodiversity using a spectral diversity metric based on dimension reduction, and 3) assessed the impact of spatial resolution on spectral diversity metrics. In this study, α-diversity (species richness) was used as a measure of plant biodiversity. The study was based on two imaging spectrometry data sets from the Cedar Creek Ecosystem Science Reserve in Central Minnesota, USA, at two levels: proximal and airborne. The data sets included varying degrees of soil background sampled at two different spatial resolutions (1 mm and 0.75 m). We explored five spectral diversity metrics, including the coefficient of variation, convex hull volume, spectral angle mapper, spectral information divergence, and a newly proposed dimension reduction-based metric called “convex hull area.” For the proximal data set (pixel size of 1 mm), filtering soil pixels by applying a normalized difference vegetation index (NDVI) threshold improved the performance of all spectral diversity metrics significantly, with the coefficient of variation showing the highest correlation with species richness. In the airborne data set (pixel size of 0.75 m), the convex hull area outperformed other metrics. These findings demonstrate promising approaches for remote sensing of biodiversity, illustrate a confounding effect of soil background on remote diversity measurement, and indicate that the most informative regions of the electromagnetic spectrum for estimating species richness can vary with spatial scale.

Original languageEnglish (US)
Pages (from-to)240-253
Number of pages14
JournalRemote Sensing of Environment
Volume206
DOIs
StatePublished - Mar 1 2018

Bibliographical note

Funding Information:
We thank Dr. Gabriel Hmimina (School of Natural Resources at UNL) for his efforts to launch the computing cluster that was used to process the large data set of this study. We also acknowledge the Cedar Creek Ecosystem Science Reserve staff and all the people who provided assistance during field data collection. The authors wish to express their sincere gratitude to Rick Perk (Center for Advanced Land Management Information Technologies at UNL) for collecting the airborne data. This work was supported by NASA/NSF Dimensions of Biodiversity Program grant DEB-1342823 to A.I.Z. and J.A.G, and DEB-1342872 to J.C.B., NSERC grant RGPIN-2015-05129 to J.A.G., CFI grant 26793 to J.A.G., iCORE/ AITF grants G224150012 and 200700172 to J.A.G., and the Cedar Creek NSF Long-Term Ecological Research program ( DEB-1234162 ). Mention of trade names does not indicate endorsement by the authors.

Funding Information:
We thank Dr. Gabriel Hmimina (School of Natural Resources at UNL) for his efforts to launch the computing cluster that was used to process the large data set of this study. We also acknowledge the Cedar Creek Ecosystem Science Reserve staff and all the people who provided assistance during field data collection. The authors wish to express their sincere gratitude to Rick Perk (Center for Advanced Land Management Information Technologies at UNL) for collecting the airborne data. This work was supported by NASA/NSF Dimensions of Biodiversity Program grant DEB-1342823 to A.I.Z. and J.A.G, and DEB-1342872 to J.C.B., NSERC grant RGPIN-2015-05129 to J.A.G., CFI grant 26793 to J.A.G., iCORE/AITF grants G224150012 and 200700172 to J.A.G., and the Cedar Creek NSF Long-Term Ecological Research program (DEB-1234162). Mention of trade names does not indicate endorsement by the authors.

Publisher Copyright:
© 2017 Elsevier Inc.

Keywords

  • Dimension reduction
  • Hyperspectral imaging
  • Prairie
  • Remote sensing
  • Soil exposure
  • Spectral diversity
  • α-Diversity

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