Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Hamed Gholizadeh, Adam P. Dixon, Kimberly H. Pan, Nicholas A. McMillan, Robert G. Hamilton, Samuel D. Fuhlendorf, Jeannine Cavender-Bares, John A. Gamon

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust in-situ plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis—i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration's Surface Biology and Geology mission.

Original languageEnglish (US)
Article number113254
JournalRemote Sensing of Environment
Volume281
DOIs
StatePublished - Nov 2022

Bibliographical note

Funding Information:
We sincerely thank three anonymous reviewers who helped us improve the quality of our manuscript. We express our sincere gratitude to The Nature Conservancy for facilitating this study. We also thank Dr. Uta Heiden (German Aerospace Center–DLR), Heath Lester (Teledyne Brown Engineering), Aaron Kaulfus, Michael Palmer, and Cooper Sherrill. DESIS data were provided through NASA's Commercial Smallsat Data Acquisition (CSDA) Program. Hamed Gholizadeh was supported by a NASA NIP award [80NSSC21K0941]. Jeannine Cavender-Bares was supported by an NSF DBI award [2021898]. Mention of trade names does not imply endorsement by the authors.

Funding Information:
We sincerely thank three anonymous reviewers who helped us improve the quality of our manuscript. We express our sincere gratitude to The Nature Conservancy for facilitating this study. We also thank Dr. Uta Heiden (German Aerospace Center–DLR), Heath Lester (Teledyne Brown Engineering), Aaron Kaulfus, Michael Palmer, and Cooper Sherrill. DESIS data were provided through NASA's Commercial Smallsat Data Acquisition (CSDA) Program. Hamed Gholizadeh was supported by a NASA NIP award [ 80NSSC21K0941 ]. Jeannine Cavender-Bares was supported by an NSF DBI award [ 2021898 ]. Mention of trade names does not imply endorsement by the authors.

Publisher Copyright:
© 2022 The Authors

Keywords

  • Airborne remote sensing
  • Biodiversity
  • DESIS
  • Fire
  • Grasslands
  • Imaging spectroscopy
  • Management practices
  • Spectral diversity

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