Multi-temporal assessment of grassland α- and β-diversity using hyperspectral imaging

Hamed Gholizadeh, John A. Gamon, Christopher J. Helzer, Jeannine Cavender-Bares

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

While more and more studies are exploring the application of remote sensing in assessing biodiversity for different ecosystems, most consider biodiversity at one point in time. Using several remote-sensing-based metrics, we asked how well remote sensing can detect biodiversity (both α- and β-diversity) in a prairie grassland across time using airborne hyperspectral data collected in two successive years (2017 and 2018) and at different periods in the growing season (2018). The ability to detect biodiversity using “spectral diversity” and “spectral species” types indeed varied significantly over a 2-yr timespan. Toward the end of the growing season in 2018, the relationship between field- and remote-sensing-based α- and β-diversity weakened compared to data collected from the same season in the previous year. This contrasting pattern between the two years was likely influenced by prescribed fire, altered weather, and the resulting shifting species composition and phenology. These findings indicate that direct detection of α- and β-diversity in grasslands should be multi-temporal when possible and should consider the effect of disturbances, climate variables, and phenology. We demonstrate an essential role for airborne platforms in developing a global biodiversity monitoring system involving forthcoming space-borne hyperspectral sensors.

Original languageEnglish (US)
Article numbere02145
JournalEcological Applications
Volume30
Issue number7
DOIs
StatePublished - Oct 1 2020

Bibliographical note

Funding Information:
We thank three anonymous reviewers for providing feedback and insightful comments on this manuscript. We thank Rick Perk (UNL) for airborne data collection and Bryan Leavitt (UNL), Rong Yu (UNL), and Nelson Winkel (TNC) for their invaluable help during fieldwork. We also thank Kim Helzer for collecting plant diversity inventory. This work was made possible by the support from NSF/NASA Dimensions of Biodiversity Program grant DEB-1342823 to J. A. Gamon and DEB-1342872 to J. Cavender-Bares. Mention of trade names does not imply endorsement by the authors.

Funding Information:
We thank three anonymous reviewers for providing feedback and insightful comments on this manuscript. We thank Rick Perk (UNL) for airborne data collection and Bryan Leavitt (UNL), Rong Yu (UNL), and Nelson Winkel (TNC) for their invaluable help during fieldwork. We also thank Kim Helzer for collecting plant diversity inventory. This work was made possible by the support from NSF/NASA Dimensions of Biodiversity Program grant DEB‐1342823 to J. A. Gamon and DEB‐1342872 to J. Cavender‐Bares. Mention of trade names does not imply endorsement by the authors.

Publisher Copyright:
© 2020 by the Ecological Society of America

Keywords

  • airborne remote sensing
  • biodiversity
  • grasslands
  • hyperspectral imaging
  • multi-temporal
  • spectral diversity
  • spectral species
  • α-diversity
  • β-diversity

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.

Fingerprint

Dive into the research topics of 'Multi-temporal assessment of grassland α- and β-diversity using hyperspectral imaging'. Together they form a unique fingerprint.

Cite this