Influence of species richness, evenness, and composition on optical diversity: A simulation study

Ran Wang, John A. Gamon, Anna K. Schweiger, Jeannine Cavender-Bares, Philip A. Townsend, Arthur I. Zygielbaum, Shan Kothari

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

While remote sensing has increasingly been applied to estimate α biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon's index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

Original languageEnglish (US)
Pages (from-to)218-228
Number of pages11
JournalRemote Sensing of Environment
Volume211
DOIs
StatePublished - Jun 15 2018

Fingerprint

Biodiversity
species richness
biodiversity
species diversity
discriminant analysis
Chemical analysis
Discriminant analysis
simulation
species evenness
reflectance
least squares
spectral reflectance
prairie
spectrometers
prairies
Ecosystems
spectrometer
remote sensing
Spectrometers
leaves

Keywords

  • Biodiversity
  • Cedar Creek
  • Imaging spectroscopy
  • Optical diversity
  • Remote sensing

Cite this

Influence of species richness, evenness, and composition on optical diversity : A simulation study. / Wang, Ran; Gamon, John A.; Schweiger, Anna K.; Cavender-Bares, Jeannine; Townsend, Philip A.; Zygielbaum, Arthur I.; Kothari, Shan.

In: Remote Sensing of Environment, Vol. 211, 15.06.2018, p. 218-228.

Research output: Contribution to journalArticle

Wang, Ran ; Gamon, John A. ; Schweiger, Anna K. ; Cavender-Bares, Jeannine ; Townsend, Philip A. ; Zygielbaum, Arthur I. ; Kothari, Shan. / Influence of species richness, evenness, and composition on optical diversity : A simulation study. In: Remote Sensing of Environment. 2018 ; Vol. 211. pp. 218-228.
@article{a539a67a4466457db3294150db2cc9b1,
title = "Influence of species richness, evenness, and composition on optical diversity: A simulation study",
abstract = "While remote sensing has increasingly been applied to estimate α biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon's index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.",
keywords = "Biodiversity, Cedar Creek, Imaging spectroscopy, Optical diversity, Remote sensing",
author = "Ran Wang and Gamon, {John A.} and Schweiger, {Anna K.} and Jeannine Cavender-Bares and Townsend, {Philip A.} and Zygielbaum, {Arthur I.} and Shan Kothari",
year = "2018",
month = "6",
day = "15",
doi = "10.1016/j.rse.2018.04.010",
language = "English (US)",
volume = "211",
pages = "218--228",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier",

}

TY - JOUR

T1 - Influence of species richness, evenness, and composition on optical diversity

T2 - A simulation study

AU - Wang, Ran

AU - Gamon, John A.

AU - Schweiger, Anna K.

AU - Cavender-Bares, Jeannine

AU - Townsend, Philip A.

AU - Zygielbaum, Arthur I.

AU - Kothari, Shan

PY - 2018/6/15

Y1 - 2018/6/15

N2 - While remote sensing has increasingly been applied to estimate α biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon's index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

AB - While remote sensing has increasingly been applied to estimate α biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon's index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

KW - Biodiversity

KW - Cedar Creek

KW - Imaging spectroscopy

KW - Optical diversity

KW - Remote sensing

UR - http://www.scopus.com/inward/record.url?scp=85045450572&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045450572&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2018.04.010

DO - 10.1016/j.rse.2018.04.010

M3 - Article

AN - SCOPUS:85045450572

VL - 211

SP - 218

EP - 228

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -