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Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest


Zheng, Zhaoju; Zeng, Yuan; Schneider, Fabian D; Zhao, Yujin; Zhao, Dan; Schmid, Bernhard; Schaepman, Michael E; Morsdorf, Felix (2021). Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest. Remote Sensing of Environment, 252:112170.

Abstract

Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- and intra-specific variation of functional traits. However, quantifying plant traits and FD consistently and cost-effectively across large and heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection and ranging (LiDAR) and imaging spectroscopy provide spatially explicit data, which allow mapping of selected forest traits and FD at different spatial scales. We develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests, and demonstrate the approach by mapping functional traits of over one million trees in a subtropical forest in China. We retrieved canopy morphological traits (95th quantile height, leaf area index and foliage height diversity) and physiological traits (proxies of nitrogen, carotenoids and specific leaf area) for each individual canopy tree crown from LiDAR and imaging spectroscopy data, respectively. Based on the multivariate trait space spanned by the six trait axes and filled by measured tree individuals, we mapped forest FD as richness, divergence and evenness, and explored spatial patterns of FD as well as FD–area and FD–tree number relationships. The results show that LiDAR-derived morphological traits and spectral indices of physiological traits are consistent with field measurements and show weak correlations between each other at individual tree level. Morphological functional richness follows a hump-shaped pattern along the elevational gradient of 984–1805 m, with maximum values at elevations around 1450 m, while high physiological functional richness occurs at medium and high elevations. At an ecosystem scale of 30 × 30 m, morphological richness increases continuously with tree density, but physiological richness decreases again at very high densities. Moreover, functional richness shows a logarithmic relationship with increasing area or number of individual trees, and local trait convergence is predominant in our study area. We demonstrate the ability to quantify FD using morphological and physiological traits by remote sensing, which provides a pathway to conduct individual-level trait-based ecology with wall-to-wall data.

Abstract

Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- and intra-specific variation of functional traits. However, quantifying plant traits and FD consistently and cost-effectively across large and heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection and ranging (LiDAR) and imaging spectroscopy provide spatially explicit data, which allow mapping of selected forest traits and FD at different spatial scales. We develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests, and demonstrate the approach by mapping functional traits of over one million trees in a subtropical forest in China. We retrieved canopy morphological traits (95th quantile height, leaf area index and foliage height diversity) and physiological traits (proxies of nitrogen, carotenoids and specific leaf area) for each individual canopy tree crown from LiDAR and imaging spectroscopy data, respectively. Based on the multivariate trait space spanned by the six trait axes and filled by measured tree individuals, we mapped forest FD as richness, divergence and evenness, and explored spatial patterns of FD as well as FD–area and FD–tree number relationships. The results show that LiDAR-derived morphological traits and spectral indices of physiological traits are consistent with field measurements and show weak correlations between each other at individual tree level. Morphological functional richness follows a hump-shaped pattern along the elevational gradient of 984–1805 m, with maximum values at elevations around 1450 m, while high physiological functional richness occurs at medium and high elevations. At an ecosystem scale of 30 × 30 m, morphological richness increases continuously with tree density, but physiological richness decreases again at very high densities. Moreover, functional richness shows a logarithmic relationship with increasing area or number of individual trees, and local trait convergence is predominant in our study area. We demonstrate the ability to quantify FD using morphological and physiological traits by remote sensing, which provides a pathway to conduct individual-level trait-based ecology with wall-to-wall data.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
08 Research Priority Programs > Global Change and Biodiversity
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Life Sciences > Soil Science
Physical Sciences > Geology
Physical Sciences > Computers in Earth Sciences
Uncontrolled Keywords:Computers in Earth Sciences, Soil Science, Geology
Language:English
Date:1 January 2021
Deposited On:08 Jan 2021 14:06
Last Modified:24 Jun 2024 01:42
Publisher:Elsevier
ISSN:0034-4257
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.rse.2020.112170
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)