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Title: Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Abstract

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients andmore » management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m2) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Michigan State Univ., East Lansing, MI (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
OSTI Identifier:
1656438
Report Number(s):
BNL-216302-2020-JAAM
Journal ID: ISSN 1051-0761
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Ecological Applications
Additional Journal Information:
Journal Volume: 31; Journal Issue: 2; Journal ID: ISSN 1051-0761
Publisher:
Ecological Society of America
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Hyperspectral imagery; airborne lidar; functional traits; forest canopies; forest structure; landscape ecology

Citation Formats

Kamoske, Aaron G., Dahlin, Kyla M., Serbin, Shawn P., and Stark, Scott C.. Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach. United States: N. p., 2020. Web. https://doi.org/10.1002/eap.2230.
Kamoske, Aaron G., Dahlin, Kyla M., Serbin, Shawn P., & Stark, Scott C.. Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach. United States. https://doi.org/10.1002/eap.2230
Kamoske, Aaron G., Dahlin, Kyla M., Serbin, Shawn P., and Stark, Scott C.. Mon . "Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach". United States. https://doi.org/10.1002/eap.2230. https://www.osti.gov/servlets/purl/1656438.
@article{osti_1656438,
title = {Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach},
author = {Kamoske, Aaron G. and Dahlin, Kyla M. and Serbin, Shawn P. and Stark, Scott C.},
abstractNote = {Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m2) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.},
doi = {10.1002/eap.2230},
journal = {Ecological Applications},
number = 2,
volume = 31,
place = {United States},
year = {2020},
month = {10}
}

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