Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties
Abstract
A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (Marea), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both Marea and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulosemore »
- Authors:
-
- Univ. of Wisconsin, Madison, WI (United States)
- West Virginia Univ., Morgantown, WV (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1228837
- Report Number(s):
- BNL-108475-2015-JA
Journal ID: ISSN 1051-0761; R&D Project: 21087/21088; YN0100000
- Grant/Contract Number:
- SC00112704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Ecological Applications
- Additional Journal Information:
- Journal Volume: 25; Journal Issue: 8; Journal ID: ISSN 1051-0761
- Publisher:
- Ecological Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; AVIRIS; airborne visible/infrared imaging spectrometer; carbon cycling; deciduous and coniferous forests; foliar traits; imaging spectroscopy; leaf chemistry; leaf morphology; nutrient cycling; PLS regression; spatial mapping; uncertainty
Citation Formats
Singh, Aditya, Serbin, Shawn P., McNeil, Brenden E., Kingdon, Clayton C., and Townsend, Philip A. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. United States: N. p., 2015.
Web. doi:10.1890/14-2098.1.
Singh, Aditya, Serbin, Shawn P., McNeil, Brenden E., Kingdon, Clayton C., & Townsend, Philip A. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. United States. https://doi.org/10.1890/14-2098.1
Singh, Aditya, Serbin, Shawn P., McNeil, Brenden E., Kingdon, Clayton C., and Townsend, Philip A. Tue .
"Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties". United States. https://doi.org/10.1890/14-2098.1. https://www.osti.gov/servlets/purl/1228837.
@article{osti_1228837,
title = {Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties},
author = {Singh, Aditya and Serbin, Shawn P. and McNeil, Brenden E. and Kingdon, Clayton C. and Townsend, Philip A.},
abstractNote = {A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (Marea), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both Marea and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than Marea and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.},
doi = {10.1890/14-2098.1},
journal = {Ecological Applications},
number = 8,
volume = 25,
place = {United States},
year = {Tue Dec 01 00:00:00 EST 2015},
month = {Tue Dec 01 00:00:00 EST 2015}
}
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