Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E.; Kingdon, Clayton C.; Townsend, Philip A.

doi:10.1890/14-2098.1

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

Journal Article · Mon Nov 30 23:00:00 EST 2015 · Ecological Applications

DOI:https://doi.org/10.1890/14-2098.1· OSTI ID:1228837

Singh, Aditya ^[1]; Serbin, Shawn P. ^[1]; McNeil, Brenden E. ^[2]; Kingdon, Clayton C. ^[1]; Townsend, Philip A. ^[1]

Univ. of Wisconsin, Madison, WI (United States)
West Virginia Univ., Morgantown, WV (United States)

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 (M_area), 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.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

OSTI ID:: 1228837

Report Number(s):: BNL--108475-2015-JA; YN0100000

Journal Information:: Ecological Applications, Journal Name: Ecological Applications Journal Issue: 8 Vol. 25; ISSN 1051-0761

Publisher:: Ecological Society of AmericaCopyright Statement

Country of Publication:: United States

Language:: English

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54 ENVIRONMENTAL SCIENCES
AVIRIS
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Supplement 1. Locations of field plots, plot-scale foliar chemical and morphological traits, results of leave-site-out and leave-year-out model cross-validations, and PLSR model coefficients. Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. https://doi.org/10.6084/M9.FIGSHARE.3521432 The current record is supplemented by this dataset	dataset	January 2016
Supplement 1. Locations of field plots, plot-scale foliar chemical and morphological traits, results of leave-site-out and leave-year-out model cross-validations, and PLSR model coefficients. Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. https://doi.org/10.6084/m9.figshare.3521432.v1 The current record is supplemented by this dataset	dataset	January 2016
Appendix A. Comparisons of model accuracies with literature, species-wise estimates of canopy foliar traits,comparisons of models built with different canopy weighing schemes, comparisons of spatial predictions of foliar traits. Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. https://doi.org/10.6084/m9.figshare.3521438 The current record is supplemented by this dataset	dataset	January 2016
Appendix A. Comparisons of model accuracies with literature, species-wise estimates of canopy foliar traits,comparisons of models built with different canopy weighing schemes, comparisons of spatial predictions of foliar traits. Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. https://doi.org/10.6084/m9.figshare.3521438.v1 The current record is supplemented by this dataset	dataset	January 2016
Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties [Supplemental Data] Singh, Aditya; Serbin, Shawn; McNeil, Brenden https://doi.org/10.6084/m9.figshare.c.3297065 The current record is supplemented by this fileset	fileset	August 2016
Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties Singh, Aditya; Serbin, Shawn P.; McNeil, Brenden E. https://doi.org/10.6084/m9.figshare.c.3297065.v1 The current record is supplemented by this dataset	dataset	August 2016

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

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