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Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties
Author(s) -
Singh Aditya,
Serbin Shawn P.,
McNeil Brenden E.,
Kingdon Clayton C.,
Townsend Philip A.
Publication year - 2015
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/14-2098.1
Subject(s) - partial least squares regression , canopy , remote sensing , mean squared error , imaging spectroscopy , lignin , range (aeronautics) , hyperspectral imaging , spectroscopy , vegetation (pathology) , nitrogen , pixel , mathematics , environmental science , statistics , chemistry , ecology , botany , physics , optics , materials science , biology , geography , medicine , pathology , composite material , organic chemistry , quantum mechanics
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, δ 15 N) as well as pixel‐wise uncertainties in their estimates based on error propagation in the analytical methods. Both M area and %N PLSR models had a R 2 > 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 R 2 > 0.65 and carbon and cellulose with R 2 > 0.45. Although R 2 of %C and cellulose were lower than M area 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 δ 15 N was the lowest ( R 2 = 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.