Analyses of Forest Foliage II: Measurement of Carbon Fraction and Nitrogen Content by End-Member Analysis

1994 ◽  
Vol 2 (1) ◽  
pp. 15-23 ◽  
Author(s):  
John D. Aber ◽  
Katherine L. Bolster ◽  
Stephen D. Newman ◽  
Margaret Soulia ◽  
Mary E. Martin

Chemical constituents of forest canopies are accepted as key indicators of ecosystem state and function. Remote sensing of these parameters offers the potential for rapid and accurate assessment of rates of key biogeochemical processes over large regions. NASA's Accelerated Canopy Chemistry Program was established to test the accuracy and generality of high-spectral resolution remote sensing in the measurement of lignin, cellulose and nitrogen concentrations in forest canopies. One of the most straightforward methods for extracting constituent concentration information from spectra is through simple linear mixing models of pre-determined end-member or pure compound spectra. While there are ample reasons to expect that linear mixing models will not work in foliar and whole-canopy samples, end-member analysis might still provide important information in support of standard and emerging statistical techniques, such as linear regression and partial least squares regression on first and second difference spectra, by indicating which parts of the spectrum should contain information on the concentrations of important constituents. The purpose of this paper is to present the results of two approaches to determining the value of end-member spectra for estimating constituent concentrations in foliage of temperate zone forest species. The first examines the spectral changes accompanying each step in the chemical proximate analysis method used to determine concentrations in the laboratory, and from them to infer the spectra of the fractions removed at each step. The second is the combination of known materials which approximate these same fractions in foliage into well-mixed samples to determine whether a simple linear mixing model can be used to predict the spectrum of the resulting mixture. Results confirm that the combination of linear mixing models and end-member analysis is not an appropriate technique for obtaining quantitative estimates of constituent concentrations for the major components of foliage of native woody plants, nor do we expect that more detailed analyses of plant ultrastructure or foliar spectra will correct the deficiencies identified. However, these results do suggest that the wet chemical procedures used to extract different carbon fractions produce consistent results with regard to the location of spectral features associated with the compounds removed at each step, and that the spectra of the cellulose and lignin isolated by this technique are very similar to those from pure materials. This in turn suggests that statistical techniques such as linear regression on first and second difference spectra and partial least squares methods which allow or correct for non-linear mixing, should be successful.

2020 ◽  
Vol 12 (18) ◽  
pp. 2867 ◽  
Author(s):  
K. Colton Flynn ◽  
Amy E. Frazier ◽  
Sintayehu Admas

Achieving reproducibility and replication (R&R) of scientific results is tantamount for science to progress, and it is also necessary for ensuring the self-correcting mechanism of the scientific method. Topics of R&R have sailed to the forefront of research agenda in many fields recently but have received less attention in remote sensing in general and specifically for studies utilizing hyperspectral data. Given the extremely local environments in which many hyperspectral studies are conducted (e.g., agricultural field plots), purposeful attention to the repeatability of findings across study locales can help ensure methods are generalizable. This study undertakes an investigation of the nutrient content of tef (Eragrostis tef), an understudied plant that is growing in importance due to both food and forage benefits, but does so within the context of the replicability of methods and findings across two study sites situated in different international and environmental contexts. The aims are to (1) determine whether calcium, magnesium, and protein of both the plant and grain can be predicted using hyperspectral data with partial least squares (PLS) regression with waveband selection, and (2) compare the replicability of models across differing environments. Results suggest the method can produce high nutrient prediction accuracy for both the plant and grain in individual environments, but selection of wavebands for nutrient prediction was not comparable across study areas. The findings suggest that the method must be calibrated in each location, thereby reducing the potential to extrapolate methods to different areas. Our findings highlight the need for greater attention to methods and results replication in remote sensing, specifically hyperspectral analyses, in order for scientific findings to be repeatable beyond the plot level.


1996 ◽  
Vol 35 (2) ◽  
pp. 187-197 ◽  
Author(s):  
W.H.A.M. van den Broek ◽  
E.P.P.A. Derks ◽  
E.W. van de Ven ◽  
D. Wienke ◽  
P. Geladi ◽  
...  

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