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Sensors 2013, 13(8), 10027-10051; doi:10.3390/s130810027
Article

Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression

 and *
Received: 1 July 2013; in revised form: 1 August 2013 / Accepted: 2 August 2013 / Published: 6 August 2013
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2013)
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Abstract: Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model to predict the total AGB involved PLSR and the Maximum Band Depth index derived from the continuum removed reflectance in the absorption features between 916–1,120 nm and 1,079–1,297 nm (R2 = 0.939, RMSE = 7.120 g/m2). Regarding the green fraction of the AGB, the Area Over the Minimum index derived from the continuum removed spectra provided the most accurate model overall (R2 = 0.939, RMSE = 3.172 g/m2). Identifying the appropriate absorption features was proved to be crucial to improve the performance of PLSR to estimate the total and green aboveground biomass, by using the indices derived from those spectral regions. Ordinary Least Square Regression could be used as a surrogate for the PLSR approach with the Area Over the Minimum index as the independent variable, although the resulting model would not be as accurate.
Keywords: biomass; continuum removal; spectrometer; hyperspectral; radiometry; Area Over the Minimum; Maximum Band Depth; PLSR; SVM; OLSR biomass; continuum removal; spectrometer; hyperspectral; radiometry; Area Over the Minimum; Maximum Band Depth; PLSR; SVM; OLSR
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Marabel, M.; Alvarez-Taboada, F. Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression. Sensors 2013, 13, 10027-10051.

AMA Style

Marabel M, Alvarez-Taboada F. Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression. Sensors. 2013; 13(8):10027-10051.

Chicago/Turabian Style

Marabel, Miguel; Alvarez-Taboada, Flor. 2013. "Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression." Sensors 13, no. 8: 10027-10051.


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