RT info:eu-repo/semantics/article
T1 Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression
A1 Marabel García, Miguel
A1 Álvarez Taboada, María Flor
A2 Ingenieria Agroforestal
K1 Ingeniería forestal
K1 Biomass
K1 Continuum removal
K1 Spectrometer
K1 Hyperspectral
K1 Radiometry
K1 Area Over the Minimum
K1 Maximum Band Deth
K1 PLSR
K1 SVM
K1 OLSR
AB [EN] 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.
PB MDPI
LK https://hdl.handle.net/10612/18871
UL https://hdl.handle.net/10612/18871
NO Marabel García, M. y Álvarez Taboada, F. (2013). Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression. Sensors, 13(8), 10027-10051. https://doi.org/10.3390/s130810027
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 21-jun-2024