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Spectroscopic Determination of Aboveground Biomass in Grass using Partial Least Square Regression Model

Journal: International Journal of Scientific Engineering and Technology (Vol.6, No. 9)

Publication Date:

Authors : ;

Page : 332-335

Keywords : biomass; partial least square regression; vegetation indices; hyperspectral; red edge position;

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Abstract

Biomass is considered as fresh matter weight or dry matter weight. Biomass is regarded as an important indicator of ecological and management processes in the vegetation. This research aims to predict biomass of grass by using partial least square regression [PLSR] model. To build this PLSR model, we used two variables as an input for PLSR model, i.e. reflectance of aboveground grass and weight of grass [biomass]. For this research we used grass type Cynodon dactylon.We used ASD FieldSpec4 Spectroradiometer for measuring reflectance of aboveground biomass. For enhancing spectral properties of green vegetation we used five vegetation indices.We estimated aboveground biomass in grass with root mean square error [RMSE] 6.33 and = 0.9 per g/ by using PLSR model

Last modified: 2017-11-15 01:59:50