Prediction of Density in Standing Trees of Various Wood Species in Natural Forests Using Near-infrared Spectroscopy

Abstract

Density plays an important role as basic information for applying wood as construction materials. Recent years, the application of near-infrared spectroscopy as non-destructive testing (NDT) has been promising. Density prediction for standing trees in huge variation trees and species of natural forest needs to be investigated using NDT as of eco-green harvesting. The combination of density information and near-infrared spectroscopy is enabled to build a prediction model. This research applied increment cores sampling for density prediction analysis using near-infrared spectroscopy method. The research combined increment cores samples from multiple wood species to be analyzed in one chemometrics analysis of cross-validation partial least squares regression (CV-PLSR) to build a prediction model of density.  The research resulted coefficient of determination for cross-validation (R²CV) of 0.76 with number of latent variable (LV) 10 from the 1^st derivative with 13 smoothing-point spectra and wavelength of 1200 – 1800 nm as the best prediction model. The result seemed sufficient enough with those number of LV for this small tube wood sampling of increment cores from multiple wood species. This research proved that building a prediction model for multiple wood species is possible to be done.