A hybrid partial least squares and random forest approach to modelling forest structural attributes using multispectral remote sensing data

Abstract

Up to date forest inventory data has become increasingly essential for sustainable planning and management of a commercial forest plantation. Forest inventory data may be collected in the form of traditional field based approaches or using remote sensing techniques. The aim of this study was to examine the utility of the partial least squares regression (PLSR), random forest (RF) and a PLSR-RF hybrid machine learning approach for the prediction of four forest structural attributes: (basal area, volume, dominant tree height and mean tree height) within a commercial Eucalyptus forest plantation using a combination of spectral and textural information of high spatial resolution (0.15m) remote sensing data. The best model for this study was produced for mature E. dunnii species for dominant tree height using the PLSR-RF hybrid model (R2 = 0.82 and RMSE = 2.07m). The results of this study highlight the robustness and potential of the PLSR-RF hybrid model for the prediction of forest structural attributes using high resolution imagery within a commercial Eucalyptus forest plantation.