Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species Composition

Abstract

Multispectral light detection and ranging (LiDAR) instruments, such as Optech Titan, record intensities at multiple wavelengths and these intensities can be used for tree species prediction in the same way as multispectral image data. In this paper, our main objective was to compare the accuracy of tree species prediction in a boreal forest area using multispectral LiDAR, the 1064-nm wavelength channel (“unispectral LiDAR”), and unispectral LiDAR with auxiliary aerial image data. We also evaluated the effect of the widely used intensity range correction method. We classified the main tree species of field plots using linear discriminant analysis (LDA) and predicted the species-specific volume proportions (%) for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and broadleaved trees using the k -nearest neighbor imputation. The effect of intensity correction on prediction errors for the dominant tree species was evaluated using optimal parameters derived from: 1) minimal intensity difference between flight lines; 2) parameters suggested by theory; and 3) uncorrected data. Although the range correction increased the classification accuracy slightly, it was observed to be ambiguous, and not consistent with theory for canopy echoes. Regardless, the intensity values were useful for the prediction of dominant tree species and species' volume proportions. The results for the dominant tree species classification using multispectral LiDAR [overall accuracy (OA) 88.2%, kappa 0.79] were comparable to the use of unispectral LiDAR and aerial images (OA 89.1%, kappa 0.81). We conclude that the multispectral LiDAR may become a useful tool in operational species-specific forest inventories.