Modelling forest species using LiDar-derived metrics of forest canopy gaps

Abstract

LiDAR intensity and texture features have reported high accuracies for discriminating forest species, particularly with the utility of the random forest (RF) algorithm. To date, limited studies has utilized LiDAR-derived forest gap information to assist in forest species discrimination. In this study, LiDAR intensity and texture features were extracted from forest canopy gaps to discriminate Eucalyptus grandis and Eucalyptus dunnii within a forest plantation. Additionally, LiDAR intensity and texture information was extracted for both canopy gaps and forest canopy and utilized for species discrimination. Using LiDAR intensity and texture information extracted for both canopy gap and forest canopy, resulted in a model accuracy of 94.74% (KHAT = 0.88). Using only canopy gap information, the RF model obtained an overall accuracy of 90.91% (KHAT = 0.81). The results highlight the potential for using canopy gap information for commercial species discrimination and mapping.