Tree defoliation classification based on point distribution features derived from single-scan terrestrial laser scanning data

Abstract Forest disruption caused by pest insects is a common disaster occurring in plantations, and is a threatening factor to forest health. Therefore, a precise method for monitoring individual tree health and estimating disturbance severity is urgently needed. Theoretically, terrestrial laser scanning (TLS) is a promising tool in high resolution remote sensing, which can provide information regarding the structural change of the affected trees with millimeter precision. However, few studies have explored the potential of TLS application in this field, especially when using only mono-temporal data. In this study, a single-scan TLS data-based method was developed and validated to classify defoliation at both individual-tree scale and plot scale. The objects were classified into three classes: healthy/slightly defoliated, moderately defoliated and severely defoliated. Sixty features were extracted from TLS data and optimized to six (individual-tree scale) and five (plot scale) explanatory variables by using a Random Forest method to accomplish the classification. By this approach, individual trees can be classified into three defoliation levels with 80% overall accuracy (kappa value 0.70), while plot-scale classification had 94% overall accuracy (kappa value 0.91). Point distribution characteristics proposed in this method were among the most important features for defoliation estimation. Evidently, the methods presented in this study are capable of providing satisfactory estimates of defoliation severity, and supporting a precise inventory and monitoring of forest health.