Estimation of pruning biomass of olive trees using airborne discrete-return LiDAR data

Abstract

The use of residual biomass from fruit tree pruning requires the creation of logistic schedules based on the knowledge of the amount of residues available in each plot. Remote sensing is shown as an important tool to quantify these resources. The objective of this research was to estimate the amount of pruned materials from olive trees (Olea europea L.) using dendrometric parameters obtained from field measurements and airborne LiDAR (Light Detection and Ranging) data. Pruning, mean crown diameter, stem diameter, total height and crown height were measured in the field for a set of 25 trees in Viver, Central East Spain. Airborne discrete LiDAR data were acquired and used to extract the crown area of the trees and metrics based on the height distribution of the LiDAR points. Two types of regression models to predict the amount of pruned material were computed, firstly using dendrometic measurements obtained in the field, and then based on tree metrics derived from LiDAR data. The dendrometric variables obtained from field measurements were also compared with the metrics obtained from LiDAR data. High values of R2 were obtained for the models, ranging from 0.86 to 0.89. The regression models derived from LiDAR data used the area as an explanatory variable. The results of this study show the potential of airborne LiDAR data to predict pruning residues in olive trees.