Prediction of dasometric variables using linear mixed models and airborne LiDAR data

Abstract

Adequate estimation of dasometric parameters such as basal area (AB), above-ground biomass (B), and timber volume (VOL) in
managed forests is a primary requirement to quantify the role of forests in mitigation climate change mitigation. In this context, forest inventories represent the general technique to estimate dasometric parameters, however, they represent a greater consumption of time and resources. Using data derived from remote sensors in the dasometric modeling offers huge possibilities as an auxiliary tool in forestry activities. The objective of this work was to obtain a statistical model for each forest variable of interest: basal area, above-ground biomass and timber volume in a temperate forest under management in Zacualtipán, Hidalgo, Mexico, using linear mixed models and LiDAR (Light Detection And Ranging) data as predictor variables. For this, we consider that the cluster sampling units have spatial correlation with respect to them distributed independently in the field. Metrics derived from LiDAR data were used to fit the models. The metrics related to height and density of the vegetation presented the highest Pearson correlations (r = 0.52 - 0.86) with the different dasometric variables and these were used as predictors in the adjusted models. The results indicated that the random effect of the cluster and the use of variance function significantly improved the heteroscedasticity, since the spatial correlation of the sites was included. This work showed the potential of using linear mixed models to take advantage of the dependency between sites in the same cluster and improve traditional estimates that do not model this hierarchical relationship.