Predicting forest parameters through generalized linear mixed models using GEDI metrics in a temperate forest in Oaxaca, Mexico.

Lidar sensors are active remote sensing instruments that provide data on forest structure, which serve as inputs in the prediction of forest parameters. The objective of this study was to evaluate the performance of mixed-effects models to predict basal area (BA), aboveground biomass (AGB), and wood volume (VOL) from GEDI (Global Ecosystem Dynamics Investigation) predictor variables. Linear mixed and generalized linear mixed models (LMM and GLMM) were fitted with a clustering structure as a random effect between forest parameters and GEDI metrics. LMM and GLMM performed similarly regarding root mean square error (RMSE) and correlation coefficients (r) between observed and predicted data. Our results showed moderate correlations between RH50 and BA (r = 0.53), AGB (r = 0.54), and RH80 with VOL (r = 0.62). Another finding was the correlation between GEDI predictions of the AGB density and the biomass predicted in this study, with an agreement of 79% and 77% and a RMSE of 76.95 and 82.34 Mg ha−1 for LMM and GLMM, respectively. We conclude that the use of mixed models allowed for the inclusion of both fixed and random effects that capture the systematic and random variation in the data, and they provide a flexible framework for modelling forest parameters. [ABSTRACT FROM AUTHOR]