A practical workflow for applying validation strategies in tree height-diameter modelling.

Abstract

Accurate validation of height-diameter (H-D) models is important for forest modelling applications, yet validation approaches are often applied inconsistently across forestry studies. This may produce different estimates of model performance depending on data structure and validation conditions. Therefore, the objective of this study was to present a practical workflow for applying internal, grouped, and external validation approaches in H-D modelling. Leave-one-out cross-validation (LOOCV), K-fold cross-validation, leave-one-district-out (LODO), leave-one-species-out (LOSO), and independent external validation strategies were included. Publicly available forest inventory datasets from Nepal were used to demonstrate the workflow, including a national multi-species dataset and two independent Shorea robusta datasets from western and far-western Nepal. The workflow demonstrated how different testing structures influence estimated model performance under within-dataset, grouped, and independent prediction conditions. This method provides a reproducible framework for validation in forest inventory and allometric modelling studies.•A structured workflow is developed to select appropriate validation strategies based on data availability and modelling objectives.•Internal, structured, and external validation are applied to assess model performance under different scenarios.•A framework for the practical interpretation of validation results is provided to guide their use in general and site-specific modelling contexts.