Moreover, the model performed tree density predictions with higher r2 in tropical forests, coniferous forests, and tropical dry forests. Notwithstanding having the highest r2 in tropical forests, it also was the forest ecosystem with the highest prediction error (Fig 3b).
On average, predicted tree height ranged between 4 to 9 m in all forest ecosystems (averaged from all pixel values). Cloud forest, arid and semi-arid zones had smaller r2 for both target variables, which could be related to the smaller amounts of sampled data in these forest types. However, arid and semi-arid zones seemed to have the smallest error in both tree height and tree density predictions (Fig 3). A Taylor diagram is a graphical approach that quantifies how closely the predicted values match the observed values and uses correlation (r ), standard deviation of the error (SDE) and standard deviation of observed (σz) and predicted (σ) values as evaluators (Wadoux et al., 2022). According to Taylor diagrams, the model had a better predictive performance for tropical dry forest and broadleaf forest when predicting tree height as stated by its correlation and RMSE together (Fig 3a). The model seemed to have the best predictive performance for tropical forest when predicting tree density, nonetheless, all forest types had a similar performance (Fig 3b).