Table 5.

Regression model’s performance (R², RMSE, MAE, RMSE%, MAE%) obtained from the complete dataset (2017–2018–2019) between pruning weight and NDVI filtered (NDVI_f), canopy thickness (Thick), canopy volume (VolDem), NDVI filtered and canopy thickness (NDVI_f Thick), NDVI filtered, canopy thickness and canopy volume (NDVI_f Thick VolDem). Each cell presents 2 values, the first calculated without cross validation, and the second using fivefold cross validation. The best resulting models are bold typed.

Pruning weight (kg)	NDVI_f	Thick	VolDem	NDVI_f*Thick	NDVI_f*Thick*VolDem
Linear
R2	0.63–0.61	0.59 -0.57	0.55–0.52	0.68–0.61	0.69–0.66
RMSE	0.07–0.08	0.08–0.08	0.08–0.09	0.07–0.08	0.07–0.07
MAE	0.06–0.06	0.07–0.06	0.07–0.07	0.05–0.06	0.06–0.06
RMSE%	0.20–0.23	0.23–0.23	0.23–0.26	0.20–0.23	0.20–0.20
MAE%	0.17–0.17	0.20–0.17	0.20–0.20	0.14–0.17	0.17–0.17
Trees fine
R2	0.80–0.58	0.71–0.55	0.72–0.4	0.85–0.58	0.85–0.52
RMSE	0.06–0.08	0.07–0.08	0.07–0.1	0.05–0.08	0.04–0.09
MAE	0.04–0.06	0.05–0.06	0.05–0.08	0.03–0.06	0.03–0.07
RMSE%	0.17–0.23	0.20–0.23	0.20–0.29	0.14–0.23	0.11–0.26
MAE%	0.11–0.17	0.14–0.17	0.14–0.23	0.09–0.17	0.09–0.20
RF boosted
R2	0.70–0.56	0.67–0.58	0.68–0.51	0.75–0.64	0.78–0.67
RMSE	0.07–0.08	0.07–0.08	0.07–0.09	0.06–0.07	0.06–0.07
MAE	0.05–0.06	0.05–0.05	0.05–0.07	0.04–0.05	0.04–0.05
RMSE%	0.20–0.23	0.20–0.23	0.20–0.26	0.17–0.20	0.17–0.20
MAE%	0.14–0.17	0.14–0.14	0.14–0.20	0.11–0.14	0.11–0.14
SVM fine Gaussian
R2	0.69–0.52	0.69–0.53	0.63–0.54	0.72–0.44	0.75–0.35
RMSE	0.07–0.08	0.07–0.08	0.07–0.08	0.06–0.09	0.06–0.1
MAE	0.05–0.06	0.05–0.07	0.05–0.06	0.04–0.07	0.03–0.07
RMSE%	0.20–0.23	0.20–0.23	0.20–0.23	0.17–0.26	0.17–0.29
MAE%	0.14–0.17	0.14–0.20	0.14–0.17	0.11–0.20	0.09–0.20
Gaussian exp GPR
R2	0.74–0.63	0.79–0.41	0.75–0.45	0.84–0.59	0.86–0.58
RMSE	0.06–0.07	0.06–0.09	0.06–0.09	0.05–0.08	0.04–0.08
MAE	0.05–0.05	0.04–0.06	0.05–0.07	0.04–0.05	0.03–0.06
RMSE%	0.17–0.20	0.17–0.26	0.17–0.26	0.14–0.23	0.11–0.23
MAE%	0.14–0.14	0.11–0.17	0.14–0.20	0.11–0.14	0.09–0.17