Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level

Table 4 Coefficients of determination (R²) and root mean square errors (RMSE) between the predicted and field-measured biomass at plot level using simple regression methods

Variable	Simple linear regression		Log transformed simple regression
Variable	R²	RMSE, g	R²	RMSE, g
H_max	0.45**	540.88	0.45**	534.22
H_mean	0.59**	478.53	0.59**	461.92
H99	0.59**	487.22	0.60**	478.55
H98	0.65**	463.21	0.66**	452.51
H97	0.67**	450.19	0.68**	438.69
H96	0.69**	435.83	0.70**	423.29
H95	0.70**	424.75	0.71**	411.57
H94	0.71**	418.13	0.72**	404.22
H93	0.72**	409.72	0.74**	394.65
H92	0.74**	400.38	0.75**	383.87
H91	0.75**	392.48	0.76**	374.88
H90	0.76**	387.18	0.77**	368.72
H89	0.76**	384.09	0.78**	364.9
H88	0.77**	383.10	0.78**	363.52
H87	0.77**	381.70	0.78**	361.71
H86	0.78**	377.77	0.79**	357.07
H85	0.78**	374.82	0.80**	353.16
H84	0.79**	374.59	0.80**	352.12
H83	0.79**	376.32	0.8**	353.34
H82	0.78**	379.15	0.8**	356.11
H81	0.78**	382.18	0.79**	359.13
H80	0.78**	384.22	0.79**	361.04
Canopy cover	0.01	697.11	0.01	706.48
PLA	0	718.57	0	733.73
Volume	0.01	725.22	0.02	730.90
PAI	0.01	697.10	0	718.56
3DPI	0.24*	615.58	0.24*	626.41

Italic values indicate the most important variable and the corresponding prediction accuracy (i.e., R2 and RMSE) of simple regression models (i.e., SLR and LSR)
* p < 0.05; ** p < 0.01

ISSN: 1746-4811