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Table 8 Prediction methods with addition of the residual estimation by ordinary kriging using the prediction and validation sets for the Eucalyptus stands

From: Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Method

Statistic

Basal area error (m2)

Volume error (m3)

Prediction set

Validation set

Prediction set

Validation set

MLR-RK

ME

0.00

− 0.03

–

–

MAE

0.03

0.09

–

–

RMSE

0.04

0.14

–

–

RMSE (%)

2.80

9.30

–

–

RI

49.09

1.27

–

–

RF-RK

ME

0.01

− 0.05

0.00

− 1.03

MAE

0.04

0.10

0.63

1.70

RMSE

0.05

0.15

0.77

2.26

RMSE (%)

3.08

10.08

5.02

15.25

RI

− 24.19

− 5.66

− 5.24

− 2.28

SVM-RK

ME

0.01

− 0.03

− 0.32

− 0.57

MAE

0.05

0.10

0.80

1.22

RMSE

0.06

0.15

1.11

1.74

RMSE (%)

4.09

9.83

7.19

11.72

RI

1.21

− 4.69

30.93

13.70

ANN-RK

ME

0.02

− 0.06

–

–

MAE

0.04

0.06

–

–

RMSE

0.09

0.09

–

–

RMSE (%)

5.79

6.37

–

–

RI

34.72

25.23

–

–

  1. MLR multiple linear regression, RF random forest, SVM support vector machine, ANN artificial neural networks, RK residual estimation by ordinary kriging, ME mean error, MAE mean absolute error, RMSE root mean square error, RI relative improvement