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Table 3 Regression models between UAV-derived biovolume and leaf biomass (n = 571)

From: Estimation of aboveground biomass and carbon stocks of Quercus ilex L. saplings using UAV-derived RGB imagery

Model

R 2 adjusted

p-value

RMSE

AIC

Lineal

0.85

< 0.001

117.16

7027.28

Second-order polynomial regression

0.87

< 0.001

109.88

6955.36

Third-order polynomial regression

0.86

< 0.001

112.51

6982.24

Fourth-order polynomial regression

0.86

< 0.001

113.85

6995.78

Logarithmic regression

0.58

< 0.001

199.30

7630.84

Inverse exponential regression

0.62

< 0.001

187.32

7560.39

  1. RMSE, root-mean-square error; AIC, Akaike information criterion
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Annals of Forest Science

ISSN: 1297-966X