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Table 7 Regression models between UAV-derived biovolume and leaf carbon dry weight (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

53.87

6144.65

Second-order polynomial regression

0.87

< 0.001

50.52

6072.73

Third-order polynomial regression

0.86

< 0.001

51.73

6099.61

Fourth-order polynomial regression

0.86

< 0.001

52.35

6113.15

Logarithmic regression

0.58

< 0.001

91.64

6748.21

Inverse exponential regression

0.62

< 0.001

86.13

6677.76

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

ISSN: 1297-966X