Regression optimization

Dependent variables

Intercept & predictor variables

Regr. Coeff.

±SE

t-value

p-value

R²

RMSE

n

MCPS%

Intercept

32.177

4.604

6.99

<0.0001

0.61

10.11

394

Equation (14)

MC_PS,DTW

0.268

0.062

4.341

<0.0001

log₁₀DTW

−1.493

0.786

−1.899

0.0583

D_b

42.561

3.284

12.961

<0.0001

Elevation

−0.109

0.01

−10.655

<0.0001

HW Blocks

13.09

1.552

8.434

<0.0001

Using plot-specific determinations for each block

Tracks

3.934

1.044

3.769

0.0002

MCV%

Intercept

35.254

3.407

10.347

<0.0001

0.51

7.49

394

Equation (15)

MC_PS,DTW

0.19

0.046

4.171

<0.0001

log₁₀DTW

−1.330

0.582

−2.285

0.0229

D_b

13.378

2.43

5.503

<0.0001

Elevation

−0.076

0.008

−10.086

<0.0001

HW Blocks

9.377

1.148

8.163

<0.0001

Tracks

2.437

0.773

3.154

0.0017

MC_PS%

Intercept

70.883

6.326

11.205

<0.0001

0.47

9.45

394

Equation (16)

MC_PS,DTW

0.262

0.073

3.589

0.0002

log₁₀DTW

−2.764

0.926

−2.986

0.003

OM_DSM

−0.981

0.278

−3.529

0.0005

Using regionally available DEM, DSM, DTW layers together with forest cover and plot/weather-specific FIA assignments

Elevation

−0.091

0.013

−6.937

<0.0001

HW Blocks

10.224

2.136

4.788

<0.0001

Tracks

4.09

1.233

3.316

0.001

MCV%

Intercept

49.018

4.096

11.968

<0.0001

0.48

7.72

394

Equation (17)

MC_PS,DTW

0.186

0.047

3.932

<0.0001

log₁₀DTW

−1.665

0.599

−2.927

0.0057

OM_DSM

−0.421

0.180

−2.338

0.0199

Elevation

−0.073

0.008

−8.650

<0.0001

HW Blocks

9.000

1.383

6.509

<0.0001

Tracks

2.449

0.798

3.067

0.0023