Table 5 Satellite image to ground total fuel density (TFD; kg m−3) and canopy bulk density (CBD; kg m−3) model calibration results using field data collected in 2015 and 2016. Results used to evaluate the potential for mapping coniferous fuel density across the Superior National Forest, Minnesota, USA. Models for TFD and CBD are denoted T and C, respectively, while numbers 1, 12, and 123 reference the zenith angle range used for canopy gap fraction measurements: 0 to 7°, 0 to 23°, and 0 to 38°, respectively. FuelCalc-derived estimates of CBD are denoted FC. LANDFIRE’s CBD estimates (LF) for our plot locations are shown for comparison; dashes (–) indicate no data. Descriptive model statistics include mean and maximum (Max) fuel density (kg m−3), standard deviation (SD), adjusted coefficient of determination (Adj R2), root mean squared error (RMSE; kg m−3), cross-validation predicted error sum of squared (PRESS), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The number of initial (iVar) and final (fVar) image predictor variables retained and the number of latent component variables (Comp) used in each of the final models are given
From: Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data
Model | Mean | Max | SD | iVar | fVar | Comp | Adj R2 | RMSE | PRESS | AIC | BIC |
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LF | 0.108 | 0.300 | 0.048 | – | – | – | – | – | – | – | – |
FC | 0.999 | 2.991 | 0.787 | 66 | 29 | 25 | 0.961 | 0.165 | 0.375 | −155.0 | 125.5 |
T1 | 0.669 | 1.037 | 0.154 | 66 | 11 | 3 | 0.340 | 0.122 | 0.833 | −226.0 | 53.3 |
T12 | 1.432 | 2.466 | 0.435 | 66 | 16 | 15 | 0.769 | 0.209 | 0.631 | −152.9 | 71.6 |
T123 | 2.158 | 3.468 | 0.726 | 66 | 23 | 18 | 0.869 | 0.242 | 0.575 | −121.3 | 99.5 |
C1 | 0.472 | 0.934 | 0.210 | 66 | 32 | 24 | 0.950 | 0.048 | 0.463 | −299.7 | 142.9 |
C12 | 1.035 | 2.492 | 0.583 | 66 | 36 | 28 | 0.974 | 0.072 | 0.418 | −236.9 | 157.3 |
C123 | 1.596 | 3.000 | 0.743 | 66 | 38 | 30 | 0.978 | 0.103 | 0.378 | −194.3 | 164.2 |