# Table 2 Results from plot level regression analysis of soil carbon (SOC), above ground tree biomass (AGB) and below ground tree biomass (BGB) using Landsat 8 OLI, ALS and the combination of these data sources

Data source Modela,b R2(adj) RMSE RMSE
% Mg ha−1 %
OLI 140731 SOC = −113.8 + 0.0637 B7 + 22.93 B5/B4 34.6 16.2 27.9
ALS SOC = 74.97 − 0.000500 XL1 + 0.425 XL2 + 0.02500 XL3 42.4 15.2 26.2
ALS + OLI 140731 SOC = −4.2 + 36.93 B7/B4 − 0.000429 XL1 + 0.00433 XL4 56.0 13.3 22.9
OLI 140512 AGB = 35.3 − 0.0661 B6 − 18.41 B5/B4 + 55.20 B6/B4 38.1 30.6 66.2
ALS AGB = 5.92 − 5.05 P60 + 1.248 PFR50 + 0.576 PFR75 64.4 23.3 50.3
ALS + OLI 140512 AGB = 80.8 − 0.02178 B5 − 3.38 P60 + 1.499 PFR75 66.0 22.7 49.1
OLI 140512 BGB = 39.4 − 0.02009 B5 + 7.12 B6/B4 40.1 11.6 62.2
ALS BGB = 1.99 − 1.960 MAD − 0.943 P70 + 0.6644 PFR500 71.5 8.1 43.4
ALS + OLI 140512 BGB = 19.43 − 0.00549 B5 − 0.03186 XLS1 + 0.6417 PFR500 71.8 8.1 43.3
1. The R2 adj statistic is for the model and RMSE values from ‘leave one out cross validation’ (LOOCV)
2. XL and XLS = combination of different variables. For full variable explanation see [37]
3. XL1 = “return 1 count above −1.00” + “total return count above −1.00”
4. XL2 = “percentage first returns above 1.50”/“P80”
5. XL3 = “P90” * “percentage first returns above 1.50” * “return 1 count above −1.00”/“total return count above −1.00”
6. XL4 = “P90” * “percentage first returns above 1.50” * “return 1 count above −1.00”/“return 2 count above −1.00”
7. XLS1 = “percentage first returns above 1.50” * “P80”/(“NDVI + 1”); MAD = elev MAD median
8. aAll regression coefficients were statistically significant at 5% level
9. bB = Landsat 8 OLI band (1, 2,…,8); P = Height percentiles of lidar vegetation echoes (0, 10,…,90); PFR = Percentage first lidar returns above heightbreak in dm (50, 75, 100)