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Table 7 Out-of-bag model evaluation metrics (pseudo R2 and RMSE) from the Random Forest aboveground biomass models

From: Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations

Allometric Equation RMSE RMSE percent of mean Pseudo R2 Percent Bias Number of Predictors (Top 3 Predictors) mtry ntrees
Local 48.1 35.0 0.5936 0.9 11 (Band 1, NDII, DEM) 8 2000
Jenkins 54.8 37.9 0.5377 0.9 10 (NDII, DEM, Band 1) 5 1500
FIA-CRM 40.8 40.8 0.5463 1.1 9 (NDII, Band 2 texture_5 × 5 mean, PAS) 4 1000
  1. These values are the prediction errors only, and do not include allometric error. FIA-CRM Forest Inventory and Analysis Component Ratio Method, RMSE root mean square error, NDII normalized difference infrared index, DEM digital elevation model, PAS precipitation as snow, mtry number of predictor variables randomly sampled at each split in model, ntrees number of trees grown in model