Comparing Modeling Methods for Predicting Forest Attributes Using LiDAR Metrics and Ground Measurements

AbstractSelected modeling methods are compared for predicting 5 forest attributes, basal area (BA), stem volume (VOL), Lorey's height (LOR), quadratic mean diameter (QMD), and tree density (DEN), from airborne LiDAR metrics in southwestern Oregon, in the United States. The selected methods included most similar neighbor (MSN) imputation, gradient nearest neighbor (GNN) imputation, Random Forest (RF)–based imputation, BestNN imputation, ordinary least square (OLS) regression, spatial linear model (SLM), and geographically weighted regression (GWR). Several performances of each method were assessed by 500 simulations with different numbers of training data. No single modeling method was always superior to the others in prediction of the forest attributes. The best method varied according to response variable, prediction type, and performance measures, even though there was a leading group (SLM, OLS, BestNN, and GWR) that always outperformed the other methods in root mean squared prediction error (RMSPE). Th...