Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative included metrics of first-return LiDAR intensity. The third alternative used area-level variables derived from STRS LiDAR metrics. The ALM model for Lorey's height did not change with inclusion of intensity and yielded the best results in terms of both model fit (adjusted R2 = 0.93) and cross-validated relative root mean squared error (RRMSE = 8.1%). The ALM model for density (stems per hectare) had the poorest precision initially (RRMSE = 39.3%), but it improved dramatically (RRMSE = 27.2%) when intensity metrics were included. The resulting RRMSE values of the ALM models excluding intensity for basal area, quadratic mean diameter, cubic stem volume, and average crown width were 20.7, 19.9, 30.7, and 17.1%, respectively. The STRS model for Lorey's height showed a 3% improvement in RRMSE over the ALM models. The STRS basal area and density models significantly underperformed compared with the ALM models, with RRMSE values of 31.6 and 47.2%, respectively. The performance of STRS models for crown width, volume, and quadratic mean diameter was comparable to that of the ALM models.
Each regional journal of applied forestry focuses on research, practice, and techniques targeted to foresters and allied professionals in specific regions of the United States and Canada. The Western Journal of Applied Forestry covers the western United States, including Alaska, and western Canada; WJAF will also consider manuscripts reporting research in northern Mexico that has potential application in the southwestern United States.