Abstract
A method to forecast forest inventory variables derived from light detection and ranging (LiDAR) would increase the usefulness of such data in future forest management. We evaluated the accuracy of forecasted inventory from imputed tree lists for LiDAR grid cells (20 × 20 m) in spruce (Picea sp.) plantations and tree growth predicted using a locally calibrated tree-list growth model. Tree lists were imputed by matching measurements from a library of sample plots with grid cells based on planted species and the smallest sum of squared difference between six inventory variables. Total and merchantable basal area, total and merchantable volume, Lorey’s height, and quadratic mean diameter increments predicted using imputed tree lists were highly correlated (0.75–0.86) with those from measured tree lists in 98 validation plots. Percent root mean squared error ranged from 12.8–49.0% but was much lower (4.9–13.5%) for plots with ≤10% LiDAR-derived error for all plot-matched variables. When compared with volumes from 15 blocks harvested 3–5 years after LiDAR acquisition, average forecasted volume differed by only 1.5%. To demonstrate the novel application of this method for operational management decisions, annual commercial thinning was planned at grid-cell resolution from 2018–2020 using forecasted inventory variables and commercial thinning eligibility rules.
Highlights
The use of light detection and ranging (LiDAR) to produce forest inventories has significantly changed forest management [1,2]
Inventory variable increments predicted with Open Stand Model (OSM) using tree lists imputed by plot matching were strongly correlated with those using measured tree lists from 98 calibration/validation plots
Percentage mean error and percentage root-mean-square error (RMSE) ranged from −3.6–0.0% and 12.8–36.6% for BAt, VOLt, VOLm, LHt, and quadratic mean diameter (QMDt), but were 11.3% and 49.0% for BAm, respectively
Summary
The use of light detection and ranging (LiDAR) to produce forest inventories has significantly changed forest management [1,2]. Around the time of LiDAR acquisition, tree-level data (e.g., species, diameter at breast height (DBH), total height) are collected from calibration ground plots and used to calculate plot-level variables of interest (e.g., average tree height, total volume) These plots are selected to represent the range of structural variability within the area of interest [3]. Using parametric or non-parametric methods, the statistical relationship between the variables of interest and point-cloud statistics is determined and used to predict variable values in non-sampled areas at grid-cell resolutions matching the size of the calibration plots (e.g., 20 × 20 m) [3,4] Such model-based estimates derived from LiDAR have been shown to provide accurate forest- and stand-level estimates of average tree DBH [5,6], average tree height [7,8], crown base height [9,10], basal area [11,12], and volume [13,14], with good spatial characterization of within-stand variability.
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