Testing the accuracy of new methods for reconstructing historical structure of forest landscapes using GLO survey data

Abstract

The accuracy of methods for reconstructing parameters (e.g., tree density) of historical forest structure from General Land Office (GLO) survey data has not been thoroughly assessed. Past simulation and statistical assessments of plotless density estimators have focused on minimizing estimation error, but not congruent with the specific data available in the GLO surveys. Most GLO reconstruction studies do not reconstruct absolute measures of density, basal area, or diameter-class distributions, key measures used for forest restoration. We tested the accuracy of a suite of plotless density estimators and other survey methods to accurately reconstruct forest attributes using both a field-based modern calibration and a cross-validation with tree-ring reconstructions. In addition to the common distance estimators, we developed several Voronoi-based plotless density estimators that can be used with GLO data. Estimators were assessed using modern survey and plot data collected in the same location and spatial arrangement as the original survey locations in three geographically distinct areas. Results showed that Voronoi-based density estimators were superior to distance-based estimators. Data need to be pooled across locations. Voronoi estimators yielded more accurate measures of density and basal area and can be used at smaller pooling levels without sacrificing much accuracy. At spatial extents of 260 and 520 ha (3- and 6-corner pools), relative mean absolute error (RMAE) averaged 29% and 22%, respectively, for density estimates in all three study areas. To estimate basal area as accurately (i.e., 23%), data must be pooled to 780 ha (9-corner pool). Composition and diameter-class distributions also required larger pooling areas to achieve accurate results. In the cross-validation, accuracy of density and basal area were both superior to accuracy in the modern calibration, and RMAE for density and basal area at all pooling levels averaged 16.6% and 15.7%, respectively. Composition and diameter-class distribution estimates were lower in accuracy. Voronoi-based methods can accurately estimate historical forest parameters across large landscapes and are accurate at finer scales (e.g., 260 ha, 3-corner pool) than previously thought possible. GLO reconstructions complement tree-ring reconstructions but can provide more spatially comprehensive estimates of the historical range of forest variability, facilitating landscape-level restoration.