Abstract
Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. The goal of this study was to compare various imputation methods to predict forest biomass and basal area, at a project planning scale (a combination of ground inventory plots, light detection and ranging (LiDAR) data, satellite imagery, and climate data was analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k = 5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k = 3), followed by the GWR model, and the random forest imputation. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k = 5), followed by k-nn (k = 3), and the random forest method. For both metrics, the GNN method was the least accurate based on the ranking of RMSE and bias.
Highlights
Estimates of forest biomass and basal area provide critical information for quantifying the amount of carbon sequestrated, making management decisions, designing processing plants, guiding decisions among conflicting land uses, and establishing and quantifying wildlife habitats
We examine the performance of four parametric and two non-parametric methods for estimating the amount of standing tree biomass and basal area at a pixel level, across the a site on the Malheur National Forest, in Eastern Oregon, US: Gradient Nearest Neighbor (GNN), Most Similar Neighbor (MSN), k-most similar neighbor (MSN), and the Random Forest (RF) nearest neighbor methods, and linear regression and geographic weighted regression
Substantial differences were found among the predictive abilities of the strategies examined to predict forest biomass and basal area
Summary
Estimates of forest biomass and basal area provide critical information for quantifying the amount of carbon sequestrated, making management decisions, designing processing plants, guiding decisions among conflicting land uses, and establishing and quantifying wildlife habitats. To meet national and international negotiations and reporting requirements, forest management plans require local inventory data on biomass, vegetation, site productivity, carbon, and other resources. Vegetation maps created using GNN figure prominently into interagency (Oregon Department of Forestry, USDI Bureau of Land Management, and USDA Forest Service) analysis and planning efforts across the Pacific Northwest. They are being used to estimate the supply of woody biomass available to proposed energy facilities and in regional conservation planning. Other techniques that use imputation, including K-NN (kNearest Neighbor), are used in parts of the Pacific Northwest Both GNN and K-NN are used to derive forest biomass and basal area maps. One can combine satellite imagery with data from field plots and impute a raster dataset showing a continuous map of biomass and basal area across the landscape (Ohmann & Gregory, 2002)
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