A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

• 作者: Donald Gagliasso Hailemariam Temesgen Susan Hummel
• 来源期刊: 林学期刊(英文)
• 学科: 医学
• 关键词: Gradient Nearest NEIGHBOR MOST Similar NEIGHBOR K-Nearest NEIGHBOR Random Forest GEOGRAPHIC Weighted Regression Biomass LiDAR
• 年，卷（期）: 2014,（1）
• 页码范围: 42-48
• 页数: 7页
• 分类号: R73

摘要

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.