A comparison of selected parametric and non-parametric imputation methods for estimating forest biomass and basal area

Donald Gagliasso; Susan Hummel; Hailemariam Temesgen

Treesearch

A comparison of selected parametric and non-parametric imputation methods for estimating forest biomass and basal area

Formally Refereed

Authors:

Donald Gagliasso, Susan Hummel, Hailemariam Temesgen

Year:

2014

Type:

Scientific Journal

Station:

Pacific Northwest Research Station

Source:

Open Journal of Forestry. 4(1): 42-48

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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 (

Keywords

gradient nearest neighbor, most similar neighbor, K-nearest neighbor, random forest, geographic weighted regression, biomass, LiDAR

Citation

Gagliasso, Donald; Hummel, Susan; Temesgen, Hailemariam. 2014. A comparison of selected parametric and non-parametric imputation methods for estimating forest biomass and basal area. Open Journal of Forestry. 4(1): 42-48.