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Evaluating the accuracy of imputed forest biomass estimates at the project level

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dc.contributor.advisor Hailemariam, Temesgen
dc.creator Gagliasso, Donald
dc.date.accessioned 2012-11-13T19:13:47Z
dc.date.available 2012-11-13T19:13:47Z
dc.date.copyright 2012-10-01
dc.date.issued 2012-10-01
dc.identifier.uri http://hdl.handle.net/1957/35061
dc.description Graduation date: 2013 en_US
dc.description.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. Previous research has shown that nearest-neighbor imputation methods can accurately estimate forest volume across a landscape by relating variables of interest to ground data, satellite imagery, and light detection and ranging (LiDAR) data. Alternatively, parametric models, such as linear and non-linear regression and geographic weighted regression (GWR), have been used to estimate net primary production and tree diameter. The goal of this study was to compare various imputation methods to predict forest biomass, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. In this study I compared the predictive performance of, 1) linear regression, GWR, gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest and 2) MSN and k-nn when imputing forest biomass at spatial scales ranging from 5,000 to 50,000 acres. To test the imputation methods a combination of ground inventory plots, LiDAR data, satellite imagery, and climate data were 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. The GNN method was the least accurate. 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. The GNN method, again, was the least accurate. The accuracy of MSN, the current imputation method used by the Malheur Nation Forest, and k-nn (k=5), the most accurate imputation method from the second chapter, were then compared over 6 spatial scales: 5,000, 10,000, 20,000, 30,000, 40,000, and 50,000 acres. The root mean square difference (RMSD) and bias were calculated for each of the spatial scale samples to determine which was more accurate. MSN was found to be more accurate at the 5,000, 10,000, 20,000, 30,000, and 40,000 acre scales. K-nn (k=5) was determined to be more accurate at the 50,000 acre scale. en_US
dc.language.iso en_US en_US
dc.relation Forest Explorer en_US
dc.subject LiDAR en_US
dc.subject biomass en_US
dc.subject Geographic Weighted Regression en_US
dc.subject Gradient Nearest Neighbor en_US
dc.subject Most Similar Neighbor en_US
dc.subject random forest en_US
dc.subject.lcsh Forest biomass -- Oregon -- Malheur National Forest -- Measurement en_US
dc.title Evaluating the accuracy of imputed forest biomass estimates at the project level en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Master of Science (M.S.) in Sustainable Forest Management en_US
dc.degree.level Master's en_US
dc.degree.discipline Forestry en_US
dc.degree.grantor Oregon State University en_US
dc.contributor.committeemember Hummel, Susan
dc.contributor.committeemember Murphy, Glen
dc.contributor.committeemember Hibbs, David
dc.description.peerreview no en_us


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