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Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes

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dc.contributor.advisor Hailemariam, Temesgen
dc.creator Goerndt, Michael E.
dc.date.accessioned 2010-03-29T22:32:58Z
dc.date.available 2010-03-29T22:32:58Z
dc.date.copyright 2010-03-16
dc.date.issued 2010-03-29T22:32:58Z
dc.identifier.uri http://hdl.handle.net/1957/15095
dc.description Graduation date: 2010 en
dc.description.abstract One of the most common practices regarding estimation of forest attributes is the partitioning of large forested subpopulations into smaller areas of interest to coincide with specific objectives of present and future forest management. New estimators are needed to improve estimation of selected forest attributes in small areas where the existing sample is insufficient to obtain precise estimates. This dissertation assessed the strength of light detection and ranging (LiDAR) as auxiliary information for estimating plot-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, Lorey's height) using intensity and nonintensity area-level LiDAR metrics and single tree remote sensing (STRS). LiDAR intensity metrics were useful for increasing precision for trees/ha. With the exception of Lorey's height, STRS did not significantly improve precision for most of the attributes. Small area estimation (SAE) techniques were assessed for precision and bias in estimating stand-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, mean height of 100 largest trees/ha) assuming a localized subpopulation using LiDAR auxiliary information. Selected estimation methods included area-level regression-based composite estimators and indirect estimators based on synthetic prediction and nearest neighbor imputation. The composite estimators produced lower bias and higher precision than synthetic prediction and imputation. The traditional composite estimator outperformed empirical best linear unbiased prediction for bias but not for precision. SAE methods were compared for precision and bias in estimating county-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, mean height of 100 largest trees/ha) assuming a regional subpopulation using Landsat auxiliary information. Selected estimation methods included unit-level mixed regression-based indirect and composite estimators, and imputation-based indirect and composite estimators. The indirect and composite estimators based on linear mixed effects models generally outperformed those based on imputation. The composite estimators performed the best in terms of bias for all attributes. en
dc.language.iso en_US en
dc.relation Explorer Site::Forest Explorer en
dc.subject small area estimation en
dc.subject light detection and ranging en
dc.subject forest attributes en
dc.subject composite predictor en
dc.subject.lcsh Radar in forestry en
dc.subject.lcsh Forests and forestry -- Research -- Methodology en
dc.subject.lcsh Forest surveys -- Methodology en
dc.subject.lcsh Estimation theory en
dc.subject.lcsh Forests and forestry -- Sampling -- Methodology en
dc.title Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes en
dc.type Thesis/Dissertation en
dc.degree.name Doctor of Philosophy (Ph. D.) in Forest Resources en
dc.degree.level Doctoral en
dc.degree.discipline Forestry en
dc.degree.grantor Oregon State University en
dc.contributor.committeemember Monleon, Vicente
dc.contributor.committeemember Wing, Michael
dc.contributor.committeemember Lesser, Virginia
dc.contributor.committeemember Ocamb, Cynthia


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