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Comparison and analysis of small are...

Goerndt, Michael E.

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes./
作者:	Goerndt, Michael E.
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 3987.
Contained By:	Dissertation Abstracts International71-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3414594
ISBN:	9781124085647

Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes.
Goerndt, Michael E.

Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes. - 150 p.

Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 3987.

Thesis (Ph.D.)--Oregon State University, 2010.

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.

ISBN: 9781124085647Subjects--Topical Terms:

517247
Statistics.

Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes.
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245 1 0 $a Comparison and analysis of small area estimation methods for improving estimates of selected forest attributes.
300 $a 150 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 3987.
500 $a Adviser: Temesgen Hailemariam.
502 $a Thesis (Ph.D.)--Oregon State University, 2010.
520 $a 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.
520 $a 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 non-intensity 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.
520 $a 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.
520 $a 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.
590 $a School code: 0172.
650 4 $a Statistics. $3 517247
650 4 $a Agriculture, Forestry and Wildlife. $3 783690
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710 2 $a Oregon State University. $3 625720
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790 1 0 $a Hailemariam, Temesgen, $e advisor
790 $a 0172
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3414594