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

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/fq977x78g

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • 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.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Language
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2010-03-29T22:32:58Z (GMT) No. of bitstreams: 1 Goerndt_Dissertation_Final.pdf: 888179 bytes, checksum: 8b8fc2a69920e3b9dec24109debe5848 (MD5)
  • description.provenance : Submitted by Michael Goerndt (goerndtm@onid.orst.edu) on 2010-03-23T16:28:36Z No. of bitstreams: 1 Goerndt_Dissertation_Final.pdf: 888179 bytes, checksum: 8b8fc2a69920e3b9dec24109debe5848 (MD5)
  • description.provenance : Made available in DSpace on 2010-03-29T22:32:58Z (GMT). No. of bitstreams: 1 Goerndt_Dissertation_Final.pdf: 888179 bytes, checksum: 8b8fc2a69920e3b9dec24109debe5848 (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2010-03-23T21:26:29Z (GMT) No. of bitstreams: 1 Goerndt_Dissertation_Final.pdf: 888179 bytes, checksum: 8b8fc2a69920e3b9dec24109debe5848 (MD5)

Relationships

In Administrative Set:
Last modified: 08/14/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items