VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Investigating Selection Criteria of Constrained Cluster Analysis: Applications in Forestry

Simple item page
dc.contributor.authorCorral, Gavin Richarden
dc.contributor.committeechairMorgan, J. P.en
dc.contributor.committeememberDu, Pangen
dc.contributor.committeememberBirch, Jeffrey B.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2017-06-13T19:44:30Zen
dc.date.adate2014-12-16en
dc.date.available2017-06-13T19:44:30Zen
dc.date.issued2014-10-29en
dc.date.rdate2014-12-16en
dc.date.sdate2014-11-17en
dc.description.abstractForest measurements are inherently spatial. Soil productivity varies spatially at fine scales and tree growth responds by changes in growth-age trajectories. Measuring spatial variability is a perquisite to more effective analysis and statistical testing. In this study, current techniques of partial redundancy analysis and constrained cluster analysis are used to explore how spatial variables determine structure in a managed regular spaced plantation. We will test for spatial relationships in the data and then explore how those spatial relationships are manifested into spatially recognizable structures. The objectives of this research are to measure, test, and map spatial variability in simulated forest plots. Partial redundancy analysis was found to be a good method for detecting the presence or absence of spatial relationships (~95% accuracy). We found that the Calinski-Harabasz method consistently performed better at detecting the correct number of clusters when compared to several other methods. While there is still more work that can be done we believe that constrained cluster analysis has promising applications in forestry and that the Calinski-Harabasz criterion will be most useful.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-11172014-125046en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-11172014-125046/en
dc.identifier.urihttp://hdl.handle.net/10919/78176en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSimulationen
dc.subjectRedundancy Analysisen
dc.subjectCluster Analysisen
dc.subjectForestryen
dc.titleInvestigating Selection Criteria of Constrained Cluster Analysis: Applications in Forestryen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
etd-11172014-125046_Corral_GR_T_2014_.pdf
Size:
3.82 MB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses