Hierarchical variable probability sampling for carbon estimation

dc.contributor.advisorKershaw, John
dc.contributor.authorChen, Yingbing
dc.date.accessioned2023-03-01T16:24:12Z
dc.date.available2023-03-01T16:24:12Z
dc.date.issued2019
dc.date.updated2023-03-01T15:02:05Z
dc.description.abstractThis thesis implemented variable probability sampling into carbon inventory and tested the possibility of using sampling to correct LiDAR- assisted enhanced forest inventory for carbon estimation. At first, the efficiency of big BAF sampling was tested using simulation to develop sample designs and estimate costs. Then, list sampling with big BAF sample plots was implemented in a case study carried out at the 5th Canadian Division Support Base Gagetown. In this case study, double sampling with ratio estimation was used to calibrate LiDAR-derived estimates of total volume. Standing trees and dead woody debris carbon estimates were made. Our results showed that big BAF sampling is very efficient for carbon inventory and that a sampling to correct approach using list sampling can yield accurate estimates of aboveground carbon.
dc.description.copyright© Yingbing Chen, 2019
dc.formattext/xml
dc.format.extentviii, 71 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13749
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineForestry and Environmental Management
dc.titleHierarchical variable probability sampling for carbon estimation
dc.typemaster thesis
thesis.degree.disciplineForestry and Environmental Management
thesis.degree.fullnameMaster of Science in Forestry
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.F.

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