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dc.contributor.supervisor Hopkinson, Christopher
dc.contributor.author Xi, Zhouxin
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2019-06-26T17:08:01Z
dc.date.available 2019-06-26T17:08:01Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10133/5431
dc.description.abstract Biomass measurement provides a baseline for ecosystem valuation required by modern forest management. The advent of ground-based LiDAR technology, renowned for 3D sampling resolution, has been altering the routines of biomass inventory. The thesis develops a set of innovative approaches in support of fine-scale biomass inventory, including automatic extraction of stem statistics, robust delineation of plot biomass components, accurate classification of individual tree species, and repeatable scanning of plot trees using a lightweight scanning system. Main achievements in terms of accuracy are a relative root mean square error of 11% for stem volume extraction, a mean classification accuracy of 0.72 for plot wood components, and a classification accuracy of 92% among seven tree species. The results indicate the technical feasibility of biomass delineation and monitoring from plot-level and multi-species point cloud datasets, whereas point occlusion and lack of fine-scale validation dataset are current challenges for biomass 3D analysis from ground. en_US
dc.description.sponsorship S.G.S. International Tuition Award from the University of Lethbridge The Dean's Scholarship from the University of Lethbridge Campus Alberta Innovates Program NSERC Discovery Grants Program en_US
dc.language.iso en_US en_US
dc.publisher Lethbridge, Alta. : University of Lethbridge, Dept. of Geography en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject 3D en_US
dc.subject Forest biomass en_US
dc.subject Multisensor data fusion en_US
dc.subject Optical radar en_US
dc.subject Terrestrial Laser Scanning en_US
dc.title Fine-scale Inventory of Forest Biomass with Ground-based LiDAR en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geography en_US
dc.degree.level Ph.D en_US
dc.proquest.subject Remote sensing [0799] en_US
dc.proquest.subject Physical geography [0368] en_US
dc.proquest.subject Forestry [0478] en_US
dc.proquestyes Yes en_US


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