{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/93071"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/93071","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Characterizing vegetation structure using waveform LiDAR","abstract":"The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters.","abstract_html":"The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters.","abstract_has_math":false,"creators":["Wang, Kunxuan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Kumar, Praveen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:43:03Z","date_published":"2016-11-10T18:43:03Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Waveform LiDAR","Canopy clumping","Biomass","Canopy structure"],"languages":["en"],"rights":["Copyright 2016 Kunxuan Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/93071","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Praveen"]},{"key":"dc:creator","label":"Author","values":["Wang, Kunxuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:43:03Z","2018-11-11T10:15:28Z","2016-07-22","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Waveform LiDAR","Canopy clumping","Biomass","Canopy structure"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Kunxuan Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/93071"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Kunxuan Wang, accepted the attached license on 2016-07-22 at 12:30.","The student, Kunxuan Wang, submitted this Thesis for approval on 2016-07-22 at 12:33.","This Thesis was approved for publication on 2016-07-22 at 14:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9982 on 2016-11-10 at 12:25:29","Made available in DSpace on 2016-11-10T18:43:03Z (GMT). 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Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Kunxuan Wang, accepted the attached license on 2016-07-22 at 12:30.","The student, Kunxuan Wang, submitted this Thesis for approval on 2016-07-22 at 12:33.","This Thesis was approved for publication on 2016-07-22 at 14:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9982 on 2016-11-10 at 12:25:29","Made available in DSpace on 2016-11-10T18:43:03Z (GMT). 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