{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/110918"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/110918","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Diameter Estimation of Eucalyptus spp. Plantations in Southern Brazil Using Global Ecosystem Dynamics Investigation Data and Support Vector Regression","abstract":"Forest plantations make up a large percentage of managed forest land globally. Assessing plantation productivity is vital from both commodity production and carbon management standpoints. Measuring the productivity of these areas is essential given their rapid growth and turnover. Transparent metrics to compare reported carbon storage with estimated values are required for internationally transferred mitigation outcomes under Article 6.2 of the Paris Agreement. Data from the Global Ecosystems Dynamics Investigation (GEDI) provide an excellent opportunity to measure plantation forests over large areas. We focused our efforts on Eucalyptus in southern Brazil and used data from an industrial partner to investigate plantation metrics (height, diameter, volume, stems per hectare, etc.) and to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree diameter in these plantations to within 1 cm using space-borne lidar, with broad implications for using space-borne lidars to monitor carbon accretion in secondary forest plantation.","abstract_html":"Forest plantations make up a large percentage of managed forest land globally. Assessing plantation productivity is vital from both commodity production and carbon management standpoints. Measuring the productivity of these areas is essential given their rapid growth and turnover. Transparent metrics to compare reported carbon storage with estimated values are required for internationally transferred mitigation outcomes under Article 6.2 of the Paris Agreement. Data from the Global Ecosystems Dynamics Investigation (GEDI) provide an excellent opportunity to measure plantation forests over large areas. We focused our efforts on Eucalyptus in southern Brazil and used data from an industrial partner to investigate plantation metrics (height, diameter, volume, stems per hectare, etc.) and to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree diameter in these plantations to within 1 cm using space-borne lidar, with broad implications for using space-borne lidars to monitor carbon accretion in secondary forest plantation.","abstract_has_math":false,"creators":["Miller, Benjamin Daniel"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Forestry","degree_department":"Forest Resources and Environmental Conservation","school":null,"contributors":[],"advisors":[],"committee_chairs":["Wynne, Randolph H.","Thomas, Valerie A."],"committee_members":["Schons, Stella Z."],"year":2022,"date_issued":"2022-06-23","date_published":"2022-06-23","updated_at":"2026-07-22T22:19:16Z","subjects":["Remote Sensing","Eucalyptus","lidar","Machine Learning","forest plantations"],"languages":["en"],"rights":["Creative Commons Attribution-ShareAlike 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-sa/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35136"],"render_values":[{"text":"vt_gsexam:35136","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/110918","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Wynne, Randolph H.","Thomas, Valerie A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Schons, Stella Z."]},{"key":"dc:contributor.department","label":"Department","values":["Forest Resources and Environmental Conservation"]},{"key":"dc:creator","label":"Author","values":["Miller, Benjamin Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-06-24T08:00:35Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-24T08:00:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-06-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Forestry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Remote Sensing","Eucalyptus","lidar","Machine Learning","forest plantations"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-ShareAlike 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-sa/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35136"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/110918"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Forest plantations make up a large percentage of managed forest land globally. Assessing plantation productivity is vital from both commodity production and carbon management standpoints. Measuring the productivity of these areas is essential given their rapid growth and turnover. Transparent metrics to compare reported carbon storage with estimated values are required for internationally transferred mitigation outcomes under Article 6.2 of the Paris Agreement. Data from the Global Ecosystems Dynamics Investigation (GEDI) provide an excellent opportunity to measure plantation forests over large areas. We focused our efforts on Eucalyptus in southern Brazil and used data from an industrial partner to investigate plantation metrics (height, diameter, volume, stems per hectare, etc.) and to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree diameter in these plantations to within 1 cm using space-borne lidar, with broad implications for using space-borne lidars to monitor carbon accretion in secondary forest plantation."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Forest management practices have shifted in some cases to very crop-like forest plantings. These areas are functionally different from a 'natural' forest. Understanding the structure of these areas in a rapid and consistent manner is important to quantify the amount of carbon stored within these forests for international climate agreements such as the Paris Agreement. This effort focuses upon Eucalyptus forests in Southern Brazil. Using measurements from a lidar instrument (a lidar system fires a laser beam from space to the ground, recording the 'deflection' of the laser beam and the amount of time it takes to return to the sensor to measure features on the ground) we were able to measure the diameter of the trees to within a centimeter in these forests."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Diameter Estimation of Eucalyptus spp. Plantations in Southern Brazil Using Global Ecosystem Dynamics Investigation Data and Support Vector Regression"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Wynne, Randolph H.","Thomas, Valerie A."],"dc:contributor.committeemember":["Schons, Stella Z."],"dc:contributor.department":["Forest Resources and Environmental Conservation"],"dc:creator":["Miller, Benjamin Daniel"],"dc:date.accessioned":["2022-06-24T08:00:35Z"],"dc:date.available":["2022-06-24T08:00:35Z"],"dc:date.issued":["2022-06-23"],"dc:description.abstract":["Forest plantations make up a large percentage of managed forest land globally. 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Using measurements from a lidar instrument (a lidar system fires a laser beam from space to the ground, recording the 'deflection' of the laser beam and the amount of time it takes to return to the sensor to measure features on the ground) we were able to measure the diameter of the trees to within a centimeter in these forests."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:35136"],"dc:identifier.uri":["http://hdl.handle.net/10919/110918"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution-ShareAlike 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-sa/4.0/"],"dc:subject":["Remote Sensing","Eucalyptus","lidar","Machine Learning","forest plantations"],"dc:title":["Diameter Estimation of Eucalyptus spp. Plantations in Southern Brazil Using Global Ecosystem Dynamics Investigation Data and Support Vector Regression"],"dc:type":["Thesis"],"thesis:degree_discipline":["Forestry"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:16Z"}