The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.
","abstract_html":"<p>The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.</p>","abstract_has_math":false,"creators":["Maceyka, Andrew"],"institution":null,"degree_name":"M.S.","degree_level":"Campus Access Thesis","degree_discipline":"Geography","degree_department":null,"school":null,"contributors":["John R Jensen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T04:36:56Z","subjects":["Geography","Social and Behavioral Sciences","Dominant Height","Forestry","Fusion","Laser Scanning","Lidar","Site Index"],"languages":[],"rights":["© 2010, Andrew Maceyka"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/1293","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John R Jensen"]},{"key":"dc:creator","label":"Author","values":["Maceyka, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Geography","Social and Behavioral Sciences","Dominant Height","Forestry","Fusion","Laser Scanning","Lidar","Site Index"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2010, Andrew Maceyka"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/1293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.
"]},{"key":"dc:title","label":"Title","values":["Modeling Loblolly Pine Dominant Height Using Airborne LiDAR"]}]}],"canonical_facts":{"dc:contributor":["John R Jensen"],"dc:creator":["Maceyka, Andrew"],"dc:description.abstract":["The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/1293"],"dc:rights":["© 2010, Andrew Maceyka"],"dc:subject":["Geography","Social and Behavioral Sciences","Dominant Height","Forestry","Fusion","Laser Scanning","Lidar","Site Index"],"dc:title":["Modeling Loblolly Pine Dominant Height Using Airborne LiDAR"],"thesis:degree_discipline":["Geography"],"thesis:degree_level":["Campus Access Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T04:36:56Z"}