Accurate automated classification of LiDAR point clouds is a well-known problem and proper parameterization of the classification algorithm is essential to creating useful bare-earth terrain models. Parameterization is particularly important in areas characterized by extremely low relief, such as the Little Red River Irrigation Project Area in central Arkansas. In this kind of landscape, analyses such as hydrological flow models are sensitive to small changes in the topography, and therefore prone to errors in the classification of the LiDAR point cloud and the digital elevation models (DEMs) derived from it. Developing effective project-specific parameters requires a high degree of knowledge of each parameters’ complex function, how parameters affect one another, and familiarity with the project data. The workflow and python script produced by this thesis automates the creation of multiple bare-earth classifications in LAStools using a range of parameter combinations and compares each to a small but representative manually-classified control surface. From these automated comparisons, the best performing parameters can be rapidly determined and subsequently applied over a larger area. The python script and workflow produced by research provides a repeatable, automated method of choosing parameters that will reduce or remove the need for further manual classification of a point cloud and therefore remove the need for a highly experienced user, thus allowing a broader range of researchers to obtain accurate bare-earth classifications from increasingly available LiDAR data.
","abstract_html":"<p>Accurate automated classification of LiDAR point clouds is a well-known problem and proper parameterization of the classification algorithm is essential to creating useful bare-earth terrain models. Parameterization is particularly important in areas characterized by extremely low relief, such as the Little Red River Irrigation Project Area in central Arkansas. In this kind of landscape, analyses such as hydrological flow models are sensitive to small changes in the topography, and therefore prone to errors in the classification of the LiDAR point cloud and the digital elevation models (DEMs) derived from it. Developing effective project-specific parameters requires a high degree of knowledge of each parameters’ complex function, how parameters affect one another, and familiarity with the project data. The workflow and python script produced by this thesis automates the creation of multiple bare-earth classifications in LAStools using a range of parameter combinations and compares each to a small but representative manually-classified control surface. From these automated comparisons, the best performing parameters can be rapidly determined and subsequently applied over a larger area. The python script and workflow produced by research provides a repeatable, automated method of choosing parameters that will reduce or remove the need for further manual classification of a point cloud and therefore remove the need for a highly experienced user, thus allowing a broader range of researchers to obtain accurate bare-earth classifications from increasingly available LiDAR data.</p>","abstract_has_math":false,"creators":["Green, Vance"],"institution":null,"degree_name":"Master of Science in Geography (MS)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Limp, Fredrick W. Jr.","Tullis, Jason A."],"advisors":["Cothren, Jackson D."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-12-01T08:00:00Z","date_published":"2015-12-01T08:00:00Z","updated_at":"2026-07-24T00:58:26Z","subjects":["Social sciences","Earth sciences","Airbourne lidar","Lidar","Parameterization","Geographic Information Sciences","Physical and Environmental Geography"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/1383","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Limp, Fredrick W. 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Parameterization is particularly important in areas characterized by extremely low relief, such as the Little Red River Irrigation Project Area in central Arkansas. In this kind of landscape, analyses such as hydrological flow models are sensitive to small changes in the topography, and therefore prone to errors in the classification of the LiDAR point cloud and the digital elevation models (DEMs) derived from it. Developing effective project-specific parameters requires a high degree of knowledge of each parameters’ complex function, how parameters affect one another, and familiarity with the project data. The workflow and python script produced by this thesis automates the creation of multiple bare-earth classifications in LAStools using a range of parameter combinations and compares each to a small but representative manually-classified control surface. From these automated comparisons, the best performing parameters can be rapidly determined and subsequently applied over a larger area. The python script and workflow produced by research provides a repeatable, automated method of choosing parameters that will reduce or remove the need for further manual classification of a point cloud and therefore remove the need for a highly experienced user, thus allowing a broader range of researchers to obtain accurate bare-earth classifications from increasingly available LiDAR data.
"]},{"key":"dc:title","label":"Title","values":["Towards Systematic Selection of Terrain- and Ground Cover-Specific LiDAR Filtering Parameters"]}]}],"canonical_facts":{"dc:contributor":["Limp, Fredrick W. Jr.","Tullis, Jason A."],"dc:contributor.advisor":["Cothren, Jackson D."],"dc:creator":["Green, Vance"],"dc:date":["2015"],"dc:date.available":["2017-09-29T07:00:00Z"],"dc:description.abstract":["Accurate automated classification of LiDAR point clouds is a well-known problem and proper parameterization of the classification algorithm is essential to creating useful bare-earth terrain models. Parameterization is particularly important in areas characterized by extremely low relief, such as the Little Red River Irrigation Project Area in central Arkansas. In this kind of landscape, analyses such as hydrological flow models are sensitive to small changes in the topography, and therefore prone to errors in the classification of the LiDAR point cloud and the digital elevation models (DEMs) derived from it. Developing effective project-specific parameters requires a high degree of knowledge of each parameters’ complex function, how parameters affect one another, and familiarity with the project data. The workflow and python script produced by this thesis automates the creation of multiple bare-earth classifications in LAStools using a range of parameter combinations and compares each to a small but representative manually-classified control surface. From these automated comparisons, the best performing parameters can be rapidly determined and subsequently applied over a larger area. The python script and workflow produced by research provides a repeatable, automated method of choosing parameters that will reduce or remove the need for further manual classification of a point cloud and therefore remove the need for a highly experienced user, thus allowing a broader range of researchers to obtain accurate bare-earth classifications from increasingly available LiDAR data.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/1383"],"dc:subject":["Social sciences","Earth sciences","Airbourne lidar","Lidar","Parameterization","Geographic Information Sciences","Physical and Environmental Geography"],"dc:title":["Towards Systematic Selection of Terrain- and Ground Cover-Specific LiDAR Filtering Parameters"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Geography (MS)"]},"updated_at":"2026-07-24T00:58:26Z"}