{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/7969"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/7969","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Discovetree - An Automated Tool To Generate Stem Maps From Terrestrial Laser Scanner Point Clouds","abstract":"Terrestrial laser scanning (TLS) is increasingly used in forestry to quickly and nondestructively capture a variety of tree attributes such as diameter, height, and volume. However, in order for these attributes to be measured, the individual trees must first be segmented from the point cloud. Tools for manual or semi-automatic tree segmentation are widely available, but a fully automated and generalizable tool does not yet exist. The first step in tree segmentation is the creation of a stem map consisting of the position and size of all trees in the point cloud. We developed a novel stem mapper, \"Discovetree\" that uses Hough transforms combined with a machine learning algorithm calibrated with field data. Our algorithm outperformed a similar existing tool, TreeLS, at both the tree and stand level. Our study examines the consequences of tree shape on common representations (i.e. circles). The results suggest that stem mappers benefit from being tuned on real-world data and that and that analytical approaches that can represent trees by shapes other than circles may be needed to achieve the same levels of accuracy that in-field or manual mensuration can achieve.","abstract_html":"Terrestrial laser scanning (TLS) is increasingly used in forestry to quickly and nondestructively capture a variety of tree attributes such as diameter, height, and volume. However, in order for these attributes to be measured, the individual trees must first be segmented from the point cloud. Tools for manual or semi-automatic tree segmentation are widely available, but a fully automated and generalizable tool does not yet exist. The first step in tree segmentation is the creation of a stem map consisting of the position and size of all trees in the point cloud. We developed a novel stem mapper, "Discovetree" that uses Hough transforms combined with a machine learning algorithm calibrated with field data. Our algorithm outperformed a similar existing tool, TreeLS, at both the tree and stand level. Our study examines the consequences of tree shape on common representations (i.e. circles). The results suggest that stem mappers benefit from being tuned on real-world data and that and that analytical approaches that can represent trees by shapes other than circles may be needed to achieve the same levels of accuracy that in-field or manual mensuration can achieve.","abstract_has_math":false,"creators":["Hartsook, Theodore Elliott"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Greenberg, Jonathan"],"committee_chairs":[],"committee_members":["Weisberg, Peter","Tavakkoli, Alireza"],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-27T21:46:08Z","subjects":["hough transform","lidar","stem mapping","tls"],"languages":[],"rights":["Creative Commons Attribution-ShareAlike 4.0 United States"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/7969","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Greenberg, Jonathan"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Weisberg, Peter","Tavakkoli, Alireza"]},{"key":"dc:creator","label":"Author","values":["Hartsook, Theodore Elliott"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-09-16T19:09:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-09-16T19:09:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hough transform","lidar","stem mapping","tls"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-ShareAlike 4.0 United States"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/7969"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Terrestrial laser scanning (TLS) is increasingly used in forestry to quickly and nondestructively capture a variety of tree attributes such as diameter, height, and volume. However, in order for these attributes to be measured, the individual trees must first be segmented from the point cloud. Tools for manual or semi-automatic tree segmentation are widely available, but a fully automated and generalizable tool does not yet exist. The first step in tree segmentation is the creation of a stem map consisting of the position and size of all trees in the point cloud. We developed a novel stem mapper, \"Discovetree\" that uses Hough transforms combined with a machine learning algorithm calibrated with field data. Our algorithm outperformed a similar existing tool, TreeLS, at both the tree and stand level. Our study examines the consequences of tree shape on common representations (i.e. circles). The results suggest that stem mappers benefit from being tuned on real-world data and that and that analytical approaches that can represent trees by shapes other than circles may be needed to achieve the same levels of accuracy that in-field or manual mensuration can achieve."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Discovetree - An Automated Tool To Generate Stem Maps From Terrestrial Laser Scanner Point Clouds"]}]}],"canonical_facts":{"dc:contributor.advisor":["Greenberg, Jonathan"],"dc:contributor.committeemember":["Weisberg, Peter","Tavakkoli, Alireza"],"dc:creator":["Hartsook, Theodore Elliott"],"dc:date.accessioned":["2021-09-16T19:09:41Z"],"dc:date.available":["2021-09-16T19:09:41Z"],"dc:date.issued":["2021"],"dc:description.abstract":["Terrestrial laser scanning (TLS) is increasingly used in forestry to quickly and nondestructively capture a variety of tree attributes such as diameter, height, and volume. However, in order for these attributes to be measured, the individual trees must first be segmented from the point cloud. Tools for manual or semi-automatic tree segmentation are widely available, but a fully automated and generalizable tool does not yet exist. The first step in tree segmentation is the creation of a stem map consisting of the position and size of all trees in the point cloud. We developed a novel stem mapper, \"Discovetree\" that uses Hough transforms combined with a machine learning algorithm calibrated with field data. Our algorithm outperformed a similar existing tool, TreeLS, at both the tree and stand level. Our study examines the consequences of tree shape on common representations (i.e. circles). The results suggest that stem mappers benefit from being tuned on real-world data and that and that analytical approaches that can represent trees by shapes other than circles may be needed to achieve the same levels of accuracy that in-field or manual mensuration can achieve."],"dc:format":["PDF"],"dc:identifier.uri":["http://hdl.handle.net/11714/7969"],"dc:rights":["Creative Commons Attribution-ShareAlike 4.0 United States"],"dc:subject":["hough transform","lidar","stem mapping","tls"],"dc:title":["Discovetree - An Automated Tool To Generate Stem Maps From Terrestrial Laser Scanner Point Clouds"],"dc:type":["Thesis"],"thesis:degree_level":["Master's Degree"]},"updated_at":"2026-07-27T21:46:08Z"}