{"corpus_id":52061671,"paper_sha":"7c941653c0f82125164bda04946b9c14ab069360","doi":"10.14358/PERS.78.1.75","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2165148193,"dblp_id":null,"acl_id":null,"title":"A New Method for Segmenting Individual Trees from the Lidar Point Cloud","year":2012,"publication_date":null,"venue":"","journal":{"name":"Photogrammetric Engineering and Remote Sensing","pages":"75-84","volume":"78"},"journal_issn":null,"journal_title":null,"publication_types":[],"pubmed_pub_types":null,"s2_fields_of_study":["Geography","Environmental Science"],"reference_count":50,"citation_count":659,"influential_citation_count":54,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3Dpoint data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.","claims":[{"public_id":"cl_a6978f4538687d10f7de97ddbae2f255","status":"active","text":"A new algorithm segments individual trees from the small footprint discrete return airborne lidar point cloud, whereas typical approaches derive segmentations from the canopy height model.","confidence":0.9,"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/claims/cl_a6978f4538687d10f7de97ddbae2f255"},{"public_id":"cl_566a412b2d17f00a08048476dd02694d","status":"active","text":"Applied to a mixed conifer forest in the Sierra Nevada Mountains, the algorithm achieved 86% recall, 94% precision, and an overall F-score of 0.9.","confidence":0.98,"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/claims/cl_566a412b2d17f00a08048476dd02694d"}],"concepts":[{"public_id":"co_028dc76746fa57d5fdc3ec54946e6c51","status":"active","name":"recall","description":"A performance metric measuring the proportion of actual trees in the forest that the algorithm correctly detected.","types":["performance metric"],"aliases":[],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_028dc76746fa57d5fdc3ec54946e6c51"},{"public_id":"co_18e0a6f2ace5ca65cb67dd9dcb9508e8","status":"active","name":"airborne lidar","description":"A remote sensing technology mounted on aircraft that emits laser pulses to generate 3D point data of forest structure with high spatial resolution and accuracy.","types":["technology","measurement technique"],"aliases":["LiDAR","Light Detection and Ranging"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_18e0a6f2ace5ca65cb67dd9dcb9508e8"},{"public_id":"co_1afe98eb773ca84af7f746eca568d0f0","status":"active","name":"individual tree segmentation","description":"The computational process of identifying and delineating boundaries of individual trees from lidar or canopy-derived data.","types":["method","task"],"aliases":["tree segmentation"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_1afe98eb773ca84af7f746eca568d0f0"},{"public_id":"co_32162d3db55d6fc0b46543bb0c00f14f","status":"active","name":"mixed conifer forest","description":"A forest type composed of multiple conifer species in the Sierra Nevada Mountains, used as the study site for algorithm evaluation.","types":["ecosystem"],"aliases":["mixed conifer stand"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_32162d3db55d6fc0b46543bb0c00f14f"},{"public_id":"co_49c9e4499f407b1ba6b9743954c58a18","status":"active","name":"small footprint discrete return lidar point cloud","description":"Lidar data acquired with a small laser footprint that records multiple discrete return pulses, used as the primary input for tree segmentation in this paper.","types":["data type"],"aliases":["discrete return airborne lidar"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_49c9e4499f407b1ba6b9743954c58a18"},{"public_id":"co_89fece2a9b55f250ebc61ec4b31129dc","status":"active","name":"canopy height model","description":"A rasterized surface model derived from lidar data that represents the height of the forest canopy, typically used as basis for individual tree segmentation.","types":["data product"],"aliases":["CHM"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_89fece2a9b55f250ebc61ec4b31129dc"},{"public_id":"co_93bf7839e578f1ce92814a88086dc130","status":"active","name":"F-score","description":"A combined performance metric representing the harmonic mean of recall and precision.","types":["performance metric"],"aliases":["F-measure"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_93bf7839e578f1ce92814a88086dc130"},{"public_id":"co_985c2216e8fdb6aed388bdfd49909e04","status":"active","name":"Sierra Nevada Mountains","description":"The mountain range in California where the lidar data was collected and the tree segmentation algorithm was experimentally evaluated.","types":["geographic location"],"aliases":["Sierra Nevada"],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_985c2216e8fdb6aed388bdfd49909e04"},{"public_id":"co_ac4de5c911d8494eb9ae4982608c2a6c","status":"active","name":"precision","description":"A performance metric measuring the proportion of algorithm-detected tree segments that correspond to real trees.","types":["performance metric"],"aliases":[],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_ac4de5c911d8494eb9ae4982608c2a6c"},{"public_id":"co_ec7817c4f54b5fa8fee79276253d8d91","status":"active","name":"individual tree structural attributes","description":"Per-tree biophysical measurements derivable from lidar including tree height, crown diameter, and canopy-based height.","types":["measurement"],"aliases":[],"contributors":[{"id":170,"public_id":"gsgmdx9r6e","public_label":"pupuri (gsgmdx9r6e)","roles":["extraction"],"url":"https://sah.borca.ai/u/gsgmdx9r6e"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"},{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["review"],"url":"https://sah.borca.ai/u/ezd9qvkvax"}],"url":"https://sah.borca.ai/concepts/co_ec7817c4f54b5fa8fee79276253d8d91"}],"external_ids":{"DOI":"10.14358/PERS.78.1.75","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":2165148193,"DBLP":null,"ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/52061671","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":630872,"paper_uid":"c81f6f93-5de2-4eb9-b835-686625527039","canonical_identity":{"paper_id":630872,"paper_uid":"c81f6f93-5de2-4eb9-b835-686625527039","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/52061671"}