File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments

TitleA marker-free method for registering multi-scan terrestrial laser scanning data in forest environments
Authors
KeywordsTerrestrial laser scanning
Registration
Marker-free
Forest
Issue Date2020
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/issn/09242716
Citation
ISPRS Journal of Photogrammetry and Remote Sensing, 2020, v. 166, p. 82-94 How to Cite?
AbstractTerrestrial laser scanning (TLS) has been recognized as an accurate means for non-destructively deriving three-dimensional (3D) forest structural attributes. These attributes include but are not limited to tree height, diameter at breast height, and leaf area density. As such, TLS has become an increasingly important technique in forest inventory practices and forest ecosystem studies. Multiple TLS scans collected at different locations are often involved for a comprehensive characterization of 3D canopy structure of a forest stand. Among which, multi-scan registration is a critical prerequisite. Currently, multi-scan TLS registration in forests is mainly based on a very time-consuming and tedious process of setting up hand-crafted registration targets in the field and manually identifying the common targets between scans from the collected data. In this study, a novel marker-free method that automatically registers multi-scan TLS data is presented. The main principle underlying our method is to identify shaded areas from the raw point cloud of a single TLS scan and to use them as the key features to register multi-scan TLS data. The proposed method is tested with 17 pairs of TLS scans collected in six plots across China with various vegetation characteristics (e.g., vegetation type, height, and understory complexity). Our results showed that the proposed method successfully registered all 17 pairs of TLS scans with equivalent accuracy to the manual registration approach. Moreover, the proposed method eliminates the process of setting up registration targets in the field, manually identifying registration targets from TLS data, and processing raw TLS data to extract individual tree attributes, which brings it the advantages of high efficiency and robustness. It is anticipated that the proposed algorithms can save time and cost of collecting TLS data in forests, and therefore improves the efficiency of TLS forestry applications.
Persistent Identifierhttp://hdl.handle.net/10722/283325
ISSN
2019 Impact Factor: 7.319
2015 SCImago Journal Rankings: 2.015

 

DC FieldValueLanguage
dc.contributor.authorGuan, H-
dc.contributor.authorSu, Y-
dc.contributor.authorSun, X-
dc.contributor.authorXu, G-
dc.contributor.authorLi, W-
dc.contributor.authorMa, Q-
dc.contributor.authorWu, X-
dc.contributor.authorWu, J-
dc.contributor.authorLiu, L-
dc.contributor.authorGuo, Q-
dc.date.accessioned2020-06-22T02:55:03Z-
dc.date.available2020-06-22T02:55:03Z-
dc.date.issued2020-
dc.identifier.citationISPRS Journal of Photogrammetry and Remote Sensing, 2020, v. 166, p. 82-94-
dc.identifier.issn0924-2716-
dc.identifier.urihttp://hdl.handle.net/10722/283325-
dc.description.abstractTerrestrial laser scanning (TLS) has been recognized as an accurate means for non-destructively deriving three-dimensional (3D) forest structural attributes. These attributes include but are not limited to tree height, diameter at breast height, and leaf area density. As such, TLS has become an increasingly important technique in forest inventory practices and forest ecosystem studies. Multiple TLS scans collected at different locations are often involved for a comprehensive characterization of 3D canopy structure of a forest stand. Among which, multi-scan registration is a critical prerequisite. Currently, multi-scan TLS registration in forests is mainly based on a very time-consuming and tedious process of setting up hand-crafted registration targets in the field and manually identifying the common targets between scans from the collected data. In this study, a novel marker-free method that automatically registers multi-scan TLS data is presented. The main principle underlying our method is to identify shaded areas from the raw point cloud of a single TLS scan and to use them as the key features to register multi-scan TLS data. The proposed method is tested with 17 pairs of TLS scans collected in six plots across China with various vegetation characteristics (e.g., vegetation type, height, and understory complexity). Our results showed that the proposed method successfully registered all 17 pairs of TLS scans with equivalent accuracy to the manual registration approach. Moreover, the proposed method eliminates the process of setting up registration targets in the field, manually identifying registration targets from TLS data, and processing raw TLS data to extract individual tree attributes, which brings it the advantages of high efficiency and robustness. It is anticipated that the proposed algorithms can save time and cost of collecting TLS data in forests, and therefore improves the efficiency of TLS forestry applications.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/issn/09242716-
dc.relation.ispartofISPRS Journal of Photogrammetry and Remote Sensing-
dc.subjectTerrestrial laser scanning-
dc.subjectRegistration-
dc.subjectMarker-free-
dc.subjectForest-
dc.titleA marker-free method for registering multi-scan terrestrial laser scanning data in forest environments-
dc.typeArticle-
dc.identifier.emailWu, J: jinwu@hku.hk-
dc.identifier.authorityWu, J=rp02509-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.isprsjprs.2020.06.002-
dc.identifier.scopuseid_2-s2.0-85086142417-
dc.identifier.hkuros310539-
dc.identifier.volume166-
dc.identifier.spage82-
dc.identifier.epage94-
dc.publisher.placeNetherlands-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats