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dc.contributor.authorJayathunga, Sadeepa
dc.contributor.authorOwari, T.
dc.contributor.authorTsuyuki, S.
dc.contributor.authorHirata, Y.
dc.date.accessioned2020-04-21T21:56:31Z
dc.date.available2019-08-01en
dc.date.issued2020-01-02
dc.date.submitted2019-06-02en
dc.identifier.issn0143-1161en
dc.identifier.urihttps://hdl.handle.net/10182/11759
dc.description.abstractForest canopy structure is an important parameter in multipurpose forest management. An understanding of forest structure plays a particularly important role in the management of uneven-aged forests. The identification of vertical and horizontal variations in forest canopy structure using a ground-based survey is resource intensive, hence often demands for alternative data sources. In this study, one of the advanced remote sensing (RS) techniques, i.e. digital aerial photogrammetry was used to characterize forest canopy structure in a mixed conifer–broadleaf forest. We used aerial imagery acquired with a fixed-wing unmanned aerial vehicle (UAV) platform to produce RS metrics that could be used to classify and map forest structure types at landscape scale. Our results demonstrated that few structural and spectral metrics derived from UAV photogrammetric data, e.g. mean height, standard deviation of height, canopy cover, and percentage broadleaf vegetation cover, could characterize the forest structure across landscapes, particularly at the forest management compartment level, in a limited amount of time. We used cluster analysis for classification of forest structure types and identified five forest structure classes with varying levels of forest canopy structural complexity: (1) short, open-canopy, conifer-dominated structure; (2) short, dense-canopy, broadleaf-dominated structure; (3) tall, closed-canopy, broadleaf-dominated structure; (4) very tall, closed-canopy, conifer-dominated structure with a relatively high degree of variation in canopy height; and (5) very tall, closed-canopy, conifer-dominated structure with a relatively low degree of variation in canopy height. These classes showed relationships with forest management activities (e.g. selection harvesting) and natural disturbances (e.g. typhoon damage). Spatial distribution of forest canopy structural complexity that was revealed in this study is capable of providing important information for forest management planning and habitat modelling. Further, the simple, and flexible data-driven method used in this study to characterize forest structure has the potential to be applied with necessary changes over larger landscapes and different forest types for characterizing and mapping forest structural complexity.en
dc.format.extent53-73en
dc.language.isoen
dc.publisherTaylor & Francis
dc.relationThe original publication is available from - Taylor & Francis - https://doi.org/10.1080/01431161.2019.1648900en
dc.relation.urihttps://doi.org/10.1080/01431161.2019.1648900en
dc.rights© 2019 Informa UK Limited, trading as Taylor & Francis Group
dc.subjectforest managementen
dc.subjectGeological & Geomatics Engineeringen
dc.titlePotential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forestsen
dc.typeJournal Article
lu.contributor.unitLincoln University
lu.contributor.unitFaculty of Agriculture and Life Sciences
lu.contributor.unitDepartment of Agricultural Sciences
dc.identifier.doi10.1080/01431161.2019.1648900en
dc.subject.anzsrc070504 Forestry Management and Environmenten
dc.relation.isPartOfInternational Journal of Remote Sensingen
pubs.issue1en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/AGSC
pubs.publication-statusPublisheden
pubs.volume41en
dc.identifier.eissn1366-5901en
lu.identifier.orcid0000-0003-3077-179X


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