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Authordc.contributor.authorWallace, Luke 
Authordc.contributor.authorBellman, Chris 
Authordc.contributor.authorHally, Bryan 
Authordc.contributor.authorHernandez, Jaime 
Authordc.contributor.authorJones, Simon 
Authordc.contributor.authorHillman, Samuel 
Admission datedc.date.accessioned2019-10-22T03:13:56Z
Available datedc.date.available2019-10-22T03:13:56Z
Publication datedc.date.issued2019
Cita de ítemdc.identifier.citationForests, Volumen 10, Issue 3, 2019,
Identifierdc.identifier.issn19994907
Identifierdc.identifier.other10.3390/f10030284
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/172022
Abstractdc.description.abstractPoint clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.
Lenguagedc.language.isoen
Publisherdc.publisherMDPI AG
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
Sourcedc.sourceForests
Keywordsdc.subjectDrones
Keywordsdc.subjectForest measurement
Keywordsdc.subjectImage based point clouds
Keywordsdc.subjectRPAS
Keywordsdc.subjectStructure from motion
Keywordsdc.subjectUAS
Títulodc.titleAssessing the ability of image based point clouds captured from a UAV to measure the terrain in the presence of canopy cover
Document typedc.typeArtículo de revista
dcterms.accessRightsdcterms.accessRightsAcceso Abierto
Catalogueruchile.catalogadorSCOPUS
Indexationuchile.indexArtículo de publicación SCOPUS
uchile.cosechauchile.cosechaSI


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile