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dc.contributor.author Antonarakis, A. S.
dc.contributor.author Brasington, James
dc.contributor.author Richards, Keith S.
dc.date.accessioned 2011-02-14T12:39:17Z
dc.date.available 2011-02-14T12:39:17Z
dc.date.issued 2008-06-16
dc.identifier.citation Antonarakis , A S , Brasington , J & Richards , K S 2008 , ' Object-based land cover classification using airborne LiDAR ' Remote Sensing of Environment , pp. 2988-2998 . , 10.1016/j.rse.2008.02.004 en
dc.identifier.issn 1879-0704
dc.identifier.other PURE: 157507
dc.identifier.other dspace: 2160/6108
dc.identifier.uri http://hdl.handle.net/2160/6108
dc.description Antonarakis, A., Richards, K. S., Brasington, J. (2008). Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112(6), 2988-2998. Sponsorship: British Society for Geomorphology (BSG) and from the William Vaughan Lewis and Phillip Lake Funds. en
dc.description.abstract Light Detection and Ranging (LiDAR) provides high resolution horizontal and vertical spatial point cloud data, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. LiDAR information potential is made even greater though, with its consideration of intensity. Elevation and intensity airborne LiDAR data are used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files, using a supervised object-orientated approach. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. Classifications were performed using two methods, one that included the influence of the ground in heavily vegetated areas, and the other which eliminated the ground points before classification. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95% and 94% for the methods including and excluding the ground influence respectively. Five types of riparian forest were classified with accuracies between 66 and 98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof Remote Sensing of Environment en
dc.title Object-based land cover classification using airborne LiDAR en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.rse.2008.02.004
dc.contributor.institution Institute of Geography & Earth Sciences en
dc.contributor.institution River Basin Dynamics and Hydrology en
dc.description.status Peer reviewed en


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