Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107092
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSzantoi, Z.en
dc.contributor.authorSmith, S.en
dc.contributor.authorStrona, G.en
dc.contributor.authorKoh, L.en
dc.contributor.authorWich, S.en
dc.date.issued2017en
dc.identifier.citationInternational Journal of Remote Sensing, 2017; 38(8-10):2231-2245en
dc.identifier.issn0143-1161en
dc.identifier.issn1366-5901en
dc.identifier.urihttp://hdl.handle.net/2440/107092-
dc.descriptionPublished online: 23 Jan 2017en
dc.description.abstractConservation of the Sumatran orangutans’ (Pongo abelii) habitat is threatened by change in land use/land cover (LULCC), due to the logging of its native primary forest habitat, and the primary forest conversion to oil palm, rubber tree, and coffee plantations. Frequent LULCC monitoring is vital to rapid conservation interventions. Due to the costs of high-resolution satellite imagery, researchers are forced to rely on cost-free sources (e.g. Landsat), those, however, provide images at a moderate-to-low resolution (e.g. 15–250 m), permitting identification only general LULC classes, and limit the detection of small-scale deforestation or degradation. Here, we combine Landsat imagery with very high-resolution imagery obtained from an unmanned aircraft system (UAS). ​The UAS imagery was used as ‘drone truthing’ data to train image classification algorithms. Our results show that UAS data can successfully be used to help discriminate similar land-cover/use classes (oil palm plantation vs. reforestation vs. logged forest) with consistently high identification of over 75% on the generated thematic map, where the oil palm detection rate was as high as 89%. Because UAS is employed increasingly in conservation proWjects, this approach can be used in a large variety of them to improve land-cover classification or aid-specific mapping needs.en
dc.description.statementofresponsibilityZoltan Szantoi, Scot E. Smith, Giovanni Strona, Lian Pin Koh and Serge A. Wichen
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.rights© 2017 European Union Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.en
dc.titleMapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photographyen
dc.typeJournal articleen
dc.identifier.rmid0030067114en
dc.identifier.doi10.1080/01431161.2017.1280638en
dc.identifier.pubid287526-
pubs.library.collectionEnvironment Institute publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Environment Institute publications

Files in This Item:
File Description SizeFormat 
hdl_107092.pdfPublished version2.83 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.