Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2218
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dc.contributor.authorMitri, Georgeen_US
dc.contributor.authorGitas, Ioannis Z.en_US
dc.date.accessioned2020-12-23T09:08:46Z-
dc.date.available2020-12-23T09:08:46Z-
dc.date.issued2013-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/2218-
dc.description.abstractCareful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.en_US
dc.format.extent7 p.en_US
dc.language.isoengen_US
dc.subjectForest regenerationen_US
dc.subjectVegetation recoveryen_US
dc.subjectVery high spatial resolution imageryen_US
dc.subjectHyperspectral imageryen_US
dc.subjectObject-based classificationen_US
dc.titleMapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imageryen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationInstitute of Environmenten_US
dc.description.volume20en_US
dc.description.startpage60en_US
dc.description.endpage66en_US
dc.date.catalogued2018-01-08-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=http://www.sciencedirect.com/science/article/pii/S030324341100122X#en_US
dc.identifier.OlibID175817-
dc.relation.ispartoftextInternational journal of applied earth observation and geoinformationen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Institute of Environment
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