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dc.contributorPla Bañón, Filiberto
dc.contributorCaetano, Mário
dc.contributorPrinz, Torsten
dc.contributor.authorSoloviov, Oleksii
dc.contributor.otherUniversitat Jaume I. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-04-19T07:06:26Z
dc.date.available2016-04-19T07:06:26Z
dc.date.issued2014-02-28
dc.identifier.urihttp://hdl.handle.net/10234/158860
dc.descriptionTreball final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial. Codi: SIW013. Curs acadèmic 2013-2014ca_CA
dc.description.abstractEvery year oak forests become infected by populations of the splendor beetle (Agrilus bigutattus). The detection and monitoring of infected trees is important, because of economic and ecological reasons. Traditional approach to detect the pest infestation level of each tree is performed by using ground-based observation method. It is long and ineffective method because of limitations, such as: poor visibility of the highest trees and impenetrability of some forest plots. The main goal is to identify infected oaks trees by splendor beetle at the 2 study areas. Pest-infested oak trees by splendor beetle are characterized by high level of defoliation and different reflection signatures. These features can be detected by using very high resolution color infrared (CIR) images. In August 2013 it was performed flight campaign by using unmanned aerial systems (UAS). CIR images were covering 2 test sites in rural area, near city Soest (Germany). Study areas represents small, privately owned oaks forest plots. In this research was used a small quadrocopter (Microdrone MD4-200) with vertical takeoff and landing capability (VTOL). Microdrone is carried a digital camera (Canon PowerShot SD 780 IS). Additionally, camera was modified to capture not just a visible spectrum, but also NIR spectrum (400 to 1100 nm) of infected oaks. The proposed workflow includes the CIR image acquisition, image stitching, radiometric correction, georeferencing, modified vegetation indices calculation, pixel based and object-based image classification and accuracy assessment. Images were classified using 5 classes (healthy, low infected, high infected, died trees and canopy gaps). Finally, the results can be integrated with existing WMS service. Applying of UAV make possible to obtain multitemporal data, which facilitates monitoring and detection of infected trees. The work was performed in close cooperation with the Forestry Department of Soest (Germany).ca_CA
dc.format.extentXII, 62 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherUniversitat Jaume Ica_CA
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 Spain*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/*
dc.subjectMàster Universitari Erasmus Mundus en Tecnologia Geoespacialca_CA
dc.subjectErasmus Mundus University Master's Degree in Geospatial Technologiesca_CA
dc.subjectMáster Universitario Erasmus Mundus en Tecnología Geoespacialca_CA
dc.subjectColor-infrared Images (CIR)ca_CA
dc.subjectNear-infrared Images (NIR)ca_CA
dc.subjectObject-based Classificationca_CA
dc.subjectPest infestationca_CA
dc.subjectPixel-based classificationca_CA
dc.subjectPrincipal componentca_CA
dc.subjectUnmanned eerial vehicleca_CA
dc.subjectVegetation indicesca_CA
dc.subjectVery high resolution imagesca_CA
dc.titleGeospatial assessment of pest-induced forest damage through the use of UAV-based NIR imaging and GI-technologyca_CA
dc.typeinfo:eu-repo/semantics/masterThesisca_CA
dc.educationLevelEstudios de Postgradoca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA


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