Rodyti trumpą aprašą

dc.contributor.authorMetrikaitytė-Gudelė, Gustė
dc.contributor.authorSužiedelytė Visockienė, Jūratė
dc.contributor.authorPapšys, Kęstutis
dc.date.accessioned2023-09-18T16:20:43Z
dc.date.available2023-09-18T16:20:43Z
dc.date.issued2022
dc.identifier.other(crossref_id)138635103
dc.identifier.other138635103
dc.identifier.other1
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/113354
dc.description.abstractThe aim of this article is to choose the most appropriate method for identifying and managing land cover changes over time. These processes intensify due to human activities such as agriculture, urbanisation and deforestation. The study is based in the remote sensing field. The authors used four different methods of satellite image segmentation with different data: Synthetic Aperture Radar (SAR) Sentinel-1 data, Multispectral Imagery (MSI) Sentinel-2 images and a fusion of these data. The images were preprocessed under segmentation by special algorithms and the European Space Agency Sentinel Application Platform (ESA SNAP) toolbox. The analysis was performed in the western part of Lithuania, which is characterised by diverse land use. The techniques applied during the study were: the coherence of two SAR images; the method when SAR and MSI images are segmented separately and the results of segmentation are fused; the method when SAR and MSI data are fused before land cover segmentation; and an upgraded method of SAR and MSI data fusion by adding additional formulas and index images. The 2018 and 2019 results obtained for SAR image segmentation differ from the MSI segmentation results. Urban areas are poorly identified because of the similarity of spectre signatures, where urban areas overlap with classes such as nonvegetation and/or sandy territories. Therefore, it is necessary to include the field surveys in the calculations in order to improve the reliability and accuracy of the results. The authors are of the opinion that the calculation of the additional indexes may help to enhance the visibility of vegetation and urban area classes. These indexes, calculated based on two or more different bands of multispectral images, would help to improve the accuracy of the segmentation results.eng
dc.formatPDF
dc.format.extentp. 1-20
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbySocial Sciences Citation Index (Web of Science)
dc.relation.isreferencedbyScopus
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:136865407/datastreams/MAIN/content
dc.titleDigital mapping of land cover changes using the fusion of SAR and MSI satellite data
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references58
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionVilniaus universitetas
dc.contributor.facultyAplinkos inžinerijos fakultetas / Faculty of Environmental Engineering
dc.subject.researchfieldT 004 - Aplinkos inžinerija / Environmental engineering
dc.subject.researchfieldT 010 - Matavimų inžinerija / Measurement engineering
dc.subject.studydirectionE03 - Aplinkos inžinerija / Environmental engineering
dc.subject.studydirectionE04 - Matavimų inžinerija / Measurement engineering
dc.subject.vgtuprioritizedfieldsSD05 - Geodezinės technologijos / Geodetic technologies
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enimage fusion
dc.subject.enSAR
dc.subject.enMSI RGB
dc.subject.ensegmentation
dc.subject.enland cover changes
dc.subject.enLULC
dc.subject.encoherence
dcterms.sourcetitleLand
dc.description.issueiss. 7
dc.description.volumevol. 11
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi10.3390/land11071023
dc.identifier.elaba136865407
dc.identifier.wos000832391500001
dc.identifier.scopus2-s2.0-85134038608
dc.identifier.scopus85134038608


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