dc.contributor.author | Tumelienė, Eglė | |
dc.contributor.author | Sužiedelytė Visockienė, Jūratė | |
dc.contributor.author | Malienė, Vida | |
dc.date.accessioned | 2023-09-18T20:44:41Z | |
dc.date.available | 2023-09-18T20:44:41Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/152264 | |
dc.description.abstract | Areas of agricultural land in Lithuania have decreased from 2005 to 2021 by up to 2.4%. Agricultural lands that are no longer used for their main purpose are very likely to become abandoned and the emergence of such lands can cause a variety of social, economic, and environmental problems. Therefore, it is very important to constantly monitor changes of abandoned agricultural lands. The purpose of the research is to analyse the influence of seasonality on image segmentation for the identification of abandoned land areas. Multi-spectral Sentinel-2 images from different periods (April, July, and September) and three supervised image segmentation methods (Spectral Angle Mapping (SAM), Maximum_Likelihood (ML), and Minimum distance (MD)) were used with the same parameters in this research. Studies had found that the most appropriate time to segment abandoned lands was in September, according to the SAM and ML algorithms. During this period, the intensity of the green colour was the highest and the colour brightness of abandoned lands differed from the colour intensity of other lands. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-16 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Social Sciences Citation Index (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | DOAJ | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://doi.org/10.3390/su13126941 | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:98220229/datastreams/MAIN/content | |
dc.source.uri | 10.3390/su13126941 | |
dc.title | The influence of seasonality on the multi-spectral image segmentation for identification of abandoned land | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This 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.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 26 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas Vytauto Didžiojo universitetas | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Vytauto Didžiojo universitetas Liverpool John Moores University | |
dc.contributor.faculty | Aplinkos inžinerijos fakultetas / Faculty of Environmental Engineering | |
dc.contributor.faculty | Humanitarinis institutasui-button / Institute of Humanitiesui-button | |
dc.subject.researchfield | T 004 - Aplinkos inžinerija / Environmental engineering | |
dc.subject.researchfield | T 010 - Matavimų inžinerija / Measurement engineering | |
dc.subject.studydirection | E03 - Aplinkos inžinerija / Environmental engineering | |
dc.subject.studydirection | E04 - Matavimų inžinerija / Measurement engineering | |
dc.subject.vgtuprioritizedfields | SD05 - Geodezinės technologijos / Geodetic technologies | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | abandoned land | |
dc.subject.en | image segmentation | |
dc.subject.en | remote sensing | |
dc.subject.en | pixels | |
dc.subject.en | classes | |
dcterms.sourcetitle | Sustainability | |
dc.description.issue | iss. 12 | |
dc.description.volume | vol. 13 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 000666377300001 | |
dc.identifier.doi | 10.3390/su13126941 | |
dc.identifier.elaba | 98220229 | |