Rodyti trumpą aprašą

dc.contributor.authorDaugėla, Ignas
dc.contributor.authorSužiedelytė Visockienė, Jūratė
dc.contributor.authorKumpienė, Jūratė
dc.date.accessioned2023-09-18T20:28:43Z
dc.date.available2023-09-18T20:28:43Z
dc.date.issued2020
dc.identifier.issn2611-4127
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/150165
dc.description.abstractLandfill operators must collect data on the topography of their landfills, their biological and hydrological characteristics, and local meteorological conditions. These data can be collected by satellite, using Unmanned Aerial Vehicles (UAVs), or by traditional methods such as static flux chambers or modelling. They serve as the basis for landfill monitoring, including the identification and measurement of methane (CH4) gas emissions. Here, we present an approach for landfill mapping using sensor data from unmanned aerial drone systems (UADS) based on DJI Matrice 200 UAVs with Zenmuse X4S sensors and Trimble UX5 UAVs with Sony NEX-5R sensors. RGB (Red, Green, Blue) and near infrared (NIR) data from these sensors were processed using a Geographic Information System (GIS) to generate orthoimages, digital elevation models (DEMs), and normalized difference vegetation index (NDVI) maps. These were then used to evaluate changes in the surface structure and topography of the study area (Kariotiskes landfill, Lithuania). The NDVI maps were used to identify areas of sparse vegetation cover that may indicate localized CH4 emissions. Surfacetemperature maps based on thermal infrared (TIR) images were then prepared for analysis of these problematic areas. Finally, the presence of CH4 in these areas was investigated using a prototype lightweight gas sensor array. The structure of the Kariotiskes landfill site remained unchanged over three years, but there is evidence of possible CH4 gas influence at the landfill cover’s surface. The combination of UADS-mounted imaging systems and the prototype gas sensor array enabled rapid analysis of emission hotspots and of landfill topography.eng
dc.formatPDF
dc.format.extentp. 127-138
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyEmerging Sources Citation Index (Web of Science)
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyScopus
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://detritusjournal.com/
dc.source.urihttps://digital.detritusjournal.com/articles/detection-and-analysis-of-methane-emissions-from-a-landfill-using-unmanned-aerial-drone-systems-and-semiconductor-sensors/312
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:58246371/datastreams/MAIN/content
dc.titleDetection and analysis of methane emissions from a landfill using unmanned aerial drone systems and semiconductor sensors
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references49
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionLuleå University of Technology,
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.vgtuprioritizedfieldsSD05 - Geodezinės technologijos / Geodetic technologies
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enLandfill
dc.subject.enUADS
dc.subject.enRGB
dc.subject.enTIR
dc.subject.enNIR
dcterms.sourcetitleDetritus - Multidisciplinary journal for waste resources and residues
dc.description.volumevol. 10
dc.publisher.nameCisa publisher
dc.publisher.cityPadova
dc.identifier.doi000546632500014
dc.identifier.doi10.31025/2611-4135/2020.13942
dc.identifier.elaba58246371


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