Analysing the Impact of Urban Growth on Agricultural Lands Using Sleuth Model and Google Earth Engine

dc.contributor.author Karasaka, Lutfiye
dc.contributor.author Gunes, Murat
dc.date.accessioned 2025-01-10T20:54:48Z
dc.date.available 2025-01-10T20:54:48Z
dc.date.issued 2024
dc.description.abstract In this study, it is aimed to determine the urban growth in the Sel & ccedil;uklu district of Konya, which is the study area with the SLEUTH model based on cellular automata, which is widely used in the modeling of urban growth and land use, and to examine the effect of urbanization on agricultural areas in the near future. In addition to the simulations carried out for the years 2030 and 2050 starting from 2015, which was determined as the last control year in the model, the simulation results of the year 2022 were compared with the terrain classes obtained from the Google Earth Engine (GEE) controlled classification of the 2022 Landsat satellite image. As a result of the creation of simulation models for the years 2030 and 2050, it was concluded that 10428.75-23747.49 hectares of agricultural land will be destroyed, respectively. The SLEUTH model has modeled a total of 56468.26 hectares of agricultural land for 2022. This corresponds to 95% of the classification result for 2022, which is an important factor in examining the accuracy of the model. This study, which aims to guide decision makers and planners, shows that the use of the SLEUTH model has for the examination of future land use. en_US
dc.identifier.doi 10.36306/konjes.1563738
dc.identifier.issn 2667-8055
dc.identifier.uri https://doi.org/10.36306/konjes.1563738
dc.language.iso en en_US
dc.publisher Konya Teknik Univ en_US
dc.relation.ispartof Konya Journal of Engineering Sciences
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Agricultural Land en_US
dc.subject Gee en_US
dc.subject Remote Sensing en_US
dc.subject Sleuth en_US
dc.subject Urban Growth en_US
dc.title Analysing the Impact of Urban Growth on Agricultural Lands Using Sleuth Model and Google Earth Engine en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.wosid Karasaka, Lutfiye/Aid-4840-2022
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
gdc.bip.popularityclass C5
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.description.department Konya Technical University en_US
gdc.description.departmenttemp [Karasaka, Lutfiye; Gunes, Murat] Konya Tech Univ, Engn & Nat Sci Fac, Geomat Engn Dept, Konya, Turkiye en_US
gdc.description.endpage 1021 en_US
gdc.description.issue 4 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 1006 en_US
gdc.description.volume 12 en_US
gdc.description.woscitationindex Emerging Sources Citation Index
gdc.description.wosquality Q4
gdc.identifier.openalex W4404887601
gdc.identifier.trdizinid 1283666
gdc.identifier.wos WOS:001410998700011
gdc.index.type WoS
gdc.index.type TR-Dizin
gdc.oaire.diamondjournal false
gdc.oaire.impulse 0.0
gdc.oaire.influence 2.4895952E-9
gdc.oaire.isgreen false
gdc.oaire.keywords Fotogrametri ve Uzaktan Algılama
gdc.oaire.keywords Agricultural Land;GEE;Remote Sensing;SLEUTH;Urban Growth
gdc.oaire.keywords Photogrammetry and Remote Sensing
gdc.oaire.popularity 2.3737945E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 02 engineering and technology
gdc.openalex.collaboration National
gdc.openalex.fwci 0.0
gdc.openalex.normalizedpercentile 0.24
gdc.opencitations.count 0
gdc.plumx.mendeley 1
gdc.virtual.author Karasaka, Lütfiye
gdc.wos.citedcount 0
relation.isAuthorOfPublication 271ee519-1581-474e-9025-f48b10e0a336
relation.isAuthorOfPublication.latestForDiscovery 271ee519-1581-474e-9025-f48b10e0a336

Files