NDVI indicated changes in vegetation and their relations to climatic comfort factors in Demre-Akcay Sub basin, Turkey

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dc.contributor.authorBenliay, A.
dc.contributor.authorYilmaz, T.
dc.contributor.authorOlgun, R.
dc.contributor.authorAk, M. K.
dc.date.accessioned2021-12-01T18:48:25Z
dc.date.available2021-12-01T18:48:25Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractAim: The aim of this study was to evaluate the relationship between vegetation change obtained by NDVI analysis and climatic comfort factors by using an artificial neural network model. M ethodology: Fethiye, Elmali, Ka, Finike, Kemer, Korkuteli, Antalya, Tefenni and Kale climatic station's temperature, humidity and wind data were evaluated during this study. Moreover, four Landsat TM satellite images (2006 - 2016) were used as 2 for each year to detect Normalized Difference Vegetation Index (NDVI) values. Climatic comfort maps were created by ArcMap10.3 software using Kriging Method. Difference maps for climatic comfort and NDVI values of satellite images between 2006 - 2016 were created. At the pixel scale, these data were used for teaching artificial neural network model by Neural Designer software in randomly selected 500 points. All NDVI values (-1 -1) and possible vegetation changes (-1 -1) that could occurs in the study area were entered as input to the trained neural network model and the possible values of climate comfort change values were determined. Results: Most significant 2006 NDVI average and mean values were observed at 0.7, 0.6 and 0.5. In the value of NDVI in 2016 forecast, climatic comfort values could get higher in an area which can change into a mediocre or low vegetation value. The maximum average and mean values were 1.0, 0.9 and 0.8. This forecast shows that there could be positive and negative major changes between the climatic comfort values that may occur. Interpretation: Artificial neural networks are the recent ways for such studies and are rapidly developing. Understanding artificial neural networks boundaries and advantages will allow for more efficient modeling tools. As in every part of life, the use of artificial neural networks is expected to increase day by day in landscape planning and design studies.en_US
dc.identifier.doi10.22438/jeb/41/2(SI)IJEB-10
dc.identifier.endpage350en_US
dc.identifier.issn0254-8704
dc.identifier.issue2en_US
dc.identifier.startpage344en_US
dc.identifier.urihttps://doi.org/10.22438/jeb/41/2(SI)IJEB-10
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10528
dc.identifier.volume41en_US
dc.identifier.wosWOS:000529304000011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherTriveni Enterprisesen_US
dc.relation.ispartofJournal Of Environmental Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectNDVIen_US
dc.subjectClimatic comforten_US
dc.subjectThermal Comforten_US
dc.subjectLocal Climateen_US
dc.subjectUrban-Growthen_US
dc.subjectUrbanizationen_US
dc.subjectHeaten_US
dc.titleNDVI indicated changes in vegetation and their relations to climatic comfort factors in Demre-Akcay Sub basin, Turkeyen_US
dc.typeArticleen_US

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