Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic

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dc.authoridPouya, Sima/0000-0001-6419-1756
dc.authoridAghlmand, Majid/0000-0003-0534-5393
dc.authorwosidPouya, Sima/AAA-6397-2021
dc.authorwosidAghlmand, Majid/ACO-2322-2022
dc.contributor.authorPouya, Sima
dc.contributor.authorAghlmand, Majid
dc.date.accessioned2024-08-04T20:52:13Z
dc.date.available2024-08-04T20:52:13Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractA recently conducted study by the Centers for Disease Control and Prevention encouraged access to urban green space for the public over the prevalence of COVID-19 in that exposure to urban green space can positively affect the physical and mental health, including the reduction rate of heart disease, obesity, stress, stroke, and depression. COVID-19 has foregrounded the inadequacy of green space in populated cities. It has also highlighted the extant inequities so as to unequal access to urban green space both quantitatively and qualitatively. In this regard, it seems that one of the problems related to Malatya is the uncoordinated distribution of green space in different parts of the city. Therefore, knowing the quantity and quality of these spaces in each region can play an effective role in urban planning. The aim of the present study has been to evaluate urban green space per capita and to investigate its distribution based on the population of the districts of Battalgazi county in Malatya city through developing an integrated methodology (remote sensing and geographic information system). Accordingly, in Google Earth Engine by images of Sentinel-1 and PlanetScope satellites, it was calculated different indexes (NDVI, EVI, PSSR, GNDVI, and NDWI). The data set was prepared and then by combining different data, classification was performed according to support vector machine algorithm. From the landscaping maps obtained, the map was selected with the highest accuracy (overall accuracy: 94.43; and kappa coefficient: 90.5). Finally, by the obtained last map, the distribution of urban green space per capita and their functions in Battalgazi county and its districts were evaluated. The results of the study showed that the existing urban green spaces in the Battalgazi/Malatya were not distributed evenly on the basis of the districts. The per capita of urban green space is twenty-four regions which is more than 9m(2) and in twenty-three ones is less than 9m(2). The recommendation of this study was that Turkiye city planners and landscape designers should replan and redesign the quality and equal distribution of urban green spaces, especially during and following COVID-19 pandemic. Additionally, drawing on the Google Earth Engine cloud system, which has revolutionized GIS and remote sensing, is recommended to be used in land use land cover modeling. It is straightforward to access information and analyze them quickly in Google Earth Engine. The published codes in this study makes it possible to conduct further relevant studies.en_US
dc.identifier.doi10.1007/s10661-022-10298-z
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue9en_US
dc.identifier.pmid35922695en_US
dc.identifier.scopus2-s2.0-85135501331en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10661-022-10298-z
dc.identifier.urihttps://hdl.handle.net/11616/100824
dc.identifier.volume194en_US
dc.identifier.wosWOS:000835713700008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Monitoring and Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBattalgazien_US
dc.subjectCOVID-19 pandemicen_US
dc.subjectGoogle Earth Engineen_US
dc.subjectMalatyaen_US
dc.subjectPSScene4Banden_US
dc.subjectSentinel-1en_US
dc.subjectSupport vector machine algorithmen_US
dc.subjectUrban green spaces per capitaen_US
dc.subjectUrbanizationen_US
dc.titleEvaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemicen_US
dc.typeArticleen_US

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