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2022-04-30Zeitschriftenartikel DOI: 10.18452/24910
Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020
dc.contributor.authorFahad Baqa, Muhammad
dc.contributor.authorLu, Linlin
dc.contributor.authorChen, Fang
dc.contributor.authorHuda, Nawaz
dc.contributor.authorLuyang, Pan
dc.contributor.authorTariq, Aqil
dc.contributor.authorQureshi, Salman
dc.contributor.authorLi, Bin
dc.contributor.authorLi, Qingting
dc.contributor.editorOsmond, Paul
dc.contributor.editorBartesaghi Koc, Carlos
dc.date.accessioned2022-07-01T11:39:42Z
dc.date.available2022-07-01T11:39:42Z
dc.date.issued2022-04-30none
dc.date.updated2022-05-05T14:00:43Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/25593
dc.description.abstractUnderstanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.eng
dc.description.sponsorshipthe Strategic Priority Research Program of the Chinese Academy of Sciences
dc.description.sponsorshipthe National Natural Science Foundation of China
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectsurface urban heat islandeng
dc.subjectland surface temperatureeng
dc.subjectmegacityeng
dc.subjectsustainable development goalseng
dc.subjectKarachieng
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitennone
dc.titleCharacterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020none
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/25593-8
dc.identifier.doihttp://dx.doi.org/10.18452/24910
dc.type.versionpublishedVersionnone
local.edoc.pages19none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn2072-4292
dcterms.bibliographicCitation.doi10.3390/rs14092164none
dcterms.bibliographicCitation.journaltitleRemote sensingnone
dcterms.bibliographicCitation.volume14none
dcterms.bibliographicCitation.issue9none
dcterms.bibliographicCitation.articlenumber2164none
dcterms.bibliographicCitation.originalpublishernameMDPInone
dcterms.bibliographicCitation.originalpublisherplaceBaselnone
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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