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Article: Effects of land use and land cover pattern on urban temperature variations: A case study in Hong Kong

TitleEffects of land use and land cover pattern on urban temperature variations: A case study in Hong Kong
Authors
KeywordsGeographically weighted regression
Land surface temperature
Land use/land cover
Remote sensing
Urban Heat Island
Issue Date2020
Citation
Urban Climate, 2020, v. 34, article no. 100693 How to Cite?
AbstractA remarkable phenomenon in urban climate is the urban heat island (UHI) effect. Based on satellite imagery and geographical and climatological data, this study analyzes the patterns of land surface temperature (LST) and land use/land cover (LULC) in Hong Kong and the seasonal variations of the relationships between them. Remote sensing, Geographical Information System (GIS) and statistical methods are used for analysis. The results indicate that LST is significantly affected by LULC types. The ordinary least squared (OLS) models indicate that LST is positively related to Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) but negatively related to Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), elevation and wind speed. The Geographically Weighted Regression (GWR) model indicates that LST is negatively related to NDVI and elevation. The magnitudes of the influences of indices vary with the season. The GWR model demonstrates better fitness compared to the OLS models. Regression results on the GIS maps reveal the fitness of the models across the study area and the spatial variations of the regression coefficients. Such information may help decision makers develop policies at the regional scale and fine-tune planning practices at the local scale to improve the urban thermal environment.
Persistent Identifierhttp://hdl.handle.net/10722/346885
ISSN
2023 Impact Factor: 6.0
2023 SCImago Journal Rankings: 1.318

 

DC FieldValueLanguage
dc.contributor.authorSiqi, Jia-
dc.contributor.authorYuhong, Wang-
dc.date.accessioned2024-09-17T04:13:56Z-
dc.date.available2024-09-17T04:13:56Z-
dc.date.issued2020-
dc.identifier.citationUrban Climate, 2020, v. 34, article no. 100693-
dc.identifier.issn2212-0955-
dc.identifier.urihttp://hdl.handle.net/10722/346885-
dc.description.abstractA remarkable phenomenon in urban climate is the urban heat island (UHI) effect. Based on satellite imagery and geographical and climatological data, this study analyzes the patterns of land surface temperature (LST) and land use/land cover (LULC) in Hong Kong and the seasonal variations of the relationships between them. Remote sensing, Geographical Information System (GIS) and statistical methods are used for analysis. The results indicate that LST is significantly affected by LULC types. The ordinary least squared (OLS) models indicate that LST is positively related to Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) but negatively related to Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), elevation and wind speed. The Geographically Weighted Regression (GWR) model indicates that LST is negatively related to NDVI and elevation. The magnitudes of the influences of indices vary with the season. The GWR model demonstrates better fitness compared to the OLS models. Regression results on the GIS maps reveal the fitness of the models across the study area and the spatial variations of the regression coefficients. Such information may help decision makers develop policies at the regional scale and fine-tune planning practices at the local scale to improve the urban thermal environment.-
dc.languageeng-
dc.relation.ispartofUrban Climate-
dc.subjectGeographically weighted regression-
dc.subjectLand surface temperature-
dc.subjectLand use/land cover-
dc.subjectRemote sensing-
dc.subjectUrban Heat Island-
dc.titleEffects of land use and land cover pattern on urban temperature variations: A case study in Hong Kong-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.uclim.2020.100693-
dc.identifier.scopuseid_2-s2.0-85090279210-
dc.identifier.volume34-
dc.identifier.spagearticle no. 100693-
dc.identifier.epagearticle no. 100693-

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