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dc.contributor.authorZhao, Chunhong ( Orcid Icon 0000-0003-0391-6420 )
dc.contributor.authorJensen, Jennifer L. R. ( Orcid Icon 0000-0002-5296-9062 )
dc.contributor.authorWeng, Qihao ( Orcid Icon 0000-0002-2498-0934 )
dc.contributor.authorWeaver, Russell ( )
dc.date.accessioned2021-07-08T18:41:19Z
dc.date.available2021-07-08T18:41:19Z
dc.date.issued2018-09
dc.identifier.citationZhao, C., Jensen, J., Weng, Q., & Weaver, R. (2018). A geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon. Remote Sensing, 10(9): 1428.en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/13821
dc.description.abstractThis study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.en_US
dc.formatText
dc.format.extent18 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.sourceRemote Sensing, 2018, Vol. 10, No. 9, Article 1428.
dc.subjectSurface Urban Heat Index (SUHI)en_US
dc.subjectLandscapeen_US
dc.subjectGeographically weighted regressionen_US
dc.subjectSpatial patternen_US
dc.subjectAustinen_US
dc.subjectSan Antonioen_US
dc.titleA Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenonen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2018 The Authors.
dc.identifier.doihttps://doi.org/10.3390/rs10091428
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentGeography


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