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dc.contributor.authorMaimaitiyiming, Matthew
dc.contributor.authorGhulam, Abduwasit
dc.contributor.authorTiyip, Tashpolat
dc.contributor.authorPla, Filiberto
dc.contributor.authorLatorre Carmona, Pedro
dc.contributor.authorHalik, Ümüt
dc.contributor.authorSawu, Mamat
dc.contributor.authorCaetano, Mário
dc.date.accessioned2015-10-14T11:58:25Z
dc.date.available2015-10-14T11:58:25Z
dc.date.issued2014-03
dc.identifier.citationMAIMAITIYIMING, Matthew, et al. Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, vol. 89, p. 59-66.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/135506
dc.description.abstractThe urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climateca_CA
dc.format.extent36 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfISPRS Journal of Photogrammetry and Remote Sensing, 2014, vol. 89ca_CA
dc.rights2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectLand surface temperatureca_CA
dc.subjectLandscape metricsca_CA
dc.subjectNormalized mutual information measureca_CA
dc.subjectRemote sensingca_CA
dc.subjectSustainable urban planningca_CA
dc.subjectUrban heat islandca_CA
dc.subjectUrban green spaceca_CA
dc.titleEffects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptationca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp:\\dx.doi.org/10.1016/j.isprsjprs.2013.12.010
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://www.sciencedirect.com/science/article/pii/S0924271614000021ca_CA
dc.editionPostprintca_CA
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_CA


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