Exploring methods for developing local climate zones to support climate research
Sigler, Laurence (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Gilabert, Joan (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Villalba, Gara (Universitat Autònoma de Barcelona. Departament d'Enginyeria Química, Biològica i Ambiental)

Fecha:	2022
Resumen:	Meteorological and climate prediction models at the urban scale increasingly require more accurate and high-resolution data. The Local Climate Zone (LCZ) system is an initiative to standardize a classification scheme of the urban landscape, based mainly on the properties of surface structure (e. g. , building, tree height, density) and surface cover (pervious vs. impervious). This approach is especially useful for studying the influence of urban morphology and fabric on the surface urban heat island (SUHI) effect and to evaluate how changes in land use and structures affect thermal regulation in the city. This article will demonstrate three different methodologies of creating LCZs: first, the World Urban Database and Access Portal Tools (WUDAPT); second, using Copernicus Urban Atlas (UA) data via a geographic information system (GIS) client directly; and third via Google Earth Engine (GEE) using Oslo, Norway as the case study. The WUDAPT and GEE methods incorporate a machine learning (random forest) procedure using Landsat 8 imagery, and offer the most precision while requiring the most time and familiarity with GIS usage and satellite imagery processing. The WUDAPT method is performed principally using multiple GIS clients and image processing tools. The GEE method is somewhat quicker to perform, with work performed entirely on Google's sites. The UA or GIS method is performed solely via a GIS client and is a conversion of pre-existing vector data to LCZ classes via scripting. This is the quickest method of the three; however, the reclassification of the vector data determines the accuracy of the LCZs produced. Finally, as an illustration of a practical use of LCZs and to further compare the results of the three methods, we map the distribution of the temperature according to the LCZs of each method, correlating to the land surface temperature (LST) from a Landsat 8 image pertaining to a heat wave episode that occurred in Oslo in 2018. These results show, in addition to a clear LCZ-LST correspondence, that the three methods produce accurate and similar results and are all viable options.
Ayudas:	European Commission 818002 Ministerio de Ciencia e Innovación CEX2019-000940-M
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Local Climate Zones (LCZs) ; Climatology ; Geographical information systems (GISs) ; Land surface temperature (LST) ; World Urban Database and Access Portal Tools (WUDAPT) ; Google Earth Engine (GEE) ; Urban land use
Publicado en:	Climate, Vol. 10, Issue 7 (July 2022) , art. 109, ISSN 2225-1154

DOI: 10.3390/cli10070109

20 p, 14.6 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > Institut de Ciència i Tecnologia Ambientals (ICTA) > Sostenibilidad y Prevención Ambiental (Sostenipra)
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