{"id":1846,"date":"2022-07-19T10:52:33","date_gmt":"2022-07-19T08:52:33","guid":{"rendered":"https:\/\/urbag.eu\/?p=1846"},"modified":"2022-07-19T10:52:33","modified_gmt":"2022-07-19T08:52:33","slug":"publication-in-climate","status":"publish","type":"post","link":"https:\/\/webs.uab.cat\/atmosphere\/2022\/07\/19\/publication-in-climate\/","title":{"rendered":"New publication in “Climate”"},"content":{"rendered":"

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See full article\/ Veure l’article complet<\/strong><\/a><\/p>\n

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English:<\/strong><\/p>\n

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\u2019s 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.<\/p>\n

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Catal\u00e0:<\/strong><\/p>\n

Els models meteorol\u00f2gics i de predicci\u00f3 clim\u00e0tica a escala urbana requereixen cada vegada dades m\u00e9s precises i d’alta resoluci\u00f3. Les Zones Clim\u00e0tiques Locals (LCZ) s\u00f3n una iniciativa per estandarditzar un esquema de classificaci\u00f3 del paisatge urb\u00e0, basat principalment en les propietats de l’estructura superficial (per exemple, l’edificaci\u00f3, l’al\u00e7ada dels arbres, la densitat) i la coberta superficial (permeable o impermeable). Aquest enfocament \u00e9s especialment \u00fatil per estudiar la influ\u00e8ncia de la morfologia urbana i el teixit en l’efecte de l’illa de calor urbana superficial (SUHI) i per avaluar com els canvis en l’\u00fas del s\u00f2l i les estructures afecten la regulaci\u00f3 t\u00e8rmica a la ciutat. Aquest article demostrar\u00e0 tres metodologies diferents per crear LCZ: en primer lloc, la World Urban Database and Access Portal Tools (WUDAPT); en segon lloc, utilitzant directament les dades de Copernicus Urban Atlas (UA) a trav\u00e9s d’un client del sistema d’informaci\u00f3 geogr\u00e0fica (GIS); i tercer, a trav\u00e9s de Google Earth Engine (GEE) utilitzant Oslo, Noruega com a cas d’estudi. Els m\u00e8todes WUDAPT i GEE incorporen un procediment de \u201cmachine learning\u201d (bosc aleatori) utilitzant imatges Landsat 8, i ofereixen la major precisi\u00f3, alhora que requereixen el major temps i familiaritat amb l’\u00fas de GIS i el processament d’imatges de sat\u00e8l\u00b7lit. El m\u00e8tode WUDAPT es realitza principalment utilitzant m\u00faltiples clients GIS i eines de processament d’imatges. El m\u00e8tode GEE \u00e9s una mica m\u00e9s r\u00e0pid de realitzar, amb treballs generats \u00edntegrament en els entorns de Google. El m\u00e8tode UA o GIS es realitza \u00fanicament a trav\u00e9s d’un client GIS i \u00e9s una conversi\u00f3 de dades vectorials preexistents a classes LCZ mitjan\u00e7ant scripting. Aquest \u00e9s el m\u00e8tode m\u00e9s r\u00e0pid dels tres; no obstant aix\u00f2, la reclassificaci\u00f3 de les dades vectorials determina la precisi\u00f3 de les LCZ produ\u00efdes. Finalment, com a il\u00b7lustraci\u00f3 d’un \u00fas pr\u00e0ctic de LCZ i per comparar encara m\u00e9s els resultats dels tres m\u00e8todes, mapegem la distribuci\u00f3 de la temperatura segons les LCZ de cada m\u00e8tode, correlacionant-se amb la temperatura de la superf\u00edcie terrestre (LST) d’una imatge de Landsat 8 pertanyent a un episodi d’onada de calor que va tenir lloc a Oslo el 2018. Aquests resultats mostren, a m\u00e9s d’una clara correspond\u00e8ncia LCZ-LST, que els tres m\u00e8todes produeixen resultats precisos i similars i s\u00f3n opcions viables.<\/p>\n

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Figure:<\/strong> The LCZs resulting from the three methods: WUDAPT (a), UA\/GIS (b), and Google Earth Engine (c), with the LCZ type classification in the legend on the left. \/ LCZ resultants dels tres m\u00e8todes: WUDAPT (a), UA\/GIS (b) i Google Earth Engine (c), amb la classificaci\u00f3 de LCZ a la llegenda de l’esquerra.<\/p>\n","protected":false},"excerpt":{"rendered":"

Title: Exploring Methods for Developing Local Climate Zones to Support Climate Research \/ <\/p>\n

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).<\/p>\n","protected":false},"author":3153,"featured_media":1853,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[148,127,149,40,150,128,52,151,152,153,10,154,55],"class_list":["post-1846","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general","tag-gee","tag-gis","tag-google-earth-engine","tag-lcz","tag-lst","tag-mapping","tag-oslo","tag-suhi","tag-ua","tag-uhi","tag-urbag","tag-urban-atlas","tag-wudapt"],"_links":{"self":[{"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/posts\/1846","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/users\/3153"}],"replies":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/comments?post=1846"}],"version-history":[{"count":0,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/posts\/1846\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/media\/1853"}],"wp:attachment":[{"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/media?parent=1846"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/categories?post=1846"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/webs.uab.cat\/atmosphere\/wp-json\/wp\/v2\/tags?post=1846"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}