ad8be24bc9029577f026f5b988b4bc06 pdf_exploring_methods_for_developing_local_climate_zones.pdf a721002b850a415a1a1666a77a1b4f8f8995d414 pdf_exploring_methods_for_developing_local_climate_zones.pdf fb37d56b7266e3970dc639535f655eeb3aef5fbdf1464472f38410c96567d0af pdf_exploring_methods_for_developing_local_climate_zones.pdf Title: Exploring Methods for Developing Local Climate Zones to Support Climate Research Subject: 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. Keywords: 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 Author: Laurence Sigler, Joan Gilabert and Gara Villalba Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Jul 20 09:23:20 2022 CEST ModDate: Wed Jul 20 10:10:49 2022 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 20 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 15288345 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WPRRDK+VnURWPalladioL Type 1 Custom yes yes no 10 0 FXBYOY+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 RUTFBJ+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 QAIWMF+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 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no 310 0 PDIBDL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 324 0 PDIBDN+PalatinoLinotype TrueType WinAnsi yes yes no 327 0 PDIBDP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 330 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-09-28 02:37:02 CEST RepresentationInformation: pdf_exploring_methods_for_developing_local_climate_zones.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-09-23 09:55:27 CEST Size: 15288345 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 495 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4923ab24 Item: Title: Case Study Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Children: Item: Title: Creation of LCZ via the WUDAPT Method Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Creation of LCZ via the Urban Atlas Method (GIS Client) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Creation of LCZ via the Google Earth Engine Method Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: How Accurate Are the Various Methods of Creating the LCZ in Assessing the Influence of Urban Fabric on Surface Temperature? Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Discussion: Advantages and Disadvantages of the Three Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Info: Title: Exploring Methods for Developing Local Climate Zones to Support Climate Research Author: Laurence Sigler, Joan Gilabert and Gara Villalba Subject: 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. Keywords: 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 Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Jul 20 09:23:20 CEST 2022 ModDate: Wed Jul 20 10:10:49 CEST 2022 ID: 0x7d25b0fea34bea6bc53fa4c19b27a4b4, 0x7d25b0fea34bea6bc53fa4c19b27a4b4 Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: AAPCWM+CMSY10 FontSubset: true FirstChar: 0 LastChar: 48 FontDescriptor: FontName: AAPCWM+CMSY10 Flags: Symbolic FontBBox: -29, -960, 1116, 775 FontFile: true Font: BaseFont: RUTFBJ+URWPalladioL-Roma FontSubset: true FirstChar: 2 LastChar: 246 FontDescriptor: FontName: RUTFBJ+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: QAIWMF+URWPalladioL-Ital FontSubset: true FirstChar: 40 LastChar: 122 FontDescriptor: 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