Linking local-climate-zones mapping to multi-resolution-analysis to deduce associative relations at intra-urban scales through an example of Metropolitan London

Mouzourides, Petros; Eleftheriou, Andreas; Kyprianou, Andreas; Ching, Jason; Neophytou, Marina

doi:10.1016/j.uclim.2019.100505

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…Compared with European and American cities [59] (Table 2), the overall accuracy of Chinese cities is low. This is because the urban population and economic development level of different regions differ greatly, resulting in different urban structures and forms [60][61][62][63][64][65], leading to differences in accuracy.…”

Section: Overall Accuracy Of Four Urban Agglomerationsmentioning

confidence: 92%

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

Wang

Zhao

Ma³

2023

Remote Sensing

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The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy.

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Section: Overall Accuracy Of Four Urban Agglomerationsmentioning

confidence: 92%

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

Wang

Zhao

Ma³

2023

Remote Sensing

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“…We find that there is no one-to-one correspondence between the identified neighbourhood types and LCZ classes, and that the mapping between them is not robust. Mouzourides et al (2019) similarly found that LCZ maps derived from morphometric indicators can be resolution dependent: they found 16 different LCZs in Greater London at a resolution of 100 m × 100 m, whereas at a resolution of 1.6 km × 1.6 km, the number of classes reduced to 6.…”

Section: Clustering Analysismentioning

confidence: 93%

Urban neighbourhood classification and multi-scale heterogeneity analysis of Greater London

Sützl

Reeuwijk

2022

Environment and Planning B: Urban Analytics and City Science

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We study the compositional and configurational heterogeneity of Greater London at the city- and neighbourhood-scale using Geographic Information System (GIS) data. Urban morphometric indicators are calculated including plan-area indices and fractal dimensions of land cover, frontal area index of buildings, evenness, and contagion. To distinguish between city-scale heterogeneity and neighbourhood-scale heterogeneity, the study area of 720 km2 is divided into 1 [Formula: see text] 1 km2 neighbourhoods. City-scale heterogeneity is represented by categorisation of the neighbourhoods using a k-means clustering algorithm based on the morphometric indicators. This results in six neighbourhood types ranging from “greenspace” to “central business district”. Neighbourhood-scale heterogeneity is quantified using a hierarchical multi-scale analysis for each neighbourhood type. The analysis reveals the dominant length scales for land-cover and neighbourhood types and the resolutions with the most information gain. We analyse multi-scale anisotropy and show that small-scale features are homogeneous, and that anisotropy is present at larger length scales.

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“…The simulations are done for week 1 and week 2 of the months of June and September, (as characteristic months for each season). Finally, the simulation results were compared with the LCZ and modified as per the proposed method [18].…”

Section: Envi-met Simulationsmentioning

confidence: 99%

Characterization of Local Climate Zones Using ENVI-met and Site Data in the City of Al-Ain, UAE

Bande¹,

Manandhar²,

Ghazal³

et al. 2020

IJSDP

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The rapid urbanization of the UAE, including medium sized cities like Al Ain City, has a significant relationship to local micro-climatic change. Al-Ain city in the southeast of the UAE and was originally an oasis. It has a hot and arid climate with a very dry and hot summer. The climate of the city is affected by the desert areas of red sand and the eastern Rocky Mountains. The local micro-climatic evolution can be studied and tracked using the local climate zone (LCZ) classification map. The districts of Al Ain are classified based on different factors, including surface cover and surface temperature, which were analysed using WUDAPT (World Urban Database and Access Portal Tools) software. The LCZ map is based on highresolution satellite images, which were used to classify regions based on building morphology and district pattern. The LCZ map results were compared with CFD (computational fluid dynamic) models that were simulated using ENVI-met software tool. The CFD models were optimized and validated based on on-site surveys and information taken from the local authorities, while the boundary conditions were validated using site measurements. Both models were analysed over the spring and summer seasons. Based on the results provided from WUDAPT and ENVI-met, a higher temperature was observed in the densest areas (downtown) and lower temperatures in the green zones (park, city date farms) and the result precision was higher in the colder season (autumn in this case).

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Linking local-climate-zones mapping to multi-resolution-analysis to deduce associative relations at intra-urban scales through an example of Metropolitan London

Cited by 11 publications

References 26 publications

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

Urban neighbourhood classification and multi-scale heterogeneity analysis of Greater London

Characterization of Local Climate Zones Using ENVI-met and Site Data in the City of Al-Ain, UAE

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