Journal Article10.1109/JSTARS.2012.2189873
Classification of Local Climate Zones Based on Multiple Earth Observation Data
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TL;DR: In this study Local Climate Zones, a system of thermally homogenous urban structures introduced by Stewart and Oke, was used in a pixel-based classification approach and seemed to yield considerable potential for an automated classification of LCZ.
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Abstract: Considerable progress was recently made in the determination of urban morphologies or structural types from different Earth observation (EO) datasets. A relevant field of application for such methods is urban climatology, since specific urban morphologies produce distinct microclimates. However, application and comparability are so far limited by the variety of typologies used for the description of urban surfaces in EO. In this study Local Climate Zones (LCZ), a system of thermally homogenous urban structures introduced by Stewart and Oke, was used in a pixel-based classification approach. Further, different EO datasets (including satellite multitemporal thermal and multispectral data as well as a normalized digital surface model (NDSM) from airborne Interferometric Synthetic Aperture Radar) and different classifiers (including Support Vector Machines, Neural Networks and Random Forest) were evaluated for their performance in a common framework. Especially the multitemporal thermal and spectral features yielded high potential for the discrimination of LCZ, but morphological profiles from the NDSM also performed well. Further, sets of 10-100 features were selected with the Minimum Redundancy Maximal Relevance approach from multiple EO data. Overall classification accuracies of up to 97.4% and 95.3% were obtained with a Neural Network and a Random Forest classifier respectively. This provides some evidence that LCZ can be derived from multiple EO data. Hence, we propose the typology and the method for the application of automated extraction of urban structures in urban climatology. Further the chosen multiple EO data and classifiers seemed to yield considerable potential for an automated classification of LCZ.
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Citations
Using Local Climate Zones to investigate Spatio-temporal evolution of thermal environment at the urban regional level: A case study in Xi'an, China
TL;DR: Li et al. as mentioned in this paper selected the Xi'an urban spatial agglomeration and used remote sensing images from 2008, 2013, and 2019 to determine the spatial and temporal variations in the thermal environment for statistical analysis and contrast.
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Investigating urban heat-related health risks based on local climate zones: A case study of Changzhou in China
Lei Ma,Guoan Huang,Brian Alan Johnson,Zhenjie Chen,Manchun Li,Ziyu Yan,Wenfeng Zhan,Heng Lu,Weiqiang He,Dongjie Lian +9 more
TL;DR: Li et al. as discussed by the authors proposed an LCZ-based risk assessment approach for assessing heat-related health risks, which can be used for informing and implementing area-level urban planning strategies.
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Mapping Local Climate Zones Using ArcGIS-Based Method and Exploring Land Surface Temperature Characteristics in Chenzhou, China
TL;DR: In this article, eight urban spatial form elements and land cover elements are calculated through ArcGIS, Skyhelios and ENVI software. And then, the land surface temperature (LST) of different local climate zones in the four seasons from 2017 to 2018 is further analyzed using one-way ANOVA F-test and Student's t-test.
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Enhanced geographic information system-based mapping of local climate zones in Beijing, China
TL;DR: Li et al. as mentioned in this paper proposed an enhanced geographic information system-based workflow to enable the hierarchical classification of LCZs with fewer indicators but higher accuracies while considering supplementary classes and subclasses.
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Land consumption in cities: A comparative study across the globe
Jingliang Hu,Yuanyuan Wang,Hannes Taubenböck,Hannes Taubenböck,Xiao Xiang Zhu,Xiao Xiang Zhu +5 more
TL;DR: In this paper, the authors developed a classification system that consistently produces accurate local climate zone (LCZ) maps at intra-urban scale for 40 cities using Sentinel data, and used the LCZ classes as proxies to disaggregate the global population grids (GHS-POP) to this intraurban scale.
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