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
Understanding Land Surface Temperature Differences of Local Climate Zones Based on Airborne Remote Sensing Data
TL;DR: The present study has successfully validated the present airborne-based classification method which is contingent on further accuracy assessment and improvements, and examined the quality of classifications by analyzing the LST variability among LCZ using high-resolution airborne remote sensing data.
61
Improved simulation of very heavy rainfall events by incorporating WUDAPT urban land use/land cover in WRF
TL;DR: In this paper, the impact of incorporating the LCZ map in the Weather Research and Forecasting (WRF) model for simulating very heavy rainfall events over Mumbai is assessed.
60
Inter-local climate zone differentiation of land surface temperatures for Management of Urban Heat in Nairobi City, Kenya
Emmanuel Matsaba Ochola,Elham Fakharizadehshirazi,Aggrey Ochieng Adimo,John Bosco Mukundi,John Wesonga,Sahar Sodoudi +5 more
TL;DR: In this article, the authors presented results of inter-local climate zone (LCZ) differentiation of land surface temperature (LST) for the management of urban hotspots in Nairobi city.
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Urban atmospheric environment and human biometeorological studies in Dar es Salaam, Tanzania
TL;DR: In this article, a holistic approach was applied to review the past research in urban climate of Dar es Salaam, by considering the past researches in air pollution and thermal climate about the city.
56
Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: A case study of Austin, Texas, USA
TL;DR: In this paper, the authors focused on Austin, Texas, U.S., an area with the highest urbanization and the best ranked standard of living, to characterize its evolution of urban morphology and the transformations between urban morphology types (UMTs) over time.
56
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