GIS-based delineation of local climate zones: The case of medium-sized Central European cities

Geletič, Jan; Lehnert, Michal

doi:10.1515/mgr-2016-0012

Cited by 80 publications

(73 citation statements)

References 20 publications

(47 reference statements)

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“…However, when interpreting the thermal behavior of LCZ 10, it must be taken into account that LCZ 10 delineation was based on the Geletič and Lehnert [30] method, which employed a higher percentage of ISF and BSF for delineation of LCZ 10 than that originally suggested by Stewart and Oke [4]. Nevertheless, the higher land surface temperatures of industrial areas in daytime have previously been described by many authors, e.g., [28,36,37].…”

Section: Discussionmentioning

confidence: 99%

“…A GIS-based approach was taken for the delimitation of LCZs [30]. This method had already been developed and tested in the area of Brno and its surroundings.…”

Section: Local Climate Zonesmentioning

confidence: 99%

“…In the second step, the cells in the first step categorized as building types (BSF ≥ 10%) were classified into particular LCZs on the basis of the smallest deviation from the optimum interval, as defined by Geletič and Lehnert [30] (against the background of values suggested by Stewart and Oke [4]; see Table 2) in terms of physical parameters (BSF, PSF, ISF and HRE). Industrial zones (LCZs 8 and 10) were refined using NoB.…”

Section: Local Climate Zonesmentioning

confidence: 99%

“…Industrial zones (LCZs 8 and 10) were refined using NoB. Cells that had been categorized as land-cover types in the first step were automatically classified into particular LCZs in the second step by means of the vector land use geodatabase [30]. When all cells had been assigned to an appropriate LCZ, the LCZ areas were delineated using a majority filter to smooth the results of classification.…”

Section: Local Climate Zonesmentioning

confidence: 99%

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Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

2016

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Abstract:The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the roles played by particular factors vary from city to city with respect to differences in geographical location, overall size, number of inhabitants and more. In urban climatology, the concept of "local climate zones" (LCZs) has emerged over the past decade to address this heterogeneity. In this contribution, a new GIS-based method is used for LCZ delimitation in Prague and Brno, the two largest cities in the Czech Republic, while land surface temperatures (LSTs) derived from LANDSAT and ASTER satellite data are employed for exploring the extent to which LCZ classes discriminate with respect to LSTs. It has been suggested that correctly-delineated LCZs should demonstrate the features typical of LST variability, and thus, typical surface temperatures should differ significantly among most LCZs. Zones representing heavy industry (LCZ 10), dense low-rise buildings (LCZ 3) and compact mid-rise buildings (LCZ 2) were identified as the warmest in both cities, while bodies of water (LCZ G) and densely-forested areas (LCZ A) made up the coolest zones. ANOVA and subsequent multiple comparison tests demonstrated that significant temperature differences between the various LCZs prevail. The results of testing were similar for both study areas (89.3% and 91.7% significant LST differences for Brno and Prague, respectively). LSTs computed from LANDSAT differentiated better between LCZs, compared with ASTER. LCZ 8 (large low-rise buildings), LCZ 10 (heavy industry) and LCZ D (low plants) are well-differentiated zones in terms of their surface temperatures. In contrast, LCZ 2 (compact mid-rise), LCZ 4 (open high-rise) and LCZ 9 (sparsely built-up) are less distinguishable in both areas analyzed. Factors such as seasonality and thermal anisotropy remain a challenge for future research into LST differences.

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Section: Discussionmentioning

confidence: 99%

“…A GIS-based approach was taken for the delimitation of LCZs [30]. This method had already been developed and tested in the area of Brno and its surroundings.…”

Section: Local Climate Zonesmentioning

confidence: 99%

Section: Local Climate Zonesmentioning

confidence: 99%

Section: Local Climate Zonesmentioning

confidence: 99%

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Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

2016

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“…Several methods for creating LCZ maps have been proposed, including supervised pixel-based classification from multiple Earth Observation (EO) data streams [23], model-based GIS approaches [24][25][26], and object-based image analysis [27,28]. For WUDAPT, it was decided that a simple and efficient computing workflow based on free software and data was needed.…”

Section: Introductionmentioning

confidence: 99%

Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)

et al. 2017

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Abstract:The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets.

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How well does the local climate zone scheme discern the thermal environment of Toulouse (France)? An analysis using numerical simulation data

Kwok

Schoetter

Lau

et al. 2019

Intl Journal of Climatology

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To build healthy, resilient, and climate‐responsive cities, planners need ways to understand the local complexities of urban thermal climates. To assist in meeting this need, this study employs the simple classification of “local climate zones” (LCZs) to conduct a spatiotemporal thermal climatic analysis of the Toulouse Metropolitan Region (France) under warm and dry summer conditions. Simulations are performed using the mesoscale atmospheric model Méso‐NH. These simulations provide a city‐wide spatial coverage of 2‐m air temperature (T2M), mean radiant temperature (MRT), and Universal Thermal Climate Index (UTCI). Model parameters describing the urban morphology are initialized based on administrative databases and independent of LCZ maps, which allows for an evaluation of whether the distributions of the modelled thermal climatic parameters will differ between LCZs. The results show that different LCZs possess significantly different distributions of T2M and MRT, confirming the suitability of the LCZ scheme for discerning the thermal environment of Toulouse. Compact urban settings (LCZ 1/2/3) show the highest T2M throughout the day and a nocturnal temperature difference of up to 2.8 K compared to rural settings. The MRT of LCZ 1/2/3 in the late afternoon (1700–2000 LST (UTC + 2)) can be as much as 6.3 K lower than it is for LCZs with open settings due to shading by dense urban structures. Additional analysis reveals that the intra‐LCZ variabilities of T2M and MRT may be explained by the distance to the city centre. Finally, the thermal stress in different LCZs is assessed with the modelled UTCI. Among the built LCZs, the probability of strong heat stress is the highest for open high/mid‐rise (LCZ 4/5) and lowest for sparsely built (LCZ 9) and open low‐rise (LCZ 6) settings. For land cover type LCZs, dense trees (LCZ A) are the most favourable for daytime outdoor human thermal comfort.

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GIS-based delineation of local climate zones: The case of medium-sized Central European cities

Cited by 80 publications

References 20 publications

Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)

How well does the local climate zone scheme discern the thermal environment of Toulouse (France)? An analysis using numerical simulation data

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