LCZ Generator: A Web Application to Create Local Climate Zone Maps

Demuzere, Matthias; Kittner, Jonas; Bechtel, Benjamin

doi:10.3389/fenvs.2021.637455

Cited by 156 publications

(79 citation statements)

References 77 publications

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“…This includes digital cadastre data, remotely received LULC and LST data, orthophotos, DEM/DTM data and building geometry (via LiDAR-and SAR-based technologies), data from ground stations for T a , and freely available data from OpenStreetMap, Google Earth Engine, Google Street View, etc. Primary methods for LCZ mapping include in situ measurements, geographic information system (GIS)-based and remote-sensingimage-based calculations (the WUDAPT method), or a combination of them, which are presented in detail in many publications [48][49][50][51][52][53][54][55][56]. To improve the accuracy, some scientists offer an alternative GIS-based workflow to translate Copernicus datasets from Urban Atlas and Corine Land Cover shapefiles and Raster GeoTiff into LCZ maps, which, however, limits the possibilities for practical application of the method outside major European cities [57].…”

Section: Introductionmentioning

confidence: 99%

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

2021

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This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 ´ 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 PM and 10:00 PM) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities.

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Section: Introductionmentioning

confidence: 99%

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

2021

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“…One of the main advantages of the LCZ Generator is that the random forest classifier is fed with 33 Earth observation input features, whereas the default WUDAPT uses only Landsat-8 data. Studies have shown that multi-input features improve LCZ classification [20]. For instance, the WUDAPT method tends to misclassify built-type LCZ classes in arid climate cities, even when multitemporal and multispectral remotely sensed data are used [1].…”

Section: Lcz Classificationmentioning

confidence: 99%

“…However, global demands for more inputs to WUDAPT and verification of the framework's applicability are still growing [1,17]. Nevertheless, WUDAPT involves three time-consuming steps: obtaining data, pre-processing, and accuracy assessment [20]. Recently, Demuzere et al [20] developed an LCZ Generator tool that addresses these issues as it requires only a training sample and its reference date.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, WUDAPT involves three time-consuming steps: obtaining data, pre-processing, and accuracy assessment [20]. Recently, Demuzere et al [20] developed an LCZ Generator tool that addresses these issues as it requires only a training sample and its reference date. The LCZ Generator is a new online platform that implements the WUDAPT framework in a reasonable time frame by using Google Earth's engine computing environment to perform random forest classification and automated accuracy assessment.…”

Section: Introductionmentioning

confidence: 99%

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Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

2021

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Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies.

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“…• Urban Multi-Scale Environment Predictor (Lindberg et al, 2019), available as a plugin in the free and open-source QGIS software, can be used for a variety of applications related to outdoor thermal comfort, urban energy consumption, climate change mitigation (Lindberg et al, 2018) • Local Climate Zone Generator (LCZ Generator 4 ), available as an online tool, produces the LCZ classification of a given area (Demuzere et al, 2021).…”

Section: State Of the Field And Features Comparisonmentioning

confidence: 99%

GeoClimate: a Geospatial processing toolbox for environmental and climate studies

Bocher¹,

Bernard²,

Wiederhold³

et al. 2021

JOSS

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Human activities induce changes on land use and land cover. These changes are most significant in urban areas where topographic features (e.g., building, road) affect the density of impervious surface areas and introduce a range of urban morphological patterns. Those characteristics impact the energy balance and modify the climate locally (e.g., inducing the so-called Urban Heat Island phenomenon).

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LCZ Generator: A Web Application to Create Local Climate Zone Maps

Cited by 156 publications

References 77 publications

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

GeoClimate: a Geospatial processing toolbox for environmental and climate studies

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