Estimating the effect of park proximity to the central of Melbourne city on Urban Heat Island (UHI) relative to Land Surface Temperature (LST)

Algretawee, Hayder; Rayburg, Scott; Neave, Melissa

doi:10.1016/j.ecoleng.2019.07.034

Cited by 69 publications

(29 citation statements)

References 24 publications

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“…These heat trapping impervious surfaces directly change the local climate in urban areas (Arathyram and Venugopala, 2012). Materials used in the walls of buildings and streets have high heat rates that store the heat throughout the daytime and emit heat during night-time (Algretawee et al, 2019).…”

Section: The Local Climate Zones (Lcz) Schemementioning

confidence: 99%

Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

Estacio

Babaan

Pecson

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Because of the vague distinction between urban and rural areas, the Local Climate Zone (LCZ) scheme was developed to better analyze the effect of Urban Heat Island. To map the LCZs in a city, the World Urban Database and Portal Tool is used as conventional method. However, this requires the assignment of training areas for each LCZ, which entails local knowledge of the area and may introduce errors, as distinction between LCZ types through visual inspection is inconclusive. This paper aims to develop a methodology and GIS tool to enhance and automate the mapping of LCZs using seven LCZ properties (sky view factor, building surface fraction, pervious surface fraction, impervious surface fraction, building height, roughness length, and surface albedo), and apply it in Quezon City, Philippines which comprises varying land use and land cover. Fuzzy Logic was used to determine the membership percentage of 100 m cells to an LCZ type based on these properties. Cellular Automata was implemented using Python to derive the LCZ map from the fuzzy layers. Results show that seven out of ten built-up LCZs and five out of seven land cover LCZs were identified. Through visual inspection on a basemap, the mapped LCZs was confirmed to match with the features of the city. Land Surface Temperature (LST) derived from Landsat 8 showed that each LCZ type displayed temperatures consistent with those observed from literature. The developed methodology and tool is ready to be used in other cities as long as the input layers are generated.

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Section: The Local Climate Zones (Lcz) Schemementioning

confidence: 99%

Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

Estacio

Babaan

Pecson

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Nurwanda [36] used Landsat 8 images to predict urban expansion and LST in Bogor City (Indonesia). These studies concerned the underlying structure of the city, especially the degree of weakening of the UHI by the allocation of urban green space [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

“…The distribution of LST varies seasonally and is not only affected by land use/land cover but also by other driving factors, such as altitude, Gross Domestic Product (GDP) level, and population density [41][42][43]. However, in current studies, the driving forces of LST distribution are rarely explored comprehensively from the combined perspective of physical and socio-economic factors and interaction between driving factors [32,[37][38][39][40]. Establishing a comprehensive driving factor system, determining the leading influencing factors of LST, and clarifying the action mechanisms of the driving factors have important scientific research significance and application value.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

et al. 2021

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Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

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“…The phenomenon, i.e., higher air or surface temperature of urban areas in relation to their surroundings was identified for the first time by Howard in the early 19th Century on the example of the City of London [2]. The reasons for this condition are due to urban surfaces being usually darker than those in the suburban and rural areas (low albedo and reflected sunlight), vegetation cover that in urban areas is typically less than those of surrounding areas, construction materials of buildings, pavements and other urban structures that tend to have high heat rate and store the heat through day hours and emit it during the night, urban morphology which affects shading and air movement, and ever-increasing rates of energy consumption [3][4][5]. The UHI effect has been aggravated in cities worldwide by climate change and increasing average global temperatures [6,7], as well as by rapid urbanisation and deforestation [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…This is because plants reduce impervious surfaces that absorb sunlight and provide shadow, as well as stimulate evapotranspiration that, in combination with shading, can help to reduce high temperatures [23,24]. Vegetation in urban areas can be classified into three main types: street trees, green roofs and walls, and urban parks [5]. The latter urban park is considered to be the most effective way to mitigate the UHI, because a park constitutes an area that combines many types of vegetation including forests, unpaved surfaces and water bodies.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland)

Blachowski

Hajnrych

2021

Forests

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Urban parks have been known to form park cooling islands (PCI), which can effectively alleviate the effect of urban heat islands (UHI) in cities. This paper presents results obtained for four different size parks in the city of Wroclaw, which is located in a temperate continental climate. The number of publications for urban areas located in this type of climate and cities is low compared to sites in hot and humid areas. Land surface temperature (LST) maps were developed from Landsat 8 Thermal Infrared Sensor (TIRS) data acquired during three hottest weather periods between 2017 and 2019. Metrics and spatial statistics characterising the four parks selected for the analysis based on their size were calculated. These included: perimeter, area, landscape shape index (LSI) and PLC (forest area) park metrics, and Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Gradient (PCG), Park Cooling Island (PCI) and Extended Park Cooling Island (PCIe) spatial indexes. The averaged PCIe values ranged from 2.0 to 3.6 °C, PCI from 1.9 to 3.6 °C, PCG from 0.7 to 2.2 °C, PCE from 5.3 to 11.5, and PCA from 78.8 to 691.8 ha depending on the park. The cooling distance varied from 110 m to 925 m depending on park size, forest area and land use type in the park’s vicinity. The study provides new insight into urban park cooling effects in a medium sized city located in a temperate continental climate, and the role of parks in regulation of urban temperature to mitigate the UHI effect.

show abstract

Estimating the effect of park proximity to the central of Melbourne city on Urban Heat Island (UHI) relative to Land Surface Temperature (LST)

Cited by 69 publications

References 24 publications

Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland)

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