Thermophysical behaviour of LULC surfaces and their effect on the urban thermal environment

Hamoodi, Mustafa N.; Corner, Robert; Dewan, Ashraf

doi:10.1080/14498596.2017.1386598

Cited by 39 publications

(24 citation statements)

References 34 publications

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“…In addition, the density of vegetation can reduce the heat of built-up surfaces via the shade provided, and also enhance energy flow and exchange between trees and their surrounding areas ( Zhou et al., 2017 ). Our results corresponded with previous studies, including one conducted in Western Australia in the central Perth metropolitan area by Hamoodi et al. (2017) .…”

Section: Resultssupporting

confidence: 92%

Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand

Adulkongkaew

Satapanajaru

Charoenhirunyingyos

et al. 2020

Heliyon

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The Bangkok Metropolitan Area is an example of urban sprawl that has undergone rapid expansion and major changes in urban composition and building configuration. This city is now faced with the urban heat island phenomenon. Initial observations of land surface temperature (LST) in recent years have indicated that LST has tended to increase in both urban and suburban areas. The purposes of this study were to: (1) assess different land cover types and combinations of land cover composition along an LST gradient, and (2) investigate effect of building configuration types on the LST in densely urban areas. We analyzed the urban composition variation of 4,960 land cover samples using a 500 m × 500 m grid and configuration metrics in spatial patterns from Landsat 8 data and a high-resolution database of buildings obtained from GIS data of the Bangkok Metropolitan Area. The results indicated that the fraction of land cover composition was strongly related to LST. Our results suggested that LST can be effectively mitigated by using below green (shrubs, grasses, and yards), above green (trees, orchards, mangroves, and perennial plants) and water land cover. By increasing tree canopy to around 20%, water body to around 30% or green yard/shrub to around 40% of the built-up areas, it is possible to reduce LST significantly. Urban configurations (edge density, patch density, large patch, mean patch size, building height, compactness of building, building type, and building use) affecting on LST were studied. Increased edge density, patch density of buildings, and building height caused reductions in LST. Distribution of LST patterns can be significantly related with urban composition or land configuration features. The results of this study can increase understanding of the interaction between urban composition and configuration metrics. Moreover, our findings may be useful in the mitigation of the impact of LST in urban-sprawl cities.

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Section: Resultssupporting

confidence: 92%

Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand

Adulkongkaew

Satapanajaru

Charoenhirunyingyos

et al. 2020

Heliyon

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“…cal TOA reflectance was computed from Landsat digital number(DN) data. ρλ′ is TOA planetary reflectance, M ρ is band-specific multiplicative rescaling factor, Q cal is quantized and calibrated standard product pixel values, A ρ is band-specific additive rescaling factor 54 . concept of UHi-hours.…”

Section: Methodsmentioning

confidence: 99%

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Ngarambe

Duhirwe

et al. 2020

Sci Rep

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Urban heat island (UHi), a phenomenon involving increased air temperature of a city compared to the surrounding rural area, results in increased energy use and escalated health problems. to understand the magnitude and characteristics of UHi in Seoul and to accommodate for the high temporal variability and spatial heterogeneity of the UHi which make it inherently challenging to analyze using conventional statistical methods, we developed two deep learning models, a temporal UHi-model and a spatial UHi model, using a feed-forward deep neural network (Dnn) architecture. Data related to meteorological elements (e.g. air temperature) and urban texture (e.g. surface albedo) were used to train and test the temporal UHi-model and the Spatial UHi-model respectively. Also, we develop and propose a new metric, UHI-hours, that quantifies the total number of hours that UHI exists in a given area. our results show that UHi-hours is a better indicator of seasonal UHi than the commonly used index, UHi-intensity. consequently, UHi-hours is likely to provide a better measure of the cumulative effects of UHI over time than UHI-intensity. UHI-hours will help us to better quantify the effect of UHI on, for example, the overall daily productivity of outdoor workers or heat-related mortality rates.The world's population is increasing dramatically; new cities are being built, while existing cities are getting overpopulated. Currently, more than 50% of the world population resides in cities 1 . This increased development in urban areas has resulted in changes in urban morphology and surfaces, as well as an increase in the amount of anthropogenic heat released to the atmosphere 2 . The combined effect of such changes leads to higher air temperature in urban areas than in the surrounding rural or suburban areas, hence the formation of heat islands 3 . These heat islands, especially in summers, considerably decrease the outdoor air quality and trigger heat-related diseases and deaths in urban areas 4,5 . Moreover, extreme temperatures have been reported to be the leading cause of weather-related deaths 6 . This is particularly prevalent among the elderly (people of 65 years of age and above) and those with cardiovascular and respiratory health issues. For example, Paravantis et al. 7 analyzed the impact of temperature and heat waves on the number of deaths caused by cardiovascular and respiratory health issues in people above 65 years of age in Athens, Greece. They reported a U-shaped exposure-response curve, indicating reduced mortality rates at moderate temperatures and 20% and 35% increase in mortality rates at very low and very high temperatures, respectively. Furthermore, due to the increase in urban temperatures, there has been an increase in air-conditioning system usage, which in turn has led to high electricity demands and overall increased building energy consumption 8 . At the same time, passive cooling methods, such as natural and night ventilation, have become ineffective for the thermal comfort of building occupants 9 .Due to...

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“…The brightness temperature was subsequently converted to LST using Eq. (7) (Hamoodi et al 2019, Ullah et al 2019:…”

Section: Conversion Of Brightness Temperature To Lstmentioning

confidence: 99%

“…Statistical analyses are commonly applied to establish relationships between parameters, for example, Land cover-LST (Dewan, Corner 2012), LST-NDVI (Ferrelli et al 2018, Hamoodi et al 2019, Malik et al 2019, Ullah et al 2019, Guha et al 2020, Mukherjee, Singh 2020, NDVI-EVI (Chen et al 2006b, Matsushita et al 2007, Li et al 2010, Uyeda et al 2017 and LST-EVI (Phompila et al 2015). In this study, an inventory of the parameters was created for the study area.…”

Section: Relationships Change Detection and Trend Analysismentioning

confidence: 99%

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Alademomi

Okolie

Daramola

et al. 2020

Quaestiones Geographicae

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The Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

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Thermophysical behaviour of LULC surfaces and their effect on the urban thermal environment

Cited by 39 publications

References 34 publications

Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand

Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

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