Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Moisa, Mitiku Badasa; Gemeda, Dessalegn Obsi

doi:10.1016/j.heliyon.2022.e10185

Cited by 15 publications

(4 citation statements)

References 72 publications

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“…Furthermore, brightness temperature and emissivity are used to obtain LST with an algorithm like that done by (Sobrino, Jiménez-Muñoz, & Paolini, 2004). Thermal comfort can be identified by UTFVI (Moisa & Gemeda, 2022). In order to determine the thermal comfort of an area, UTFVI calculations are performed using Equation 1 (Singh, Kikon, & Verma, 2017).…”

Section: Thermal Comfort Extractionmentioning

confidence: 99%

“…LST is the basis for determining thermal comfort with the UTFVI algorithm (Moisa & Gemeda, 2022). SPL that has been extracted from Landsat 9 imagery is presented in Figure 4.…”

Section: Mapping the Distribution Of Lst And Thermal Comfortmentioning

confidence: 99%

“…This results from LULC conversions, which frequently do not follow the principles of sustainable development (Asmiwyati, 2016). Look Moisa (2022). Rapid urbanization transforms natural surfaces into built-up areas, which has a significant impact on the thermal conditions of cities (Qu et al, 2020;Sadiq Khan, Ullah, Sun, Rehman, & Chen, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Effect of Vegetation and Building Densities to Urban Thermal Comfort (Case Study of Denpasar City)

Hadibasyir,

Firdaus

2023

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Denpasar is the capital of Bali Province, which is one of the most popular tourist destination in Indonesia. Anthropogenic activities have changed the land use and land cover (LULC) that affect the land surface temperature (LST) and thermal comfort. Good quality of thermal comfort can improve the livelihood of its citizen as well as the tourists. This research aims a) to analyses the density distribution of vegetation and buildings, b) to map the distribution of LST and thermal comfort, and c) to analyses the effect of vegetation and building density on thermal comfort in Denpasar City. LST and thermal comfort were extracted using Landsat 9. Thermal comfort was assessed using urban thermal field variance index (UTFVI). Then, Pearson correlation and simple regression were applied to investigate the relationship and effect of Effect of vegetation and building densities to urban thermal comfort. In general, high density of vegetation is located in the northern part of North Denpasar District, north and east of East Denpasar District, and south of South Denpasar District. High density buildings are scattered in the southern Denpasar District, the western eastern Denpasar District, the northern South Denpasar District, and parts of the West Denpasar District. Low UTFVI values indicate good thermal comfort. Greater vegetation and lower building densities are likely to have an impact by improving thermal comfort levels in a location. This research can be served as a guidance in urban heat island mitigation as well as ecological restoration planning.

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Section: Thermal Comfort Extractionmentioning

confidence: 99%

“…LST is the basis for determining thermal comfort with the UTFVI algorithm (Moisa & Gemeda, 2022). SPL that has been extracted from Landsat 9 imagery is presented in Figure 4.…”

Section: Mapping the Distribution Of Lst And Thermal Comfortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Vegetation and Building Densities to Urban Thermal Comfort (Case Study of Denpasar City)

Hadibasyir,

Firdaus

2023

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“…Njoku and Tenenbaum studied the relationship between LULC and LST in Ilorin, Nigeria, and found that LST values exhibited considerable spatial and temporal variation, with the high temperature clusters evident in the built-up areas, with a difference in mean LST of 4.4 • C between built-up land and green space [62]. Moisa and Gemeda assessed the urban thermal field variance of Addis Ababa metropolitan city, Ethiopia, and found that the highest LST was recorded in low-vegetation areas, particularly on built-up areas, cropland, and bare land [63]. At the same time, Song et al found a 3.4 • C difference between the average LSTs for built-up land and other land use types, including cropland, grassland, and forestland, using Landsat ETM+ and Quick Bird data in a SUHI study in Beijing, China [58].…”

Section: Analysis Of the Relationship Between Lulc And Lstmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Ma,

Mamitimin,

Tiemuerbieke

et al. 2023

Land

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Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.

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The Impact of Industrial Activities on the Land Surface Temperature (LST) in the Urban Environment of the City of Kenitra (Morocco)

Nasraoui,

Benzidiya

2024

Springer Climate

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Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Cited by 15 publications

References 72 publications

Effect of Vegetation and Building Densities to Urban Thermal Comfort (Case Study of Denpasar City)

Effect of Vegetation and Building Densities to Urban Thermal Comfort (Case Study of Denpasar City)

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

The Impact of Industrial Activities on the Land Surface Temperature (LST) in the Urban Environment of the City of Kenitra (Morocco)

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