Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery

Abstract

Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this work deals with the SUHI pattern identification within the different land use categories of Bangkok city plan. This study first provides an overview of the SUHI phenomenon in Bangkok, then singles out the surface heating behavior in each land use category. To describe the SUHI dynamics within the different classes, the main statistics of the SUHI intensity (mean, standard deviation, maximum and minimum) are computed. Overall, the analysis points out that the categories placed in the city core (high-density residential; commercial; historical and military classes) exhibit the highest mean SUHI intensities (around 4 °C); whilst the vegetated pixels exert a less cool effect with respect to the greenery of categories mainly placed farther from the city center. The proposed analysis can help to identify if the land use plan requires targeted future actions for the SUHI mitigation; or if the maintenance of the current urban development model is in line with the environmental sustainability.

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

“…For air-temperature measurements, fixed measurements are most often used. However, urban-monitoring networks are generally not dense enough to characterize the processes occurring on such a small scale [16]. Thus, mobile measurements can be used, operating by transect, though they are not continuous in time, contrary to a ground measurement network [17,18].…”

“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Evaluating the Spatial-Seasonal Variation, Heterogeneity and Distribution of Urban Thermal Environment: Case Study of Nanjing, China

“…The LST of study area retrieved, using Landsat 8 Band10, based on the Radiative Transfer Equation (RTE) algorithm(Garcí a-Santos et al, 2018; Voogt and Oke, 2003). The advantage of this algorithm is not only it needs less amount of parameters to retrieve LST comparing with other algorithm, such as monowindow algorithm and split-window algorithm, but also it was demonstrated that it has a high LST retrieve accuracy using Band10 of Landsat 8 than split window algorithm and single channel method (Darge et al, 2019;Keeratikasikorn and Bonafoni, 2018;Shirani-bidabadi et al, 2019;Xiaolei Yu, 2014). In the status of data pre-processing, The landsat 8 thermal band 10 have been converted to at-sensor radiance at the top-of-atmosphere ( ), next, Planck's radiance function was utilized to calculating brightness temperature.…”

“…According to previous studies, UTE variation and distribution closely related to land use types, urban structure, urban surface feature materials even the air speed and cloud cover (Zullo et al, 2019). The influence factors of UTE have been extensively studied in recently years, especially the land covers, such as build-up area, water and vegetable, which were demonstrated closely related with UTE (Guo et al, 2019;Keeratikasikorn and Bonafoni, 2018;Madanian et al, 2018). However, the study of spatial distribution, heterogeneity and pattern of urban thermal environment was less researched than the former, which also is a significant research topic for sustainability development of urban environment.…”

“…However, the study of spatial distribution, heterogeneity and pattern of urban thermal environment was less researched than the former, which also is a significant research topic for sustainability development of urban environment. Chaiyapon et al (Keeratikasikorn and Bonafoni, 2018) utilized main statistics to describe the urban thermal environment, such as mean, standard deviation, maximum and minimum LST value; Weng at al. (Weng et al, 2018) presented a innovative method by combining the Shannon's entropy and Pearson chi-square statistic to explore UTE in summer.…”