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Bangkok is a rapidly expanding city with existing natural areas being replaced by developed areas creating an urban heat island (UHI) phenomenon in the city. LANDSAT imagery, near-infrared wavelength data, and time series information were used to study and to monitor the phenomenon of surface urban heat island (SUHI) in Bangkok. The variation of la...
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... The increment in PM10 may be related to the rise in construction activities, e.g., the construction of condominiums and sky train lines in Bangkok and its neighborhood. Sanecharoen et al. (2019) reported that the Landsat satellite images for 2008-2014 indicate land-use changes in Bangkok. They discovered that natural areas were being replaced by artificial objects, as indicated by increasing number and density of highrise buildings in the city. ...
... The density of buildings and industries in the area indirectly affect the corrosion of materials. Recently, a study reported the influence of building and industrial densities on land surface temperature in Bangkok (Sanecharoen et al., 2019). As seen from the DRFs (Table 1), temperature plays an important role in material recession and is used as a variable to explain material corrosion. ...
Historical buildings are recognized as the valuable cultural heritage of a nation. They may suffer material deterioration unavoidably because of exposure to air pollution. We used geographic information systems with dose-response functions (DRFs) to estimate the corrosion of copper and Portland limestone, and their risk of corrosion with regard to historical buildings in Bangkok, Thailand. The first step was to find a suitable spatial interpolation method considering the air pollution and meteorological measurement data for 2010-2019 from 26 monitoring stations in Bangkok and its neighborhoods. Applying multiple performance measures, the inverse distance weighting (IDW) method was found to be the most suitable. Predictions of the pollutant concentration in the spatial atmosphere showed that the concentration of all pollutants (SO2, NO2, O3, and PM10) tends to increase in 2028. Air pollution exposure time duration tends to be a key factor affecting the corrosion of material. The results of spatial corrosion estimations indicated that in 2010, the corrosion of copper and Portland limestone were at acceptable levels; however, the estimated corrosion levels for 2019 and 2028 are higher and beyond the acceptable levels. Moreover, both materials in the Rattanakosin historical area exceed their tolerable corrosion rates with considerably serious risks in 2028. The results can be further used to establish active measures to reduce the rate of corrosion of historical buildings in Bangkok.
... Infrared thermal imager uses an infrared detector (1) Q = εσT 4 and an optical imaging objective lens to receive the infrared radiation energy distribution pattern of the test object and then reflects it on the photosensitive element of the infrared detector to obtain infrared thermal images (Speakman and W 1998). At present, this imager is widely used for the visualisation and detection of thermal performance (Cardone and Merla 2017;Sanecharoen et al. 2019;Jiao et al. 2020). ...
The local thermal property evaluation of pavement materials has not received enough attention, making the implementation of cool material measures to alleviate the urban heat island difficult. This study aims to conduct a control experiment on cool pavement bricks selection from the local market. The surface temperature difference and change characteristics of 28 bricks with different physical properties (i.e. colour, thickness, size and internal structure) were recorded by an infrared thermal imager and thermometers. Comparative analysis shows that the maximum surface temperature of the light-yellow brick is 7.3 °C lower than that of the ash black one, and the maximum surface temperature of hollow bricks is 5.6 °C lower than that of solid bricks. Thick bricks have a slightly low mean and maximum surface temperatures, and large bricks also have a low maximum surface temperature, but only deep coloured bricks. The analysis of variance revealed that for the maximum surface temperature, the three factors (i.e. colour, thickness and internal structure) have an interactive effect; for the mean surface temperature, only the colour and internal structure factors have a significant independent effect. This paper provides a feasible bottom-up cooling scheme for urban underlying surface.
... Based on homogenized daily data during 1970-2019, the present study provided additional evidence to supplement the earlier studies of the urbanization effects on local surface climate in Thailand (Jongtanom et al., 2011;Ruthirako et al., 2015;Sanecharoen et al., 2019;Kamma et al., 2020;Kachenchart et al., 2021), especially the recent work of Kachenchart et al. (2021), demonstrating that urban-induced warming contributed to a large fraction (40.5%) of a national temperature increase. The results also extended the current understanding that urbanization as a local important factor exert great influence on changes of temperature and rainfall extremes in Thailand, in addition to large-scale natural and anthropogenic-induced climate phenomena previously documented (e.g. ...
Applying the homogenized daily data during 1970–2019, this study examined the urbanization effects on changes in a set of extreme climate indices for large cities across Thailand, where has undergone rapid urbanization in recent decades. Analysis revealed positive urbanization effects on a number of the indices related to hot extremes, providing a consistent picture with the previous studies. Larger and more significant urbanization effects were evident for minimum temperature-related extreme indices, probably resulting from stronger nighttime enhancement of the urban heat island (UHI) intensity. From a sub-regional perspective, the urbanization effects for four clusters contributed, on an average, to half of the total trends of temperature extremes. Another noteworthy finding was marked by significant urbanization-induced increases in the amount, frequency, intensity, and magnitude of rainfall extremes at the urban stations in the Bangkok metropolis and the central part of the country. Analysis showed that most of the rainfall indices in this region exhibited a 100% urbanization contribution. These results highlighted that the Bangkok metropolis and its adjacent urban areas experienced substantial increases in climate extremes, resulting in greater risks from their associated disasters. The mechanisms how urbanization has induced changes in climate extremes in Thailand seem to be complex, depending on many factors including increases in UHI intensity, heat absorption in urban boundary layer, urban-rural roughness, anthropogenic aerosol, and pollutant emissions. However, to better understand their underlying physical processes and mechanisms, climate model-based dynamical experiments and additional analysis of high-resolution satellite data are further needed.
... The Bangkok Metropolitan Area (BMA) is the most economically developed city in Thailand ( Figure 1) spread across 1568.7 km 2 , with 5,696,409, registered residents as of 2015 [36,37]. BMA is composed of 50 districts [38], which are divided into 3 main areas: inner city (22 districts, the old city center dominated by the historical conservation area, government offices, schools, and densely populated commercial areas), the urban fringe (22 districts, area of population expansion, commercial and residential activities, located within 10-20 km radius of the city center), and suburban areas (6 districts, the outer area of BMA, dominated by empty spaces and farming areas with a mixture of urban and rural) ( Figure 1). ...
... The LST calculation methods are based on LANDSAT 5 and LANDSAT 8 User's Handbooks [40,41], which are widely used [37,42]. To retrieve LST, the steps are as follows. ...
... Landsat 5 (band 3, 4) and Landsat 8 (band 4, 5) were used to calculate the Normalized Difference Vegetation Index (NDVI). Then, the proportions of vegetation covering the BMA were calculated by using the equation [37,44] as shown: ...
It is known that climatic factors, such as rainfall and temperature, influence increases in dengue incidences. Thus, this research uses spatial statistical analysis to consider the association of urban LST with dengue incidences.
... Based on homogenized data during 1970-2019 and the UMR method, this study investigates the urbanization effects on Tmean trends for Thailand where has recently experienced fast urbanization but the scientific information on the degree to which urbanization influences local and national surface climate is not well documented. Most of the previous studies focus on UHI analysis at an individual city scale especially Bangkok Metropolis [34][35][36][37][38]. Therefore, the main objectives are to analyze the differences in the rates of Tmean changes between the urban and suburban stations and rural stations, and to estimate the relative contribution of the urbanization effects to the trends of both individual station and country-wide average. ...
... Previous studies have also shown that seasonal variations of the UHI in the tropical cities are evidently more related to urban-rural surface moisture characteristics with higher intensities during the dry season [68,69]. Our results also agree well with the studies carried out in Thailand based on both station observations and satellitemeasured data showing that UHI intensity in Bangkok, Chiang Mai and Songkhla is strongest in dry season [34][35][36][37][38]. The maximum UHI intensity of around 6-7°C in Bangkok is found during dry season [35]. ...
Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 o C per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thailand. The increasing trend of annual Tmean for Thailand as a whole after adjusting urban-warming bias is brought down to the same rate as the annual global mean temperature trend, reflecting a national baseline signal driven by large-scale anthropogenic-induced climate change. Our results provide a scientific reference for policy makers and urban planners to mitigate substantial fraction of the UHI warming.
... Urban heat island refers to the phenomenon of higher air and land surface temperatures in Urban areas as compared to the neighbouring country side (Sanecharoen et al., 2019;Guha et al., 2018). Land surface temperature is a significant parameter in exploring the energy balance and exchange of surface matter that control the surfaces biological, chemical and physical processes on the Earth (Deng et al., 2018;Nasar-u-Minallah, 2018;Pu et al., 2006). ...
Abstract. The present study focuses on determining the correlation of land surface temperature (LST)
with normalized difference builtup index (NDBI) and normalized difference vegetation index (NDVI) of
Lahore, a metropolitan city of Pakistan using landsat 5 and 8 dataset. This study also categorizes different
types of land use through supervised image classification scheme and maximum likelihood algorithm
(MLA), and assess the correlation between LST and land use type of different classes. The results of the
study indicate that modifications in type of land use altered spatial variations of land surface temperature
in 1990 and 2015. The findings also show that the ever increasing temperature caused by impervious
surfaces such as builtup area, roads, construction sites and vacant land considerably contributes to heat
island effect. However, vegetation cover, green and blue spaces decrease LST and effectively relieve the
effect of heat island. LST builds a strong positive correlation with NDBI and strong negative correlation
with NDVI. Based on the regression analysis between LST and NDBI and NDVI, these indices can be
utilized as a sign to assess the impact of LU changes on temperature. The results further indicates that LST
changes follow the pattern of LU changes in Lahore and the warmness intensity has been observed highest
in the high density builtup area and vacant land, while low at the green and blue spaces. The analysis
reveals that an increase in LST by 1.98 °C during the period of 25 years at the rate of 0.079 °C/year in
high density builtup area was due to the excessive increase in settlement growth. The study concludes that
change of land use has an effect on the LST in Lahore.
