Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

: Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature inﬂuences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classiﬁcation and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a signiﬁcant decrease in built-up areas was noticed in Sylhet < − 1.45% and Rajshahi < − 3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 ◦ C, Chattogram > 9 ◦ C, and Barisal > 8 ◦ C compared to other districts due to dense population and unplanned urbanization. We identiﬁed higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study ﬁndings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.


Introduction
Urbanization, for instance, in the form of roads, industries, buildings, etc., contributes significantly to changing climatic measures by warming the atmosphere and generating carbon emissions associated with increased surface temperature, referred to as an urban heat island (UHI) [1]. It involves population migration, socioeconomic changes, physical diversion, and multiple differentiations in land surface observation [2]. The urban heat events such as drought and floods. Thus, policy intervention and resilience to lessen the effect of these extreme phenomena are well documented. Rapid urbanization and urban warming are a matter of concern in recent times due to their negative effects on major urban districts [36]. Therefore, a comprehensive study on the impacts of urbanization on UHI intensity is urgently required for a highly populous, resource-limited and poor vulnerable country such as Bangladesh.
Although the effect of LULC changes on LST has been thoroughly investigated by several research scholars in Bangladeshi cities [32,35], only two have explored UHI intensity at the seasonal scale over large Bangladeshi cities [37,38]. For example, [39] used MODIS datasets from 2002 to 2014 during the monsoon period (June-September) to assess UHI intensity across megacities of Asian countries such as Dhaka [36]. Dewan focused on daily and seasonal surface UHI spatiotemporal trends and probable drivers in five cities of Bangladesh. Another recent study performed by Dewan [36] utilized a diurnal (day/night) MODIS time-series dataset from 2000-2019 to determine surface UHI, driver and variability in the similar five cities of Bangladesh. These previously cited studies improved our understanding of spatial and temporal changes of LST and associated UHI; however, these studies have adopted limited datasets (e.g., selected years) and are restricted in scope (e.g., single city or multiple cities). These earlier works also investigated the nexus between LST and vegetation, but the impact of urbanization on major districts' regional climate by appraising the presence of UHI intensity has not yet been performed with regard to the major districts of Bangladesh. The critical literature survey indicates that reference datasets about the UHI intensity of the country's districts are lacking, except for the recent studies [36]. However, a thorough and recent analysis of UHI in association with LST during the dry winter period has not been carried out using high-resolution imageries in major districts of Bangladesh. Our hypothesis is that the impact of urbanization on UHI intensity over major districts in the seven climatic regions varies between these districts, even if they are in the same nation [40]. Therefore, the preliminary intention of this research is to generate reference datasets on spatial and temporal changes of UHI intensity in the seven major districts of the country using high-resolution satellite images data. Seven major districts, namely Dhaka, Chattrogram, Sylhet, Mymensingh, Rangpur, Barishal, and Rajshahi, were chosen based on urban expansion, population size, important divisional cities, and the accessibility of supplementary information [31,41]. The main goals are to (i) examine spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019; (ii) determine the significance of urban LULC expansion in the variation of UHI intensity and LST patterns in seven major districts of Bangladesh. The outcomes of this work can be of value in building region-specific adaptation policies to lessen environmental effects associated with urbanization-derived LST warming and to enhance the quality of life of urban residents.

Data and Methods
Landsat-5 and Landsat-8 satellite images were downloaded from (https://earthexplorer. usgs.gov/; accessed on 20 February 2021) for the years 2000, 2010, and 2020 according to specific paths and rows for the study area purposes during the winter time. There are some reasons for selecting the research data, i.e., during the winter period between 2000 and 2019. The first reason is that satellite observation in wintertime greatly influences UHI intensity owing to the fact that the overpass time varies between districts. The second reason is that energy and health effects vary between summer and winter, so characterizing the winter seasonal scale of the local climate is determined as crucial. The third reason is that measures undertaken to curb urban warming may intensify UHI intensity in wintertime [38]. As for calculating LST, NDVI, and LULC classification, the images were preprocessed and analyzed in ArcGIS 10.8. The following flowchart represents the process of this study ( Figure 1).

Data Acquisition and Pre-Processing
For LULC classification, NDVI derivation, LST retrieval, and to detect changes in UHI, Landsat-5 (2000Landsat-5 ( , 2010 and Landsat-8 (2020) imagery data were collected from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/; accessed on: 20 February 2021) [32,45,46]. This site was also used to collect the digital elevation of the study area. Details regarding the path, row, and the acquisition dates are given in Table 2. All images have a 30 m spatial resolution except band 6 of Landsat-5, which is 120 m, and band 10 and 11 of Landsat-8. To keep a cloud-free environment for this study, every image was collected between 0% to 5% percent cloud level [32,45,46]. ArcMap 10.8 was used for LULC classification, LST and NDVI derivation, and UHI calculation [32,45,46].

Data Acquisition and Pre-Processing
For LULC classification, NDVI derivation, LST retrieval, and to detect changes in UHI, Landsat-5 (2000Landsat-5 ( , 2010 and Landsat-8 (2020) imagery data were collected from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/; accessed on 20 February 2021) [32,45,46]. This site was also used to collect the digital elevation of the study area. Details regarding the path, row, and the acquisition dates are given in Table 2. All images have a 30 m spatial resolution except band 6 of Landsat-5, which is 120 m, and band 10 and 11 of Landsat-8. To keep a cloud-free environment for this study, every image was collected between 0% to 5% percent cloud level [32,45,46]. ArcMap 10.8 was used for LULC classification, LST and NDVI derivation, and UHI calculation [32,45,46].

LULC Classification
Landsat-5 and Landsat-8 images were used for LULC classification. The maximum likelihood supervised classification (MLSC) algorithm was used for LULC classification using training sample areas [45]. Bare land, built-up area, vegetation, and water body were the four categories used to classify the images. To generate LULC maps, around 40 to 50 sample areas were collected for each class [46].

Accuracy Assessment
Accuracy assessment is the procedure used to test the accuracy of computer-classified maps and to view descriptive statistics used to compare classification results with ground information [32]. Around 200-220 points were used as ground observation points to show the validation of the LULC map in the year 2000, 2010, and 2020 in all seven disctricts [45,47,48]. Those points were validated with Google Earth Pro [45,47,48]. Four classes (bare land, built-up area, vegetation, water body) were identified through computer-classified maps and ground observations. Overall accuracy, user accuracy, producer accuracy, and kappa coefficient were calculated from the error matrix [45,47,48]. The standard kappa coefficient test was carried out to quantify the level of agreement. Accuracy is categorized as good when the kappa coefficient is higher than 0.75 [45]. In this study, the results show that all the accuracy was over 0.75.

NDVI Derivation
The NDVI method has been extensively used to identify different vegetation types and non-vegetated areas [49]. R (red) and NIR (near-infrared) values are used to calculate it, and it is a ratio of the two [50]. We employed the NDVI to observe the relationship between built-up area and vegetated area changes corresponding with LST.
The NDVI is a measurement of a plant's health based on how it reflects light at specific frequencies (some waves are absorbed, and others are reflected) [13]. Because it compensates for variations in lighting conditions, surface slope, exposure, and other environmental factors, the NDVI is preferred for global vegetation monitoring [13,19,21,22,48]. In this study, we used the NDVI to obtain the vegetation index which is utilized in various studies, whereas the EVI has also proved its advancement [13,19,21,22,[48][49][50]. Therefore, Climate 2022, 10, 3 8 of 32 besides the NDVI, the EVI is preferred for future comparative study in our study area for vegetation indexing.

LST Derivation for Landsat-5
Thermal band 6 was used for LST calculation for Landsat-5 satellite images in some steps [51]. This can be carried out in three steps.
Step 1 is the conversion of digital number (DN) to Radiance (Lγ): where L γ is spectral radiance, QCAL is the quantized calibrated value in DN, LMAX γ is spectral radiance scaled to QCALMAX in (W/(m 2 × sr ×µm)), LMI N γ is spectral radiance scaled to QCALMI N in (W/(m 2 × sr ×µm)), QCALMAX is the maximum quantized calibrated pixel value (corresponding to LMAX γ ) in DN, and QCALMI N is the minimum quantized calibrated pixel value (corresponding to LMAX γ ) in DN.
Step 2 is the calculation of temperature brightness in kelvin: where T is the effective at-satellite temperature in kelvin, K2 is calibration constant 2, which is 1260.56 for Landsat-5, K1 is calibration constant 1, which is 607.76 for Landsat-5, and Lγ is spectral radiance.
Step 3 is the conversion of the temperature to degrees Celsius.

LST Derivation for Landsat-8
Thermal band 10 was used to collect the land surface temperature for Landsat-8 [52], which can be performed in six steps.
Step 1 is the calculation of the radiance from band 10 [52].
where Lγ is spectral radiance, M L is the band-specific multiplicative rescaling factor from the metadata, and A L is the band-specific additive rescaling factor from the metadata.
Step 2 comes after converting the spectral radiance, which then needs to be converted to atmospheric temperature brightness [53].
where TB is the top of the atmospheric brightness temperature in kelvin and K2, and K1 is the band-specific thermal conversion constant of 1321.0789 and 774.8853.
Step 3 is to calculate NDVI, which is essential for calculating LST for Landsat-8. [54] where NIR is band 5 and RED is band 4. Step 4 is the calculation of the proportion of vegetation using maximum and minimum NDVI, Step 5 is the calculation of land surface emissivity using Pv [55] Step 6 is the calculation of land surface temperature in degrees Celsius, where γ is the wavelength of emitted radiance, c2 is h × c/s = 1.4388 × 10 -2 mK =14,388 µmK, where h is Plank's constant, which is 6.626 × 10 − 34 Js, s is Boltzmann constant, which is 1.38 × 10 − 23 J/K and c is the velocity of light which is 2.994 × 103 m/s.

Estimation of UHI
UHI is often described as the difference between rural and urban areas [56]. This can also be defined by quantification to consider urban surfaces' local and regional climate change [56]. As for calculating the UHI, land surface temperature value was used [45].
where T is LST and Tm is LST mean, and Tsd is the standard deviation of LST.

LULC and NDVI Variations and Accuracy ASSESSMENT
Spatiotemporal LULC variations in seven districts were categorized in four definitions for 20 years, including the three assessed years of 2000, 2010, and 2020 ( Figure 4). The changing pattern depicts rapid urban expansion in heterogeneous formations for Dhaka, Barisal, Mymensingh, Rangpur, and Chattogram, whereas a systematic change was observed in Rajshahi and Sylhet districts. In addition, Rajshahi city development was observed from east to west, and Sylhet was mostly from the center to the southern part. Table 3 is the measurement of change area variates for different classes of LULC. Dynamic expansion of the built-up area was observed for Barisal, ranging from 10.06% to 32.51%, whereas substantial prolongation of the urban area was experienced by Chattogram, Mymensingh, and Rangpur districts, which changed from 2.76% to 14.98%, 3.24% to 18.36%, and 8.02% to 13.22%, respectively, during 2000-2010 and 2010-2020. A sudden notable increase in urban area was found for the megacity of Dhaka 17.36%, which was observed as negative (−0.98%) for 2000-2010. In contrast, large momentous diminution was fostered by Sylhet, presenting 1.45% of urban land in the 2010-2020 period; meanwhile, it was 12.21% during 2000-2010. Furthermore, Rajshahi and Sylhet were influenced by a decreased urban corridor for both upward and downward patterns compared with other districts, which indicate 5.83% to 3.72% and 1.72% to 0.91% throughout in the same period, as mentioned earlier. However, the significant seesaw is also explored for other classes (Table 3). These results corroborate developed urban areas, scattered extensive population, and the zonation of industrial areas as responsible factors for the swell in built-up areas with particularly diminishing vegetation and water sources. Several existing pieces of works of literature extrapolate similar findings [57][58][59]. However, another investigation in Bangladesh proves Rangpur, Rajshahi, and Sylhet had excessive city growth of 16%, 12%, and 11%, respectively, which slightly contradicts the present study [60]. Additionally, vegetated areas were observed to decrease significantly over all of the seven districts. areas with particularly diminishing vegetation and water sources. Several existing pieces of works of literature extrapolate similar findings [57][58][59]. However, another investigation in Bangladesh proves Rangpur, Rajshahi, and Sylhet had excessive city growth of 16%, 12%, and 11%, respectively, which slightly contradicts the present study [60]. Additionally, vegetated areas were observed to decrease significantly over all of the seven districts.     Figure 5 shows the NDVI variations for the districts considered for the evaluation of the correlations between urban expansion and NDVI reduction during 2000-2020. We found a significant relationship between built-up area and NDVI, which demonstrates areas of the NDVI decreased excessively for Barisal, Dhaka, Mymensingh, Chattogram, and Rangpur, whereas the built-up area increased substantially in the respective areas ( Figure 5 and Table 3). In contrast, Rajshahi and Sylhet experienced less NDVI degradation, supported in Table 3, because the urban area was not heavily expanded, resulting in less vegetation loss. Overall, there is a strong and positive correlation between NDVI-estimated areas and the LULC classification assessment.  Accuracy evaluation is vital for urban development and the surface's temperature [61]. Congalton used it to determine classification validation. Moreover, accuracy assessment was utilized for the present exploration to validate LULC classification (Table 4). Overall, 200-220 reference points were taken for each district and visualized with the Google Earth Pro engine. The kappa coefficient and overall accuracy values mostly exhibit more than 80 for LULC classes. This estimated value suggests a strong validation demonstration for the study area categorization. Additionally, the kappa coefficient > 0.75 strengthens the very good position of classified accuracy, whereas 40< is defined as poor accuracy [32,61].    Figure 6 and Table 5 demonstrate the spatiotemporal change measurement of land surface temperature in the study period of 2000, 2010, and 2020. Extreme land surface temperature due to compact developed areas depicts the hotspots zones of the respective location. LULC mutation and LST transformation are equivalent in temporal and spatial depiction between the stated period. Barisal district had a large area of temperature change ranging from 17 to 18 • C (>45% area) in 2000, wherein in two decades, it increased to 18-20 • C (>68% area). This corresponds to   Table 5, which signifies expanded urbanized and dryland areas causing an increase in temperature; hence, hotspot areas ( Figure 7) were primarily found in those two LULC categorized areas. Similarly, in the Chattogram district, dominating scatter LST formation found which depicted >32% area of 20-21 • C in 2000 as it increased in 2020, with a >55% area enlargement of 21-23 • C, which compares identical built-up and bare land area extension (Figure 4 and Table 3). In 2000, Dhaka's more significant portion (43% area) was 16-17 • C; a rapid increase was observed in 2010 that sustained until 2020, comprising 20-23 • C for the prolonged area of 66%. Additionally, Mymensingh represented a high concentrated temperature zone along the riverside in bare land and the developed area (Figures 4 and 6), validated by Figure 7 of the hotspot zones of the respective area. A gradual upward trend of temperature augmentation was also observed for Mymensingh from 2000 to 2020 in the range of 17-18 • C (>60% area) to 20-22 • C (>70% area). Furthermore, for the Sylhet district, the LST threshold remained 16-24 • C, which was significant in the proximate sense for the entire echelon of exploration ( Figure 5 and Table 5). Due to acknowledged heterogenous urban and bare land (Figure 4), hotspots of the Sylhet district remained interspersed in 4.42% of the area in 2020 (Table 5). Ordinary change of LST precipitated for Rangpur district that sustained 18-20 • C for the utmost territory during the same study period with a deficient hotspot area of 4.47% (Figures 6 and 7 and Table 5).    In contrast to the other six districts of Bangladesh, Rajshahi, which experienced less urban expansion (Table 3), showed LST hotspots of 2.47% area, while gradual temperature improvement was noticed from 2000 to 2020, whereas 2010 was found to be stretched for LST, as well as the highly concentrated urban area (Figures 6 and 7 and Table 5). These results conclude that Dhaka and Chattogram are highly concentrated urban areas with intense land surface temperature; hence, they retain greater hotspot zones. Similarly, diversified surface temperatures are acknowledged for different cities in Bangladesh including Chattogram [62,63], Dhaka [58], Rajshahi [45,64,65], Barisal [66], and Mymensingh [67]. The obtained identification determines that dry land considers high LST values as well as urbanized areas, which is shown in the exploration of several studies [45,63]. As each district advances, urban expansion in Bangladesh is the key reason for rainwater infiltration and potential water flow, leading to groundwater deficiency. Moreover, these multiplicators cause abnormal water cycles due to evaporation-transpiration disharmonious phenomenon.

Assessment of LST
Consequently, the water cycle degrades, resulting in environmental change [68]. Thus, it affects the study area's maximum and minimum temperature fluctuations [22]. Aerosol pollution and landscape albedo variations are accompanied by excessive land usage. As a result, land-use reform is one of the worst fundamental cognitive biases that could compromise the planet's radioactive equilibrium [69]. For instance, warm air levels decrease significantly during the transformation of swamp surfaces to cropland, corresponding to extreme albedo rates [70].
Urban expansion influences the minimum temperature to a greater extent than the maximum temperature in the winter season, and this decrease in temperature variations in the winter period has been earlier reported by several researchers [71,72]. In [73], Huang stated that the intensity of LST is elevated because of rapid urbanization in China, causing a higher daily temperature variation in Beijing compared to Shanghai. So, LULC change is the main disquiet; the major districts of Bangladesh have likely faced a higher degree of variation than other areas worldwide, a rate mainly driven by elevated rural-urban migration strategies [74]. In contrast to the other six districts of Bangladesh, Rajshahi, which experienced less urban expansion (Table 3), showed LST hotspots of 2.47% area, while gradual temperature improvement was noticed from 2000 to 2020, whereas 2010 was found to be stretched for

UHI Intensity Assessment
The UHI intensity graph for the seven significant districts of Bangladesh depicts diversified intensity for each location (Figures 8 and 9). The topmost increase in intensity is observed for Mymensingh, 2.81 • C to 10.8 • C, from 2000 to 2020. Moreover, substantial amplification was noticed for Chattogram and Sylhet, observed as 9.65 • C and 7.74 • C. Consequently, in 2020, Barisal retained approximately the same 8.220 C intensity as in 2000, whereas Dhaka indicated a reduction of 1.46 • C as it was 6.95 • C in 2000.

REVIEW
24 of 31 In contrast, Rajshahi and Rangpur alleviated their temperature intensities notably from 9.11 °C to 6.31 °C and 9.02 °C to 6.37 °C, accordingly, from 2000 to 2020. Figure 8 provides an accurate spatiotemporal demonstration of heat intensity regarding the particular study area's industrious, populated, and dry land. The aggregated UHI intensity also corresponds with the illustration of hotspots shown in Figure 7 for each aerial and temporal distribution. [36] Dewan also found the same results as those reported in the present investigation, which determines high intensity in the core of urban areas due to heavy anthropogenic force, population, and fewer vegetated areas of five major cities of  In contrast, Rajshahi and Rangpur alleviated their temperature intensities notably from 9.11 • C to 6.31 • C and 9.02 • C to 6.37 • C, accordingly, from 2000 to 2020. Figure 8 provides an accurate spatiotemporal demonstration of heat intensity regarding the particular study area's industrious, populated, and dry land. The aggregated UHI intensity also corresponds with the illustration of hotspots shown in Figure 7 for each aerial and temporal distribution. [36] Dewan also found the same results as those reported in the present investigation, which determines high intensity in the core of urban areas due to heavy anthropogenic force, population, and fewer vegetated areas of five major cities of Bangladesh: Dhaka, Chattogram, Khulna, Rajshahi, and Sylhet. Thus, the effect of UHI on winter temperature is evident. The temporal changes of UHI could be linked with fast LULC variation and crop phenological change [75], a decline of reference evapotranspiration because of a lot of impervious layers [76], and disparity cooling rates during the winter period [77]. The main difference in delineating the selected urban coverage is a probable area of UHI changes. In [45], an increase of >37% surface temperature caused by the built-up area of Rajshahi was revealed, which validates the UHI observation. Furthermore, a canopy describes dwellings and cycle lanes within a built environment [78]. In general, region-specific UHI works of the country are still lacking except for one study [36]; however, earlier cited works [78] using a chosen Landsat dataset have reported an enhanced UHI, following the present outcomes. Earlier works have focused on the greenness activity in regulating surface temperature, especially during the daytime in the summer [79][80][81][82] The cooling ability of an urban region is commonly regulated by differences in evaporation cooling potential, changes in LULC, the lack of moisture content, and the absence of vegetation cover [83,84].

Possible Implication and Limitation of the Study
Fast population growth and the related urban districts are considered pivotal drivers of local and regional temperature variations [85], particularly in developing countries such as Bangladesh. Urbanization coupled with global climate warming will likely enhance heatassociated mortality events [86]. A normal urban district in Bangladesh is characterized by few tree plantations and highly scattered vegetated areas [87]. Tree coverage might be of aid in enhancing cooling activities. The cooling impacts of vegetation rely on vegetation types. For example, green vegetation is noticeably more efficient in giving year-round advantages than other vegetation types [88]. This study gave reference datasets on the seasonal intensity of UHI during the winter dry season, and larger districts seem to have higher variation than smaller districts. The outcomes of our research are anticipated to give vital information for future study, provided that global climate warming is possible to exacerbate UHI impacts in the forthcoming period. This research aids advancement towards the United Nation's SDGs and the regional climate information found in this work can support the generation of district-specific adaptation strategies.
Several drawbacks to this work should not be overlooked. First, the only remotesensing-derived index was adopted to identify UHI over major districts of Bangladesh. The climatic variables [89], landscape metrics [80], and clear albedo [90] can make further study more effective, as the parameters could have a substantial influence on LST. Second, since UHI has considerable daily and seasonal variations, which would also be of great value, we only considered the winter period and thus limited its wide application. Third, the problem of lessening pixel values due to a lack of clear sky is noticed most remarkably in the winter period in Bangladesh, and thus the effect on LST and derived UHI intensity is most evident in that season. In general, the application of in situ arrangement can also be applied to estimate the validation of LST extents. Ultimately, it is worth mentioning that the outcomes obtained from our research are based on high-resolution satellite imageries assessment using remote sensing tools, and the measured LST is not verified with the actual ground condition. Despite these drawbacks, this research gives a better understanding of the local temperature changes and global warming within the large districts of Bangladesh and gives further information for developing potential mitigation actions. However, this deserves further investigation.

Conclusions
This research aimed to investigate LULC, NDVI, and LST variations concerning UHI intensity diversification over Bangladesh's seven most populated urbanized districts, combining 20 years of quality-controlled LULC and LST datasets. The MLSC algorithm was employed to measure the LULC category with good precision (81-93%). The results indicated that a significant reduction in vegetated land was observed at the expense of built-up areas in all districts except for Rajshahi and Sylhet. In the winter season, LST had increased from 3 • C to 8 • C during the study period. The LST patterns indicate that built-up areas under urban expansion exhibited high LST, while the vegetated area and water bodies depicted relatively low LST. Our study revealed that the UHI intensities appeared to be increasing, which might mean the narrowing of the diurnal temperature range. The UHI intensities for all districts were found to vary from 8 • C to 10 • C. Analysis showed that the magnitude of UHI intensity was high for Mymensingh (10 • C) and low for Dhaka (1.46 • C). These changes will substantially affect the regional climate change of these districts, which highlights significant thermal variations present in all district areas. This study identified significant hotspots zones and UHI intensity in densely populated urban dwellings and low moisture content area/dry, bare land. The outcome of our research is anticipated to give crucial information for future work, provided that global warming is expected to exacerbate UHI impacts in the forthcoming period. A practical initiative to a city decentralization policy is suggested. Governments, the NGO sector, climate scientists, urban planners, and engineers could consider the potential findings of this study for sustainable climate and urban design purposes. Our study confirms development towards the UN's SDGs (sustainable development goals), and the local climate information in our study could aid in developing district-specific mitigation strategies.