International Journal of Environmental Research and Public Health Article The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China Nadeem Ullah 1,† , Muhammad Amir Siddique 1,† , Mengyue Ding 1 , Sara Grigoryan 1 , Irshad Ahmad Khan 2 , Zhihao Kang 1 , Shangen Tsou 1 , Tianlin Zhang 1 , Yike Hu 1, * and Yazhuo Zhang 3, * 1 School of Architecture, Tianjin University, Tianjin 300272, China 2 Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, 08193 Barcelona, Spain 3 School of Civil Engineering, Tianjin University, Tianjin 300272, China * Correspondence: huyike11@tju.edu.cn (Y.H.); zhangyazhuo@tju.edu.cn (Y.Z.) † These authors contributed equally to this work. Abstract: Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city’s land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 ◦ C, and the region moved to a high temperature that the built-up area’s temperature increased by 1.5%. Still, the increase in vegetation Citation: Ullah, N.; Siddique, M.A.; cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 ◦ C. Ding, M.; Grigoryan, S.; Khan, I.A.; The high-temperature areas moved into an aerial distribution, and the direction of urbanization Kang, Z.; Tsou, S.; Zhang, T.; Hu, Y.; determined their path. Urban heat island mitigation is best achieved through forests and water, and Zhang, Y. The Impact of Urbanization managers of urban areas should avoid developing bare land since they may suffer from degradation. on Urban Heat Island: Predictive The increase in the land surface temperature caused by the land cover change proves that the site is Approach Using Google Earth becoming more urbanized. The findings of this study provide valuable information on the various Engine and CA-Markov Modelling aspects of urbanization in Tianjin and other regions. In addition, future research should look into the (2005–2050) of Tianjin City, China. Int. public health issues associated with rapid urbanization. J. Environ. Res. Public Health 2023, 20, 2642. https://doi.org/10.3390/ ijerph20032642 Keywords: land surface temperature; urban heat island; land use cover change; urban planning and development; ecological evaluation; urban system Academic Editor: Paul B. Tchounwou Received: 4 December 2022 Revised: 23 January 2023 1. Introduction Accepted: 29 January 2023 The rapid growth of urban areas worldwide has been observed over the past few Published: 1 February 2023 decades [1]. The main factors contributing to urbanization are the lack of economic develop- ment and the increasing population [2]. Despite the slow growth of the global population, it is still expected that the number of people will continue to increase by around 2030 [1]. According to estimates, the world’s urban area is expected to grow by over a million Copyright: © 2023 by the authors. kilometers by 2030 [1,3–6]. Urbanization is most prevalent in developing countries due to Licensee MDPI, Basel, Switzerland. rapid economic development. China is one of the most prominent in the world regarding This article is an open access article distributed under the terms and urbanization. It has been estimated that the country’s urban land area expanded at an conditions of the Creative Commons annual rate of 13.3% [7]. Attribution (CC BY) license (https:// Urbanization positively impacts people’s lives, as it allows them to improve their living creativecommons.org/licenses/by/ standards and reduce their energy consumption. It can also help mitigate climate change 4.0/). by reducing vehicle miles travelled and greenhouse gas emissions [8]. Unfortunately, there Int. J. Environ. Res. Public Health 2023, 20, 2642. https://doi.org/10.3390/ijerph20032642 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2023, 20, 2642 2 of 15 are still negative impacts of urbanization. Due to the human activities that have occurred in the past few decades, the city has expanded. This process has caused both positive and negative effects [9]. Urbanization is a complex process involving multiple modelling variables and mecha- nisms involved in its development. The various aspects of this process must be thoroughly studied to understand its effects. One of the most effective ways to predict an urban area’s characteristics is through Land Use Cover change (LUCC) analysis [8,10,11]. A compre- hensive simulation of the urban development process is necessary in today’s world [12,13]. With the help of spatial data, such as land area and development characteristics, urban models can be used to study the patterns of urbanization. These models can also simulate the conditions affecting the city’s development. Urban models use mathematical equations to describe the urban system [14,15]. They can also deal with the various factors that affect the development of a city. The study results are based on the interactions between different strategies and aspects [9,16]. Urban models are becoming more effective at predicting future changes in the LUCC due to the complexity of the process. They can use the avail- able data and conditions to model the different factors affecting the city’s development. Numerous studies have been conducted on the use of LUCC in policy formulation and decision making. However, the application of cellular automata and the Markov process is relatively rare. Tianjin is considered one of the most prominent cities in China that has experienced sustained urbanization, industrialization, and urbanization in China [17,18]. As a result of its ongoing development, many cities are expected to continue to grow [19]. It is essential that the cities’ LUCC change be studied and analyzed to determine its future trend [20,21]. This study was conducted to comprehensively analyze the various factors that have affected the city’s development. The study examined the LUCC change in Tianjin from 1995 to 2015. It first created five maps with different classifications at different points in time. The analysis revealed that many areas were converted into built-up areas. The model was then analyzed to create a set of dynamic variables for the Cellular Automata Model (CA) [11,22,23]. These variables were then used to project the LUCC change in the city from 2025 to 2050. 2. Materials and Methods 2.1. Study Area The city of Tianjin, the largest city on China’s northern coast, is straddled at 38◦ 340 N to 40◦ 150 N and 116◦ 430 E to 118◦ 040 E (Figure 1), having a thousand square kilometers. It is regarded as the fifth-largest city in the country after Shanghai, Beijing, Guangzhou, and Shenzhen [24]. With a warm, temperate, semi-humid monsoonal climate, it is characterized by four distinct seasons during the year [25,26]. Over the past few years, Tianjin has experienced massive urbanization, with its population increasing from 12.99 million in 2010 to 13.86 million in 2021 [1].The city of Tianjin has a gross domestic product of about 240 billion yuan, making it one of the most prominent economic centers in China’s northern region [25,27,28]. It is an international port city and has experienced rapid urbanization over the past few decades. Due to rapid urbanization, large areas of land, such as forests, farmland, and meadows, have been converted into built-up areas [29,30]. 2.2. Acquisition of Spatial Dataset The United States Geological Survey (USGS) provided cloud-free images of the study area, which were taken from path 170 and series 053, through its website (http: //earthexplorer.com) [6,9,10,13,17,25,31]. Due to the varying time of day and night in the study area, the data collected by the Landsat 5 Thematic Mapper (TM) and the Landsat 7 Enhanced Thematic Mapper (ETM) were used to create the LUCC map [13,18,32–34]. The data collected by the two satellites (Table 1) were also used to calculate the Normalized Difference of Vegetation Index (NDVI) and Land Surface Temperature (LST). es. Public Health 2023, 20, x FOR PEER REVIEW 3 of 15 Int. J. Environ. Res. Public Health 2023, 20, 2642 3 of 15 Figure Figure 1. Geographic 1. Geographic location location and characterization and characterization of the study of the study area: area: (A)Republic (A) People’s People’s Republic of China; of China; (B) Beijing–Tianjin–Hebei (B) Beijing–Tianjin–Hebei (TBH); and (C)(TBH); and (C) multispectral multispectral satellite satellite image image ofcity. of Tianjin Tianjin city. 1. The Landsat data used in this study are outlined in detail. 2.2. Acquisition ofTable Spatial Dataset The United States Geological Survey (USGS) Period-Images provided Satellite cloud-free Sensor ID images of Resolution Spatial the study area, which were 2005 taken from path 170 and May–September series Landsat-5 TM 053, through 30 itsm|100 website m 2010 May–September Landsat-5 TM 30 m|100 m (http://earthexplorer.com) [6,9,10,13,17,25,31]. Due to the varying time of day and night in 2015 May–September Landsat-8 OLI_TIRS 30 m|100 m the study area, the data collected 2020 by the LandsatLandsat-8 May–September 5 Thematic Mapper (TM) and30the OLI_TIRS Landsat m|100 m 7 Enhanced Thematic Mapper (ETM) were used to create the LUCC map [13,18,32–34]. The data collected by the two satellites (Table 1) were also used to calculate the 2.3. Methodology Normalized Difference Anof Vegetation integrated Indextemplate workflow (NDVI) (Figure and Land Surface 2) was used Temperature (LST). to perform a series of steps. We began by processing the information sets in GEE to create a false colour positive Table 1. The Landsat data (FCC) used in this study [10,11,25,35,36]. are outlinedmap A georeferenced in detail. of the outer boundaries of Tianjin was used to extract and mask the study area from all spatial ideas. The support Vector Machine (SVM) Period-Images classification method was Satellite applied to Sensor improveID the supervised Spatial Resolution classification results obtained from Landsat imagery [13,30,37,38]. 2005 May–September Landsat-5Then, TM the LST was calculated to determine 30 m|100 m the time zones 2010 May–September in the city [17]. A Pearson correlation Landsat-5 TM analysis was performed 30 m|100 mbased on the land cover, average LST, and percentage of greened and non-greened areas from 2005, 2010, 2015 May–September Landsat-8 OLI_TIRS 30 m|100 m 2015, and 2020 [8,31,39,40]. The CA-Markov model was used to forecast future trends for 2020 May–September LUCC and LST in 2035 and Landsat-8 2050 [41]. OLI_TIRS 30 m|100 All spatial statistical analyses and mapsm were created using ArcGIS 10.7, and ggplot2, corrplot and psych packages used in RStudio [42]. 2.3. Methodology An integrated workflow template (Figure 2) was used to perform a series of steps. We began by processing the information sets in GEE to create a false colour positive (FCC) [10,11,25,35,36]. A georeferenced map of the outer boundaries of Tianjin was used to extract and mask the study area from all spatial ideas. The support Vector Machine (SVM) classification method was applied to improve the supervised classification results obtained from Landsat imagery [13,30,37,38]. Then, the LST was calculated to determine the time zones in the city [17]. A Pearson correlation analysis was performed based on the Int.Int. J. Environ. Res. J. Environ. Public Res. Health Public 2023, Health 20, 2023, 20,x xFOR FORPEER PEERREVIEW REVIEW 4 of4 15 of 15 were were Int. J. Environ. Res. Public Health 2023, created created using 20, 2642 using ArcGIS ArcGIS 10.7, and and ggplot2, ggplot2, corrplot corrplotand andpsych psychpackages packages ofused 4used15 in in RStudio RStudio[42]. [42]. Figure 2. Flowchart of the methodology for the present study. Figure Figure 2. 2. Flowchartofofthe Flowchart the methodology methodology for forthe thepresent presentstudy. study. 2.4. 2.4.Land LandUseUseCover Cover Change (LUCC) Calculation Change (LUCC) Calculation 2.4. Land Use Cover Landsat Landsat imagery Change (LUCC)TM imagery (Landsat-5 (Landsat-5 TM Calculation && Landsat-8 Landsat-8OLI) OLI)waswasused usedtoto map mapthethe LUCCLUCCof of Tianjin Tianjin city cityfor Landsat for aafour-time imagery frame (2005, (Landsat-5 four-time frame (2005, 2010, TM 2010, 2015,and & Landsat-8 2015, andOLI) 2020). 2020). TheSupport was The Support used Vector toVector map theMachine LUCC of Machine (SVM) Tianjin classification (SVM)cityclassification algorithm algorithm for a four-time frame inGEE in GEEwas (2005,was used used 2010, totoclassify 2015, classify land land and 2020). use use Theandand areas areas Support [43,44]. [43,44]. Vector Five Five Machine types (SVM) ofofLUCC LUCC were typesclassification identified: werealgorithm built-up identified:inbuilt-up GEE was land, land, cropland, cropland, used lowland, lowland, to classify forest, landforest, use andandand water water areas body body [43,44]. Five (Figure (Figure 3A). 3A).Built-up Built-up land included included artificial artificial structures structures such such asas buildings, buildings, types of LUCC were identified: built-up land, cropland, lowland, forest, and water body roads, roads, andand other other impervioussurfaces. impervious surfaces. Water Water included included rice ricefields, reservoirs, fields, reservoirs, and andrivers [28,35,45]. (Figure 3A). Built-up land included artificial structures such as rivers [28,35,45]. buildings, roads, and other impervious surfaces. Water included rice fields, reservoirs, and rivers [28,35,45]. Figure Figure3.3.(A) (A)Shows Shows the the land use land land cover coverarea, area,and and(B) (B)proportional proportional changes changes of LUCC of LUCC during during 2005-2020. 2005-2020. Figure 3. (A) Shows the land use land cover area, and (B) proportional changes of LUCC during At specified At specified intervals, intervals,GEE GEEwaswas usedused to assess the accuracy to assess of the classification the accuracy results. of the classification 2005-2020. Field reference results. points were Field reference collected points wereusing a Google collected Earth using a explorer, which collected Google Earth explorer,field which reference average of 250 points for 2005, 2010, 2015, and 2020. collected field reference average of 250 points for 2005, 2010, 2015, and 2020. At specified intervals, GEE was used to assess the accuracy of the classification results. Field reference points were collected using a Google Earth explorer, which collected field reference average of 250 points for 2005, 2010, 2015, and 2020. Int. J. Environ. Res. Public Health 2023, 20, 2642 5 of 15 The classification accuracy of the signatures and images was evaluated by creating a confusion matrix consisting of rows and columns that refer to the categories derived from the image. The matrix rows are labelled with the reference values, while the columns represent the categories identified using the same criteria. The total number of entries that formed the main diagonal was then divided by the number of pixels. The Kappa coefficient was calculated using Equations (1)–(3) [1,2,46,47]: r P0 = ∑ ( Pi+ ∗ P+i ) (1) i =1 r Pc = ∑ ( Pi+ ∗ P+i ) (2) i =1 P0 − Pc KΛ = (3) 1 − Pc where r = the number of rows in the error matrix; Pij = The proportion of pixels in a row “i” and column “j”; and Pi = the fraction of the marginal sum of row “i”. 2.5. Calculation of Land Surface Temperature (LST) The Landsat-8 thermal infrared sensor (TIRS) of bands 10 and 11 and the OLI sensor of bands 2–5 were used individually to convert the raw image into a radiance spectral image (SR) by following the equations (Table 2) step by step. Table 2. Stepwise process for Land surface temperature (LST) determination. Steps Process Name Equations References 1 Spectral Radiance (SR) Lλ = 0.0003342 ∗ DN + 0.1 [16,23,48] K2 2 Brightness Temperature (TB ) TB = ln((K1 /Lλ )+ 1) − 273.15 [11,16,24] 3 NDVI NDV I = ( N IR − RED/N IR + RED ) [11,25,32]   4 Fractional Vegetation (Fv ) NDV I − NDV I min [16,25,33,38] Fv = NDV I max − NDV I min 5 Surface Emissivity (Sε) Sε = 0.004 ∗ Fv + 0.986 [16,38,40,47] Land Surface Temperature 6 LST = 1+(λ T T/B(hc))ln ε [15,38,40,49] (LST) σ B where λ is the effective wavelength (10.9 mm for a thermal band in Landsat 8 data), σ is the Boltz–Mann constant (1.38 × 10−23 J/K), h is the Plank constant (6.626 × 10−34 Js), and c is the speed of light in vacuum (2.998 × 10−8 m/s). 2.6. CA-Markov Prediction Model Analysis This model uses a stochastic Markov probability matrix to predict the transition from one state to another [14,43,50]. The study aims to analyze the various effects of urbanization on the land use and development of the city of Tianjin using a computer model known as a Markov chain model. This model was used to predict land use and development trends [13,36,51]. A conditional probability formula was used to estimate trend lines from Equations (4)–(6). S(t + 1) = Pij × S(t) (4)   P11 P12 P1n Pij =  P21 P22 P2n  (5) Pn1 Pn2 Pn3 However, ! N 0 ≤ Pij < 1 and , ∑ Pij = 1, (i, j = 1, 2, 3 . . . . . . . . . .n (6) j =1 Int. J. Environ. Res. Public Health 2023, 20, 2642 6 of 15 Because of Markov chain and cellular automata modelling, LUCC and LST’s future scenarios are calculated by projecting 2035 and 2050 using Terrset’s land use change modeler (LCM) (Clark Labs TerrSet 18.31). 3. Results 3.1. Changes in LUCC between 2005 and 2020 According to the LUCC distribution values for 2005, 2010, 2015, and 2020, the built-up area in cities has increased (Figure 3). Built-up area increased from 15.46% in 2005 to 17.80% in 2010, 19.56% in 2020, and 22.72% in 2050. In 2005, the study area included 18.43% of the lowlands; this number decreased to 12.52% by 2010, 11.89% by 2015, and 10.21% by 2020. Arable land increased rapidly from 26.10% in 2005 to 28.95% in 2020, while other land decreased from 26.47% to 26.10%. Increasing migration from villages to cities has led to an expansion of cultivated land outside prime locations. The cultivated area decreased from 10.72% in 2005 to 7.98% in 2020. Water covered 1.21% of the site in 2005, 0.92% in 2010, 0.87% in 2015, and 0.68% in 2020. An assessment of land use changes during 2005–2020 showed that farmland in the northeastern study area was converted to urban areas (mainly industrial areas). Between 2005 and 2020, built-up urban land and cropland increased by 15.45% and 1.64%, respectively, while lowland land decreased by 13.73%. These results show that about 11.45% of the lowlands have been converted into built-up areas. The LUCC changes were classified into five categories LUC with corresponding definitions (Table 3). Table 3. Land use cover (LUC) statistics in 2035 and 2050. LUC_2035 LUC_2050 Category Area %Age Area %Age urban 3001.75 18% 3204.70 19% cropland 401.69 2% 272.27 2% water 1716.04 10% 1678.71 10% forest 1982.96 12% 1946.75 11% lowland 9914.57 58% 9914.57 58% The results of all studies show that urban built-up has changed significantly over two decades. In recent decades, Tianjin has gone from a village to a city residential settlement. This transition happens between agricultural land to residential areas. Urban growth and LST are sensitive to accuracy assessment [29]. According to [52,53], a method was defined for assessing the accuracy of the classification of maps. According to the LUCC maps, the overall accuracy was 84.39% in 2005, 90.43% in 2010, and 94.11% in 2020. Kappa coefficients for the LUCC maps were 0.79, 0.87, and 0.92. The kappa coefficient should be greater than 0.75 or 0.80 to show compatibility between the classification and the reference data [54]. The United States Geological Survey (USGS) recommends using Landsat satellite images for LUCC mapping if the accuracy level is 85% [55]. Our accuracy evaluation results are consistent with those recommended in the literature. 3.2. Relationship between LUCC and LST LST is significantly affected by land use changes (LUCC). The number and distribu- tion of hotspots increase with LUCC types (especially urban expansion) [56]. A map of LST distribution was created using Landsat TM/ETM+/OLI imagery for the study area (Figure 4). There were temperature variations from 21 ◦ C to 43 ◦ C in 2005, 21.8 ◦ C to 44.3 ◦ C in 2010, 22.1 ◦ C to 44.9 ◦ C in 2010, and 22.5 ◦ C to 45.9 ◦ C in 2020. During 2005–2020, built-up urban areas had the highest average temperatures, followed by lowland, crop- land, vegetation, and water. In 2005, all LUCC categories had the most elevated average temperatures (Figure 5A). In 2005, urban built-up areas had an average LST of 38.43 ◦ C, 38.99 ◦ C in 2010, 41.86 ◦ C in 2015, and 44.80 ◦ C in 2020. During 2005–2020, the temperature in built-up urban areas LST decreased by 4.12 ◦ C but increased by a maximum of 6.82 ◦ C Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW 7 of 15 Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW 7 of 15 38.43 °C, 38.99 °C in 2010, 41.86 °C in 2015, and 44.80 °C in 2020. During 2005–2020, the Int. J. Environ. Res. Public Health 2023,38.43 °C, 38.99 °C in 2010, 41.86 °C in 2015, and 44.80 °C in 2020. During 2005–2020, 20, 2642 7 ofthe 15 temperature in built-up urban areas LST decreased by 4.12 °C but increased by a temperature in built-up urban areas LST decreased by 4.12 °C but increased by a maximum of 6.82 °C from 2010 to 2020. Lowlands had the second-highest LST for all maximum of 6.82 °C from 2010 to 2020. Lowlands had the second-highest LST for all LUCC categories during the study period. The LST for wasteland decreased by 3.38 °C LUCC2010 from categories during the study period. The LST forLSTwasteland decreased by 3.38 °C from 2005toto2020. 2010 Lowlands but did not had the changesecond-highest significantly betweenfor all ◦ 2010LUCC and categories 2020. The LSTduring for from the 2005 to 2010 study period. but The did not change significantly between 2010 and 2020. The LST for cropland was 31.04 °CLST for wasteland in 2015 decreased and increased by °C, to 31.98 3.3832.63 C from 200533.75 °C, and to 2010 °C but in ◦ Cin did 2010, cropland not change was 31.04 °C in significantly 2015 and between increased 2010 and 2020.toThe 31.98 LST°C, for32.63 °C, and cropland was33.75 31.04°C in2010, 2015 2015, and 2020, respectively. ◦ ◦ ◦ 2015,increased and and 2020,torespectively. 31.98 C, 32.63 C, and 33.75 C in 2010, 2015, and 2020, respectively. Figure 4. Figure 4. (A) Maps Maps for forland landuseuseland landcover cover(LUCC) (LUCC) classes: classes:(i) (i) water, (ii)(ii) water, vegetation, (iii)(iii) vegetation, forest, (iv) forest, Figure 4. (A) Maps for land use land cover (LUCC) classes: (i) water, (ii) vegetation, (iii) forest, (iv) urban, (iv) (v) (v) urban, barren land, barren (vi) land, cropland; (vi) cropland;(B) (B)land landsurface surfacetemperature temperature(LST) (LST) was into five was divided into five urban, (v) barren land, (vi) cropland; (B) land surface temperature (LST) was divided into five thermal categories: (i) thermal (i) <20 <20 ◦°C, (ii) 20–25 20–25 ◦°C, (iii) 25–30 25–30 ◦°C, (iv) 30–35 30–35 ◦°C, and (v) (v) >35 ◦°C of Tianjin Tianjin thermal categories: categories: (i) <20 C, (ii) °C, (ii) 20–25 °C,C, (iii) (iii) C, (iv) 25–30 °C, (iv) C, and 30–35 °C, and (v) >35 >35 °C C of of Tianjin between between the study period of 2005 to 2020, at 5-year intervals. between the the study study period period ofof 2005 2005 toto 2020, 2020, at at 5-year 5-year intervals. intervals. Figure 5. (A) Distribution of land surface temperature, and (B) proportional changes of LST during Figure 5. (A) Distribution of land surface temperature, and (B) proportional changes of LST during 2004-2019. 2004-2019. 2004–2019. From 2005 to 2020, the temperature of cropland LST decreased by 3.85 ◦ C, while it increased by 15.35% in developed areas. Vegetated areas recorded a decrease of 5.32 ◦ C between 2005 and 2010 LST but an increase of 6.83 ◦ C between 1999 and 2015. All LUCC Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW 8 of 15 From 2005 to 2020, the temperature of cropland LST decreased by 3.85 °C, while it Int. J. Environ. Res. Public Health 2023, 20, 2642 8 of 15 increased by 15.35% in developed areas. Vegetated areas recorded a decrease of 5.32 °C between 2005 and 2010 LST but an increase of 6.83 °C between 1999 and 2015. All LUCC categories recorded the lowest LST in 2010, and the average temperature in water bodies categories and vegetatedrecorded the lowest areas was LST in the lowest 2010, and overall. the average According temperature to statistics in water from LST bodies for 2005– and vegetated areas was the lowest overall. According to statistics 2020, the maximum difference between urban areas and water bodies is 14.35 from LST for °C.2005–2020, the LST maximum difference between stable urban between areas and2005 waterand bodies is 14.35 ◦ has remained relatively 2020. These C.areas are also LST has remained relatively stable between 2005 and 2020. These areas are also referred to as lowlands. Compared to urban areas, lowlands have a higher LST value. Our referred to as lowlands. Compared to urban areas, lowlands have a higher LST value. Our study came to similar conclusions. High LST values may be found in these areas due to study came to similar conclusions. High LST values may be found in these areas due to the soil composition (sand, clay, etc.). The average daily air temperature may influence the soil composition (sand, clay, etc.). The average daily air temperature may influence the the LST values at the satellite imagery data on the day the satellite imagery was taken LST values at the satellite imagery data on the day the satellite imagery was taken rather rather than the spatial values of the land use classes. To verify that LST results calculated than the spatial values of the land use classes. To verify that LST results calculated from the from the Landsat TM/ETM heat band are comparable to actual field temperatures, Landsat TM/ETM heat band are comparable to actual field temperatures, temperatures of temperatures of the various LUCC properties must be measured from field observations the various LUCC properties must be measured from field observations [57]. Considering [57]. Considering the values reported at LST, the daily mean air temperatures of the the values reported at LST, the daily mean air temperatures of the reported data (the daily reported data (the daily mean air temperature on 19 June 2005 is 27.5 °C; on 10 July 2010, mean air temperature on 19 June 2005 is 27.5 ◦ C; on 10 July 2010, it is 23.03 ◦ C; on 23 July it is 23.03 °C; on◦23 July 2015, it is 26.86 °C; and the daily mean air temperature on 11 2015, it is 26.86 C; and the daily mean air temperature on 11 August 2020 is 29.53 ◦ C) are August 2020 is 29.53 °C) are all parallel all parallel to each other (See Figure 6). to each other (See Figure 6). Figure 6. Spatial distribution map for change detection of (A) LUCC (km2 ) and (B) LST (◦ C) during Figure 6. Spatial distribution map for change detection of (A) LUCC (km2) and (B) LST (°C) during 2005–2020. 2005–2020. Pearson’s correlation analysis shows LST is statistically associated with popu- Pearson’s correlation analysis shows LST is statistically associated with lated/developed areas. Even though LST is bad for water and plants and does not have populated/developed areas. Even though LST is bad for water and plants and does not much to do with them, it is strongly linked to forested areas. In the same way, LSTs in have much to do with them, it is strongly linked to forested areas. In the same way, LSTs cities have a negative and insignificant effect on water and plants. LST and urban/built-up in cities have a negative and insignificant effect on water and plants. LST and urban/built- areas have a significant and favourable relationship, as shown by the simple correlation up areas have coefficient [51]. aInsignificant urban areas,and favourable rise a temperature relationship, may also beascaused shown by by the the simple construction correlation coefficient [51]. In urban areas, a temperature rise may also be caused of new buildings, highways, businesses, and industrial regions. Negative and insignificant by the construction of new buildings, highways, businesses, and industrial regions. Negative correlations are observed with barren land, while optimistic and negligible correlations are and insignificant marked with arablecorrelations are Pearson and cropland. observedcorrelation with barren land,results analysis whileareoptimistic andall reported for negligible correlations LUC variables and LST areindices marked with arable (Figure 7). and cropland. Pearson correlation analysis results are reported for all LUC variables and LST indices (Figure 7). Int. J. J.Environ. Int. Int. J.Environ.Res. Environ. Res.Public Res. PublicHealth Public Health2023, Health 20, 20,xx2642 2023,20, FORPEER FOR PEERREVIEW REVIEW 9 9ofof915of 15 15 Figure 7. Pearson correlation analysis of land use change and land surface temperature during 2005- Figure 7. Figure Pearsoncorrelation 7. Pearson correlationanalysis analysisofofland landuse usechange changeand andland land surface surface temperature temperature during during 2005- 2020. 2005-2020. 2020. 3.3. 3.3.Variations VariationsofofLSTLSTChanges ChangesoveroverDifferent DifferentLUCC LUCC 3.3. Variations We of estimated LSTthe Changes mean over LST Different LUCC distributions forLUCC LUCC classes over 2005–2020. During We estimated the mean LST distributions for classes over 2005–2020. During the the We study estimated period, the mean mean values LST of distributions LST increased for LUCC significantlyclasses in study period, mean values of LST increased significantly in all LUCC classes, but matters allover LUCC 2005–2020. classes, During but the study matters of LST of period, were LST weremean substantially valuesinofbuilt-up substantially higher LST higher increased andsignificantly in built-up areas bare areas ground. andin all Thebare LUCC ground. importance classes, LST but ofThe importance matters of ofLST in the built-up LST in areawere thesubstantially built-up increased from area 28.86increased higher ◦ C to 37.23infrom 28.86 °C built-up ◦ C between to 37.23 areas 2005 and°C and between bare 2020, 2005 ground. while in theThe and empty2020, importance while ground in the of LST area, in empty groundfrom theincreased they built-up area,increased area they ◦increased 21.56 C tofrom from 25.0128.86 21.56 ◦ C. Over °C tothe°C to 25.01 37.23 past °C two °C. Over2005 between decades, the thepast and 2020, two average decades, while LST in thethe average empty distribution LST area, inground built-up distribution and they in built-up increased bare-ground from regionsandhasbare-ground 21.56 °C to risen regions by 25.01 about 9 ◦C °C. Over has ◦ risen by about 9 °C and 4 °C, respectively. The LST distribution in water bodies and the andpast 4 C,two decades, The respectively. the LST average LST distribution distribution in water bodiesin built-up and bare-ground and vegetated areas have alsoregions vegetated changed. areas havethe9 also changed. In 2000, the mean LST ◦ C, for vegetated areas was 21.31 has risen Inby 2000, about mean °C and LST for vegetated 4 °C, respectively.areas wasLST The 21.31 but it is in distribution expected water to reachand bodies °C, but◦ it 25.98 vegetated C is by expected 2020.have areas Theto also reachchanged. LST of25.98 water °Cbodies by In 2020. 2000, The the LST increased mean of LST fromwater tobodies 20for 24.45 ◦ C. The following increased vegetated areasfrom was2021.31 to 24.45 figure °C. (Figure The8) following briefly figure describes (Figure the 8) changes briefly in LUCCdescribes types °C, but it is expected to reach 25.98 °C by 2020. The LST of water bodies increased the and changes their in relativeLUCC types impacts on 20 from and landtheir relative surface impacts on land surface temperature. temperature. to 24.45 °C. The following figure (Figure 8) briefly describes the changes in LUCC types and their relative impacts on land surface temperature. Figure 8. Figure Shows classified 8. Shows classified maps mapsof ofLUC LUCand andLST LSTforfor thethe year 2020, year andand 2020, predicted maps predicted of 2020, maps 2035, of 2020, and 2050. 2035, and 2050. Figure 8. Shows classified maps of LUC and LST for the year 2020, and predicted maps of 2020, 2035, and 2050. Int. J. Environ. Res. Public Health 2023, 20, 2642 10 of 15 3.4. Validation of Predicted LUCC and LST Scenarios To validate the accuracy of the predicted values, we first used the CA-Markov model to estimate the LUCC and LST for 2020 (Figure 8). Based on various kappa parameters, the predicted and estimated maps were compared using the land use Change Modeler in Clark Lab’s Terrset software. The average error value for all parameters during the comparison was about 12.86%, and all kappa parameters, percentage of accuracy, and total kappa values were above 0.80. 3.5. Predicted LUCC for 2035 and 2050 We could predict the scenario for 2035 and 2050 based on the classified maps for the study period. According to the predicted LUCC map, the growth of urban areas will be concentrated by 37% in the northwestern and central regions if the trend of the building continues without planned actions. Urban areas will replace the lowlands and vegetation cover. Vegetation cover has decreased by 9.62% from 12.82% in 2020. Based on the study scenario, LUCC would face a 20.51% increase in developed land, followed by a significant decrease in lowlands, vegetation cover, and water bodies of 10.87%, 9.62%, 8.32%, and 2.45%, respectively (Figure 8). The category-wise land use statistics for the forecast years are shown in the following table: Ecosystem services, urban health, and thermal characteristics may be affected by decreased vegetation cover and increased urbanization. If unplanned urban expansion continues, the environmental, economic, and medical problems will increase significantly. A proper land use plan, the protection of water bodies, and the reforestation of forests are needed to make Tianjin city more environmentally sustainable. By forecasting LST for 2035 and 2050, the simulation showed that higher temperatures will occur in the built-up areas in the northwest and central parts of the country (Figure 8), ranging from 41.56 ◦ C to 44.34 ◦ C in 2035 and 2050, respectively. We divide the temperature zone into five classes to estimate how much area is covered by each temperature range (Table 4). Based on the projections, LST has increased over the past two decades (2005–2020), with urban areas influencing the prevalence of LST. UHI effects will increase as urban areas and vegetation cover decrease. It would be possible to explain the temperature increase without urbanization by climate change, greenhouse effects, and surface features. The LST prediction highlights the real risks of the temperature rise in the trend, including higher UHI effects. A combination of energy use, greenhouse gas emissions, and air pollution contribute to the UHI effect. It threatens aquatic systems (rivers, lakes, ponds, streams, and oceans) and human health. Human health is primarily harmed by increased greenhouse gas emissions, which affect urban health and reduce the urban environment’s sustainability [58]. Table 4. Land surface temperature (LST) statistics in 2035 and 2050. LST-2035 LST-2050 Category Area %Age Area %Age <20 ◦C 1839.763 16.85% 1851.812 16.96% 20–25 ◦ C 4933.693 45.18% 5056.619 46.31% 25–30 ◦ C 4118.498 37.72% 3859.214 35.34% 30–35 ◦ C 26.86548 0.25% 150.4464 1.38% >35 ◦ C 0.113955 0.00% 0.842528 0.01% 3.6. Limitations of the CA-Markov Model The prediction of LUCC and LST can be improved using the CA-Markov model if the previous LUCC and LST patterns are consistent. As a result, CA-Markov models do not provide accurate spatial predictions for raster datasets [59]. Since influential factors can be directly determined between CA-Markov and other factors, CA-Markov is based on a probability matrix [60]. Given the relative importance of the different variables in identifying the most important variables, it is essential to note that the CA-Markov model Int. J. Environ. Res. Public Health 2023, 20, 2642 11 of 15 generates training patterns and automatically begins training after receiving inputs from the strata. The input parameters are not individually weighted according to established standards [61]. Since urbanization, the loss of green space and increase in surface tempera- tures are primarily influenced by human activities and conscious decisions at regional to metropolitan scales; it is impossible to predict them accurately. It is essential to recognize that dynamic models have some limitations. Still, they help develop hypotheses and make decisions about changes in land cover or surface temperatures in any given area, regardless of their rules. In recent years, LUCC and LST variability and predictive maps have emerged as one of the best tools for managing and mitigating vital natural resources. 4. Discussion Tianjin’s rapid urbanization and development between the 1990s and 2020s signif- icantly altered the LUCC landscapes caused by farmland separation and reduced total vegetation cover [25,33]. The city’s urban development also resulted in the establishment of new industries and residential areas. Rapid vegetation cover loss affects an area’s natural cooling effect [29,62]. Some factors contributing to this phenomenon are vegetation shading and transpiration. To amplify this, LST and NDVI have shown that VC, due to its cooling ef- fect, serves as a sink in an urban heat island [11,63]. Rapid vegetation cover loss has several consequences for an area’s natural cooling effect. It has the potential to eventually eliminate the processes that regulate surface transpiration and evaporation [11,17,56,64]. Urbaniza- tion leads to distorted construction, reducing soil infiltration and increasing surface runoff. As a result, the water table and groundwater table decrease. Evapotranspiration is not adequately realized due to these two factors. Climate change leads to a deterioration of the water balance [49]. Climate variables such as daily maximum and minimum temperatures are affected by changes in land use. Surface albedo changes due to changes in land use. Therefore, land use changes disturb the balance of Earth’s radiation [65]. An important factor in reducing air temperatures is the conversion of wetlands to agricultural land with high albedo [66]. Although the impact of this phenomenon on the LST of various types of plants is less than that of urban tree cover and gardens, studies have shown that it still contributes to the overall reduction of the area’s natural cooling effect [32,67]. The impact of various types of urban vegetation, water bodies, and forests on the LST varies according to their proportional area [23,37]. In urban areas, vegetation plays a vital role in controlling or mitigating temperature. Evaporation from urban water bodies contributes to moisture accumulation in the surrounding air. According to studies, these bodies regulate the LST in residential areas. It is also known that urban areas contribute to the development of intricate heat flows within these regions [46,68]. Various private and public entities have worked together to revitalize large tracts of land for industrial, commercial, and residential development. Traditional wooden structures have been demolished and replaced with tall structures made of non-evaporative materials such as glass, concrete, and aluminium. These materials can directly impact heat flows in urban areas [8,10]. According to studies, urban areas in China are more vulnerable to severe LST than rural areas. LST has risen due to the government’s decision to convert agricultural and forest land into urban areas [38,69]. The government has relocated factories and businesses to the outskirts of cities to improve their efficiency. These facilities are typically found in developed areas. Before the development of urban areas, forests and vegetation were regarded as buffer zones between rural and urban areas, absorbing excess heat generated by factories and automobiles [3,29,40]. According to the scientific literature, the cooling effect of LUCC is well-matched to the expected warming effect caused by the physical interaction of the Indian region and its surroundings [32]. For example, the maximum cooling contribution from forested areas is 0.27%, while the minimum cooling effect is 0.06%. The most negligible difference between the surface temperature and the impervious surface is the primary reason why vegetation contributes the least to the cooling effect. The greatest cooling effect, on the other hand, is observed when forested areas are converted into water bodies. This is due to the fact that the Int. J. Environ. Res. Public Health 2023, 20, 2642 12 of 15 contribution of land cover to cooling is negligible in various areas, such as urban areas, water bodies, and vegetation. The results of the study revealed that the built-up area in the southeastern and central port areas will continue growing. The paper discussed the various effects of the LUCC on the Tianjin city’s development. The study used the CA model and Geographic Information Systems to analyze the data. The results of the analysis helped improve the Tianjin city’s planning process. In addition, the paper discussed the use of remote sensing tools for improving the urban planning process. 5. Conclusions The objective of this study was to analyze the influence of LUCC on land surface temperature (LST) in a large urban area of Tianjin. Data from RS were used to observe the area’s various socioeconomic and development parameters. The study also used the CA- Markov model and Pearson correlation coefficient to evaluate the contribution of landscape dynamics to temperature. A 5.94% increase in built-up area was found to increase the temperature by 1.5%. However, the increase in vegetation cover by 10% showed a negative correlation. In addition, the study concluded that LUCC has a cooling effect of about 1.40 ◦ C in the city. The average warming effect of LUCC on the UHI is about 0.5%. On the other hand, the cooling effect of LUCC compared to the shifts in the reverse direction is 0.11%. The positive contribution of LUCC to the UHI was higher than the negative one. Urban development and infrastructure planning should be further targeted to minimize the impacts of climate change. In addition to improving water bodies and parks, other measures, such as the establishment of green spaces and linear planting of woody plants, should also be implemented. The study found that further research is needed to analyze the impact of land use change on the climate of regions and cities. As more areas are affected by climate change, the government and private sector must work together to develop effective cooling strategies. Environmental education should be made accessible to promote the development of ecological resources. This needs effective urban planning and green policies to address the increasing thermal stress. In addition, a quantitative analysis of these parameters needs to be conducted. Although the study found that urbanization directly impacts land surface temperature, it is not yet clear how the effects of this process are related to the other factors. The practical application of the study provides essential guidance for urban landscape planning. It shows how landscape connectivity between impervious and green areas can affect LST. Future research should also address infrastructure stress and public health issues associated with rapid urbanization. Author Contributions: Conceptualization, N.U., M.A.S., Y.Z. and Y.H.; data curation, N.U., M.A.S., M.D., S.G., S.T. and T.Z.; formal analysis, N.U., M.A.S., S.T., Z.K. and M.D.; funding acquisition, Y.H. and Y.Z.; methodology, N.U., M.A.S., S.T., I.A.K. and S.G.; project administration, Y.H. and Y.Z.; software, N.U. and M.A.S.; supervision, Y.H. and Y.Z.; visualization, N.U., M.A.S., T.Z., Z.K. and Y.H.; writing—original draft, N.U. and M.A.S.; writing—review and editing, N.U., M.A.S., I.A.K. and Y.H. All authors have read and agreed to the published version of the manuscript. Funding: Reconstructing the Architecture System based on the coherence mechanism of “Architecture- human-environment” in the Chinese context, Key project of National Natural Science Foundation of China, grant number 52038007, 2021-01-2025-12. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: On request, the authors will provide the data from this study. Acknowledgments: The authors wish to express his appreciation and gratitude to the anonymous reviewers and editors for their insightful comments and suggestions to improve the paper’s quality. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2023, 20, 2642 13 of 15 References 1. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. Global consequences of land use. Science 2005, 309, 570–574. [CrossRef] [PubMed] 2. Beckline, M.; Yujun, S.; Yvette, B.; John, A.B.; Mor-Achankap, B.; Saeed, S.; Richard, T.; Wose, J.; Paul, C. Perspectives of remote sensing and GIS applications in tropical forest management. Am. J. Agric. For. 2017, 5, 33–39. [CrossRef] 3. Coseo, P.; Larsen, L. How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago. Landsc. Urban Plan. 2014, 125, 117–129. [CrossRef] 4. Tonkaz, T.; Çetin, M. Effects of urbanization and land-use type on monthly extreme temperatures in a developing semi-arid region, Turkey. J. Arid Environ. 2007, 68, 143–158. [CrossRef] 5. Carlson, T.N.; Arthur, S.T. The impact of land use—Land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective. Glob. Planet. Chang. 2000, 25, 49–65. [CrossRef] 6. Walawender, J.P.; Szymanowski, M.; Hajto, M.J.; Bokwa, A. Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ data. Pure Appl. Geophys. 2014, 171, 913–940. [CrossRef] 7. Zhang, Q.; Su, S. Determinants of urban expansion and their relative importance: A comparative analysis of 30 major metropolitans in China. Habitat Int. 2016, 58, 89–107. [CrossRef] 8. Huang, Y.; Qiu, Q.; Sheng, Y.; Min, X.; Cao, Y. Exploring the Relationship between Urbanization and the Eco-Environment: A Case Study of Beijing. Sustainability 2019, 11, 6298. [CrossRef] 9. Morabito, M.; Crisci, A.; Messeri, A.; Orlandini, S.; Raschi, A.; Maracchi, G.; Munafò, M. The impact of built-up surfaces on land surface temperatures in Italian urban areas. Sci. Total Environ. 2016, 551, 317–326. [CrossRef] 10. Ullah, S.; Tahir, A.A.; Akbar, T.A.; Hassan, Q.K.; Dewan, A.; Khan, A.J.; Khan, M. Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region. Sustainability 2019, 11, 5492. [CrossRef] 11. Amir Siddique, M.; Wang, Y.; Xu, N.; Ullah, N.; Zeng, P. The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050). Remote Sens. 2021, 13, 4697. [CrossRef] 12. Huff, F.; Changnon, S., Jr. Climatological assessment of urban effects on precipitation at St. Louis. J. Appl. Meteorol. 1972, 11, 823–842. [CrossRef] 13. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 2004, 90, 434–440. [CrossRef] 14. Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 2003, 24, 583–594. [CrossRef] 15. Singh, P.; Kikon, N.; Verma, P. Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain. Cities Soc. 2017, 32, 100–114. [CrossRef] 16. Zullo, F.; Fazio, G.; Romano, B.; Marucci, A.; Fiorini, L. Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Sci. Total Environ. 2019, 650, 1740–1751. [CrossRef] [PubMed] 17. Tariq, A.; Riaz, I.; Ahmad, Z.; Yang, B.; Amin, M.; Kausar, R.; Andleeb, S.; Farooqi, M.A.; Rafiq, M. Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environ. Earth Sci. 2019, 79. [CrossRef] 18. Liu, L.; Zhang, Y. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sens. 2011, 3, 1535–1552. [CrossRef] 19. Ren, G.; Chu, Z.; Chen, Z.; Ren, Y. Implications of temporal change in urban heat island intensity observed at Beijing and Wuhan stations. Geophys. Res. Lett. 2007, 34. [CrossRef] 20. Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China. Remote Sens. 2012, 4, 2033–2056. [CrossRef] 21. Xu, J.; Zhao, Y.; Sun, C.; Liang, H.; Yang, J.; Zhong, K.; Li, Y.; Liu, X. Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019. Remote Sens. 2021, 13, 1019. [CrossRef] 22. Quan, J.; Chen, Y.; Zhan, W.; Wang, J.; Voogt, J.; Wang, M. Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model. Remote Sens. Environ. 2014, 149, 33–46. [CrossRef] 23. Liu, W.; Ji, C.; Zhong, J.; Jiang, X.; Zheng, Z. Temporal characteristics of the Beijing urban heat island. Theor. Appl. Climatol. 2007, 87, 213–221. [CrossRef] 24. Wu, Q.; Tan, J.; Guo, F.; Li, H.; Chen, S. Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China. Remote Sens. 2019, 11, 3021. [CrossRef] 25. Ullah, N.; Siddique, M.A.; Ding, M.; Grigoryan, S.; Zhang, T.; Hu, Y. Spatiotemporal Impact of Urbanization on Urban Heat Island and Urban Thermal Field Variance Index of Tianjin City, China. Buildings 2022, 12, 399. [CrossRef] 26. National Bureau of Statistics of China. China Statistic Year Book 2012; National Bureau of Statistics of China: Beijing, China, 2012. 27. Feng, H.; Liu, H.; Wu, L. Monitoring the relationship between the land surface temperature change and urban growth in Beijing, China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4010–4019. [CrossRef] 28. Ding, H.; Shi, W. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City. Int. J. Remote Sens. 2013, 34, 5503–5517. [CrossRef] Int. J. Environ. Res. Public Health 2023, 20, 2642 14 of 15 29. Zhang, X.; Wang, D.; Hao, H.; Zhang, F.; Hu, Y. Effects of Land Use/Cover Changes and Urban Forest Configuration on Urban Heat Islands in a Loess Hilly Region: Case Study Based on Yan’an City, China. Int J Env. Res Public Health 2017, 14, 840. [CrossRef] 30. Tali, J.; Emtehani, M.; Murthy, K.; Nagendra, H. Future Threats to CBD: A Case Study of Bangalore CBD. N. Y. Sci. J. 2012, 5, 22–27. 31. Sannigrahi, S.; Bhatt, S.; Paul, S.K.; Sen, S.; Rahmat, S.; Uniyal, B. The Spatial Variations and Hotspots of Ecosystem Service Values in India During 1985–2005. In Proceedings of the AGU Fall Meeting Abstracts, New Orleans, LA, USA, 11–15 December 2017. 32. Macarof, P.; Statescu, F. Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environ. Sustain. Dev. 2017, 11, 141–150. [CrossRef] 33. Li, P.; Siddique, M.A.; Fan, B.; Huang, H.; Liu, D. Effects of land surface type changes on urban heat island: A case study of Chaoyang District, Beijing. J. Beijing For. Univ. 2020, 2020, 42. [CrossRef] 34. Khan, T.U.; Mannan, A.; Hacker, C.E.; Ahmad, S.; Amir Siddique, M.; Khan, B.U.; Din, E.U.; Chen, M.; Zhang, C.; Nizami, M. Use of GIS and remote sensing data to understand the impacts of land use/land cover changes (LULCC) on snow leopard (Panthera uncia) habitat in pakistan. Sustainability 2021, 13, 3590. [CrossRef] 35. Chen, X.-L.; Zhao, H.-M.; Li, P.-X.; Yin, Z.-Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133–146. [CrossRef] 36. Buyantuyev, A.; Wu, J. Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landsc. Ecol. 2010, 25, 17–33. [CrossRef] 37. Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483. [CrossRef] 38. Wang, R.; Hou, H.; Murayama, Y.; Derdouri, A. Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China. Remote Sens. 2020, 12, 440. [CrossRef] 39. Sexton, J.O.; Song, X.-P.; Huang, C.; Channan, S.; Baker, M.E.; Townshend, J.R. Urban growth of the Washington, DC–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sens. Environ. 2013, 129, 42–53. [CrossRef] 40. Guha, S.; Govil, H.; Dey, A.; Gill, N. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur. J. Remote Sens. 2018, 51, 667–678. [CrossRef] 41. Li, X.; Li, W.; Middel, A.; Harlan, S.L.; Brazel, A.J.; Turner Ii, B. Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors. Remote Sens. Environ. 2016, 174, 233–243. [CrossRef] 42. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013. 43. Hansen, M.C.; Loveland, T.R. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 2012, 122, 66–74. [CrossRef] 44. Guha, S.; Govil, H.; Mukherjee, S. Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. J. Appl. Remote Sens. 2017, 11, 036020. [CrossRef] 45. Debbage, N.; Shepherd, J.M. The urban heat island effect and city contiguity. Comput. Environ. Urban Syst. 2015, 54, 181–194. [CrossRef] 46. Amiri, R.; Weng, Q.; Alimohammadi, A.; Alavipanah, S.K. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens. Environ. 2009, 113, 2606–2617. [CrossRef] 47. Almazroui, M.; Islam, M.N.; Jones, P. Urbanization effects on the air temperature rise in Saudi Arabia. Clim. Chang. 2013, 120, 109–122. [CrossRef] 48. Cristóbal, J.; Jiménez-Muñoz, J.; Prakash, A.; Mattar, C.; Skoković, D.; Sobrino, J. An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band. Remote Sens. 2018, 10, 431. [CrossRef] 49. Zhang, Y.; Yu, T.; Gu, X.; Zhang, Y.; Chen, L. Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. J. Remote Sens. 2006, 10, 789. 50. Connors, J.P.; Galletti, C.S.; Chow, W.T. Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc. Ecol. 2013, 28, 271–283. [CrossRef] 51. Firozjaei, M.K.; Sedighi, A.; Kiavarz, M.; Qureshi, S.; Haase, D.; Alavipanah, S.K. Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery. Remote Sens. 2019, 11, 1966. [CrossRef] 52. Saadat, H.; Adamowski, J.; Bonnell, R.; Sharifi, F.; Namdar, M.; Ale-Ebrahim, S. Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS J. Photogramm. Remote Sens. 2011, 66, 608–619. [CrossRef] 53. Congalton, R.G. Remote sensing and geographic information system data integration: Error sources and. Photogramm. Eng. Remote Sens. 1991, 57, 677–687. 54. Wondrade, N.; Dick, Ø.B.; Tveite, H. GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia. Environ. Monit. Assess. 2014, 186, 1765–1780. [CrossRef] [PubMed] 55. Bakr, N.; Weindorf, D.; Bahnassy, M.; Marei, S.; El-Badawi, M. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Appl. Geogr. 2010, 30, 592–605. [CrossRef] Int. J. Environ. Res. Public Health 2023, 20, 2642 15 of 15 56. Mumtaz, F.; Arshad, A.; Mirchi, A.; Tariq, A.; Dilawar, A.; Hussain, S.; Shi, S.; Noor, R.; Noor, R.; Daccache, A. Impacts of reduced deposition of atmospheric nitrogen on coastal marine ecosystem during substantial shift in human activities in the twenty-first century. Geomat. Nat. Hazards Risk 2021, 12, 2023–2047. [CrossRef] 57. Mallick, J.; Kant, Y.; Bharath, B. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union 2008, 12, 131–140. 58. Kafy, A.-A.; Rahman, M.S.; Hasan, M.M.; Islam, M. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sens. Appl. Soc. Environ. 2020, 18, 100314. [CrossRef] 59. Li, J.; Zhang, C.; Zheng, X.; Chen, Y. Temporal-Spatial Analysis of the Warming Effect of Different Cultivated Land Urbanization of Metropolitan Area in China. Sci. Rep. 2020, 10, 2760. [CrossRef] [PubMed] 60. Van Gerven, M.; Bohte, S. Artificial neural networks as models of neural information processing. Front. Comput. Neurosci. 2017, 11, 114. [CrossRef] 61. Shatnawi, N.; Abu Qdais, H. Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling. Int. J. Remote Sens. 2019, 40, 3968–3983. [CrossRef] 62. Niu, L.; Tang, R.; Jiang, Y.; Zhou, X. Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas. Sustainability 2020, 12, 478. [CrossRef] 63. Luo, X.; Li, W. Scale effect analysis of the relationships between urban heat island and impact factors: Case study in Chongqing. J. Appl. Remote Sens. 2014, 8, 084995. [CrossRef] 64. Yang, P.; Ren, G.; Liu, W. Spatial and temporal characteristics of Beijing urban heat island intensity. J. Appl. Meteorol. Climatol. 2013, 52, 1803–1816. [CrossRef] 65. Myhre, G.; Myhre, A. Uncertainties in radiative forcing due to surface albedo changes caused by land-use changes. J. Clim. 2003, 16, 1511–1524. [CrossRef] 66. Chen, W.; Zhang, Y.; Pengwang, C.; Gao, W. Evaluation of urbanization dynamics and its impacts on surface heat islands: A case study of Beijing, China. Remote Sens. 2017, 9, 453. [CrossRef] 67. Pal, S.; Ziaul, S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 2017, 20, 125–145. [CrossRef] 68. Tian, G.; Wu, J.; Yang, Z. Spatial pattern of urban functions in the Beijing metropolitan region. Habitat Int. 2010, 34, 249–255. [CrossRef] 69. Yu, Z.; Xu, S.; Zhang, Y.; Jorgensen, G.; Vejre, H. Strong contributions of local background climate to the cooling effect of urban green vegetation. Sci. Rep. 2018, 8, 6798. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.