Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City

Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City Pardeep Kumar, Pratyush Verma, Bhawna Yadav, Saumitra Mukherjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3873203/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land Surface Temperature (LST) is a crucial parameter for assessing the thermal comfort of urban residents. This study investigates the impact of land use/land cover changes on the variations in land surface temperature from 1989 to 2019 at 10-year intervals in Hyderabad city, Telangana. The mono window and split window algorithms were employed to derive LST, while the contribution index was utilized to analyze changes in the contribution of land use/land cover (LULC) to LST. The built-up area has witnessed a notable increase from 35.81–56.49%, accompanied by corresponding decreases in barren land (42.73–33.42%), vegetation (19.39–8.20%), and water bodies (2.07% to 8.20). The study further indicates that barren land significantly contributes to LST, with a decreasing trend observed from 1989 to 2019. The mitigating effects of water bodies (-0.14 to -0.1) and vegetation (-0.42 to -0.06) on LST have diminished over the same period. Additionally, a decline in Normalized Difference Vegetation Index (NDVI) for vegetation and Normalized Difference water Index (NDWI) for water bodies reflects increased stress and pollution in their respective LULC areas. Furthermore, the decrease in the Normalized Difference Barren Land Index (NDBaI) and Normalized Difference Built-up Index (NDBI) depicts urban expansion and the transformation of primary barren land to cultivation. This research enhances our understanding of how shifting landscapes influence a material's surface energy budget. Analyzing the interplay between land cover and incoming radiation throughout the day provides insights into the effects of climate change. Land surface temperature Land use/ land cover Support Vector Machine Indices Hyderabad city Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Climate change has various impacts on the world. One of these impacts is the occurrence of severe heat storms for extended durations during the summer. These events create intensely uncomfortable conditions in urban areas. In recent years, numerous scientific studies have been conducted to examine the nature, temperature variations, and patterns of change on a global scale, encompassing local, regional, and global levels (Khan and Chatterjee, 2019). The climatic changes observed in urban centers result from both natural processes and human-induced alterations to the Earth's surface and atmospheric properties (Henderson and Gornitz, 1984). In the late 1950s, urban areas accounted for 30% of the global population, a percentage that grew to 55% by 2018 and is projected to reach 68% by 2050 (UN, 2014; UN, 2018). The global population grew only six times in the last 200 years, and the urban population increased 128 times (Schell et al., 1993). This rapid urbanization has significantly impacted society, the environment, and humanity as a whole. Urbanization has had a profound effect on urban societies and living standards. It has also placed substantial pressure on the environment to sustain the growing urban population. Numerous studies have highlighted that urbanization has been a driving force behind the social and economic development of urban centers, transforming them into efficient, convenient, and innovative marketplaces, albeit with consequences for surrounding suburban and rural areas (Seto et al., 2011 ; Cui and Shi, 2012 ; Liu and Diamond, 2005 ). These studies have shown that urbanization has led to increased demand for housing, resulting in subpar living conditions, inadequate housing, reduced natural vegetation due to decreased permeable surfaces, altered surface albedo, loss of biodiversity, increased water and air pollution, and diminished water supply (Ohwo and Abotutu, 2015 ; Hahs et al., 2009 ; Zhao et al., 2006 ; Cui and Shi, 2012 ). Additionally, it has altered land use and land cover patterns, modifying natural biogeochemical and water cycles, fragmenting natural habitats, leading to biodiversity loss, rapid depletion of productive farmland, and causing local weather variations due to changes in surface albedo (Seto et al., 2011 ; Radeloff et al., 2010; Jago-on et al., 2009 ; Yuan, 2008). Urban development, combined with modifications to the lithosphere and atmosphere, has inadvertently resulted in climatic changes. Dense urban materials absorb heat and create impermeable surfaces, while tall buildings alter albedo, trapping more radiation, increasing air stagnation, and leading to heightened air pollution and the formation of Cloud Condensation Nuclei (CCN). Additionally, human activities release heat, further contributing to the urban heat environment (Oke, 1987 ). This inadvertent impact of urbanization and climatic changes, along with infrastructural development, can render urban environments vulnerable. The Urban Heat Island (UHI) effect, has become a growing concern for urban climatologists (Doan and Kusaka, 2015 ). The UHI effect is characterized by elevated air temperatures in urban landscapes surrounded by cooler temperatures in suburban or rural areas. The heat island effect is most prominently noticed around the minimum temperature epoch, when the temperature curve is more or less flat (Bahl and Padmanabhamurty 1979 ; Gangadharan et al. 1999 . Oke (1995) defined UHI as the temperature differences between the urban and surrounding rural stations. Urban Heat Island (UHI), a consequence of urbanization was first observed by (Howard, 1818 ; Oke, 1982 ). Numerous studies have indicated that various properties of the UHI are directly linked to the structural features of the urban landscape. As Bridgman et al. ( 1995 ) pointed out, "buildings impact the urban environment in five significant ways: by replacing vegetated areas, generating artificial heat, introducing block-like buildings, emitting pollutants, and draining rainfall rapidly." In recent years, extensive research has been conducted on the UHI in diverse climate zones to investigate the UHI phenomenon's mechanisms and the impact of urbanization on UHI effects in cities (Doan and Kusaka, 2015 ). Most studies encompass urban modifications to local climate, such as UHI effects and changes in LULC, at an urban scale. This implies that urban temperatures tend to be higher in the urban core compared to its suburban natural surroundings, particularly at night (Oke and Cleugh, 1987 ; Arnfield, 2003 ; Parker, 2010 ). Chen et al. (2014) identified factors contributing to the formation of Urban Heat Islands (UHI): (a) alterations in the land surface physics due to extensive urbanization, resulting in changes to urban albedo, thermal emissivity, and material conductivity; (b) human-induced heat generation from various activities; (c) reduced surface evapotranspiration caused by urban concretization; and (d) modifications in flow characteristics and near-surface atmospheric processes due to factors like a low sky view factor (SVF) and urban geometries (Kim and Baik, 2005 ). The ongoing development poses a threat to the conservation value of protected areas, potentially leading to a decline in biodiversity (Helmers, 2010 ). Urban regions experience heightened heat levels due to multiple factors, with some, such as climate and topography, beyond human control. However, there are two modifiable factors—vegetation quantity and surface color—that significantly contribute to the additional heat associated with human activities (Akbari, 2009 ). Several studies have employed different approaches to represent urban climate and changes in Land Surface Temperature (LST), which can be categorized into ground-based and satellite observations. Traditional ground-based observations rely on weather stations to estimate variations in near-surface air temperature between rural and urban areas (Eludoyin et al., 2013 ; Vancutsem et al., 2010 ; Rao, 1972 ). Ground-based measurements require a significant number of weather stations within the study area to yield meaningful results. In contrast, satellite observations offer a more promising alternative, as they provide LST data at suitable temporal and spatial resolutions for studying UHI (Sobrino et al., 2012 ; Weng, 2009 ; Hamdi and Schayes, 2008 ; Qiao et al., 2013 ). Thermal infrared (TIR) imagery has been employed for detecting urban sprawl and UHI intensities (Landsat Project Science Office, 2002 ; Rao, 1972 ; Gallo et al., 1995). NDVI values serve as indicators of vegetation density, facilitating the assessment of changes in (LULC) patterns to analyze UHI intensities. Numerous studies have explored the relationship between vegetation density and LST, providing insights into estimating UHI intensity using Landsat data (Estoque et al., 2017 ; Bokaiea et al., 2016 ; Chen et al., 2006 ; Omran, 2012 ; Kawashima, 1994 ;). Study area Hyderabad is both the capital and the largest city of Telangana. The study area encompasses the region surrounding Hyderabad, situated at latitude 17° 16' 56'' to 17° 33' 40'' N and longitude 78° 14' 38'' to 78° 38' 28'' E, covering an area of 686 square kilometers (Fig. 1 ). Hyderabad is characterized by hilly terrain with numerous artificial lakes and is situated on the banks of Musi, which is a tributary of Krishna River. With a population of 6.9 million people (INDIA, 2011 ) it ranks as the fourth-most populous city in the country. Situated on a sloping terrain of pink and grey granite the city has an average altitude of 542 meters (Ramachandraia, 2013). Some of the notable artificial lakes in the area include Hussain Sagar, Osman Sagar, and Himayat Sagar. Hyderabad experiences a tropical wet and dry climate. The region falls within the moderate (26°C-32°C) to strong heat stress (32°C to 38°C) range according to the Universal Thermal Climate Index. It maintains an annual average temperature of 26.6℃, with monthly average temperatures ranging between 21℃ to 33℃. Summers are characterized by hot and humid conditions, often exceeding 40℃ in May. The months of December and January are the coldest, with temperatures occasionally dropping to 10℃ (Norman et al., 1995 ). On June 2, 1966, Hyderabad recorded its highest temperature of 45.4℃, while the lowest temperature ever recorded was 6.1℃ on January 8, 1946. The majority of Hyderabad's rainfall occurs during the southwest monsoon period between June and September. The heaviest 24-hour rainfall recorded was 241.5 mm on August 24, 2000 (IMD). Annually, the city receives 2,731 hours of sunshine, with the highest daily sunlight exposure occurring in February (Yimene and Minda, 2004). Cloud cover varies throughout the year, with the months from June to October experiencing around 50% cloud cover, while January to March has significantly less at only 25%. Clear skies are often observed in January, February, and March. The monsoon months of July, August, and September are characterized by very high humidity levels exceeding 75%, while the months of March, April, and May are dry and experience lower humidity levels ranging from 25–30% ( http://hyderabad-india-online.com/2011/06/climatic-conditions-hyderabad ). Wind patterns in Hyderabad vary by season, with southern winds prevailing for three months (February to April), western winds for four and a half months (mid-May to September end), and eastern winds for four months (October to January). Wind speeds are higher during the windier months of June, July, and August, with average speeds of 10 miles per hour, while the rest of the months experience calmer conditions with average wind speeds of 6.3 miles per hour ( https://weatherspark.com/y/109450/Average-Weather-in-Hyderabad-India-Year-Round ). Material and methods Landsat 8 provides 11 multi-spectral bands, while Landsat 4–5 offers 7 bands, categorized into visible, near-infrared, shortwave radiation, and thermal infrared bands. Landsat 4–5 is equipped with the Multispectral Scanner (MSS) system and the Thematic Mapper (TM), while Landsat 8 utilizes the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (Table 1 ). The data for the years 1989, 1999, 2009, and 2019 were obtained from the United States Geological Survey (USGS) earthexplorer (Table 1 ). These bands were then stacked using ERDAS IMAGINE software and utilized to calculate Land Surface Temperature (LST) through ArcGIS software. Table 1 Satellite Data for the Study Area Satellite Date Time Path/Row Cloud Cover Landsat 8 OLI 25-02-2019 05:09 144/048 0 Landsat 4–5 TM 13-02-2009 04:55 144/048 0 Landsat 4–5 TM 18-02-1999 04:48 144/048 0 Landsat 4–5 TM 22-02-1989 04:49 144/048 0 Support Vector Machine Classification SVM is a well-established supervised machine learning method frequently used for classification and regression tasks. SVM has proven highly effective for classifying high-dimensional data. In its decision function, SVM is a binary classifier based on supervised learning which gives better performance than other classifiers. SVM classifies between two classes by constructing a hyperplane in high-dimensional feature space which can be used for classification (Khan and Syed, 2015 ). This technique employs a kernel trick to transform data and determine an optimal boundary between potential outcomes. SVM can utilize both linear and non-linear kernels. SVM classification was conducted using ArcGIS software. Initially, signature files (training samples) were created based on visual feature identification. These signature files served as references for training the SVM classifier (source: ArcGIS SVM Classifier) and evaluating classification accuracy (ArcGIS Accuracy Assessment Points). The final classification was performed using the "classify raster" tool in ArcGIS (ArcGIS Classify Raster). A total of 55 sample points were employed to assess classification accuracy (Compute Confusion Matrix). Indices Used The following indices were computed: NDVI = (NIR - Red) / (NIR + Red) (Eq. 1) NDWI = (Green - Red) / (Green + Red) (Eq. 2) NDBI = (SWIR – NIR) / (SWIR + NIR) (Eq. 3) NDBaI = (NIR – SWIR1) / (NIR + SWIR1) (Eq. 4) Where: Red - central wavelength 0.560µm Green - central wavelength 0.655µm NIR - central wavelength 0.865µm SWIR - central wavelength 1.610µm SWIR1 - central wavelength 2.200µm Two such algorithms employed estimating land surface temperature for our present study are the Mono Window Algorithm and the Split Window Algorithm: Mono Window Algorithm The Mono-window algorithm developed by (Qin et al., 2015) (Wang et al., 2015 ) was used for LST calculation using Landsat TM data and can be expressed as follows: Ts = [a10(1 − C10 − D10) + (b10 (1 − C10 − D10) + C10 + D10) T10 − D10Ta] / C10 C10 = τ10ε10 D10 = (1 − τ10) [1+(1 − ε10) τ10 (Eq. 5) Here, a10 = -62.7182 and b10 = 0.4339 represent model constants for temperature variations from 0 to 50°C. ε10 is the emissivity for Landsat 8 OLI-TIRS, determined from the NDVI, and τ10 is the atmospheric transmittance for Landsat 8 OLI-TIRS. Ts is the effective mean atmospheric temperature for the tropical model, with near-surface air temperature obtained from meteorological stations on the Landsat acquisition date. Split Window Algorithm The Split-window algorithm (Tian et al., 2015 ) was used for LST calculation based on Landsat 8 dataset developed by Rosenstein et al. (2014). This algorithm incorporates atmospheric transmittance and emissivity as inputs. The Split Window algorithm is more accurate than mono window algorithm because it has got smaller error value (Bunai et al., 2017 ). The formula for estimating land surface temperature (Ts) is as follows: Ts = A0 + A1 * T10 - A2 * T11 (Eq. 6) Where Ts represents the LST value in degrees Celsius (°C), and T10 and T11 are the brightness temperatures from bands 10 and 11, respectively. The coefficients A0, A1, and A2 are determined based on emissivity and atmospheric transmittance values for both TIRS bands. Result and discussion Support Vector Machine Classification SVM has was classified the Hyderabad city into water bodies, vegetation, barren land, and built-up areas for the years 1989, 1999, 2009, and 2019 (Fig. 2 ). Accuracy and kappa coefficients range from 85–93% and 0.89 to 0.94, respectively. In 2019, SVM classification reveals a further decrease in the percentage share of water bodies (1.89%) and barren land (33.42%), coupled with an increase in built-up areas (56.49%) and vegetation (8.20%). Once again, built-up areas dominated the study area in 2019. SVM classification for 2009 shows a decrease in the percentage share of water bodies (2.02%) and barren land (34.46%), while built-up areas (49.10%) and vegetation (14.42%) slightly increased. Built-up areas became the predominant land type in 2009. Comparing SVM classification for 1999 to 1989, there is an increase in the percentage share of water bodies (2.45%), built-up areas (39.55%), and barren land (44.40%), with a decrease in vegetation from 19.39–13.60%. In 1999, barren land continued to dominate the city. SVM classification for 1989 reveals four distinct classes: water bodies (2.07%), built-up areas (35.81%), barren land (42.73%), and vegetation (19.39%) within the study area. Barren land occupies the largest portion, followed by built-up areas. Land Use/Land Cover Change Matrix (1989–2019) The Land Use/Land Cover Change matrix illustrates that 99.70 km 2 of barren land, 243.33 km 2 of built-up areas, 36.07 km 2 of vegetation, and 9.52 km 2 of water bodies remained unchanged from 2009 to 2019 (Table 2 ). There was a conversion of barren land into built-up areas, vegetation, and water bodies amounting to 127.82 km 2 , 6.50 km 2 , and 0.50 km 2 , respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, covering 82.64 km 2 , 9.87 km 2 , and 1.33 km 2 , respectively. Vegetation converted into barren land, built-up areas, and water bodies, encompassing 43.90 km 2, 15.82 km 2 , and 1.42 km 2 , respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 1.15 km 2 , 0.80 km 2 , and 2.28 km 2 , respectively. The matrix shows that from 1999 to 2009, 182.92 km 2 of barren land, 229.17 km 2 of built-up areas, 51.48 km 2 of vegetation, and 11.48 km 2 of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 92.99 km 2 , 27.20 km 2 , and 0.50 km 2 , respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 25.61 km 2 , 15.14 km 2 , and 0.62 km 2 , respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 25.12 km 2 , 14.21 km 2 , and 1.14 km 2 , respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 0.91 km 2 , 0.84 km 2 , and 3.41 km 2 , respectively. Table 2 Land Use/Land Cover Change Matrix from 1989 to 2019 Year 2009–2019 (km 2 ) 1999–2009 (km 2 ) 1989–1999 (km 2 ) 1989–2019 (km 2 ) Barren land 99.70 182.92 218.84 105.79 Barren land-Built-up 127.82 92.99 65.41 173.04 Barren land-Vegetation 6.49 27.20 7.73 12.55 Barren land-Waterbody 0.50 0.50 0.47 0.98 Built-up-Barren land 82.64 25.61 41.46 71.28 Built-up-Built-up 243.33 229.17 183.86 156.73 Built-up-Vegetation 9.87 15.14 18.03 15.41 Built-up-Waterbody 1.33 0.62 1.35 1.24 Vegetation-Barren land 43.90 25.12 42.56 48.65 Vegetation-Built-up 15.82 14.21 20.62 57.13 Vegetation-Vegetation 36.07 51.48 65.46 23.96 Vegetation-Waterbody 1.42 1.14 2.95 1.82 Waterbody-Barren land 1.15 0.91 0.86 1.70 Waterbody-Built-up 0.80 0.84 0.65 0.88 Waterbody-Vegetation 2.28 3.41 0.74 2.79 Waterbody-Waterbody 9.52 11.48 11.86 8.75 The matrix shows that from 1989 to 1999, 218.84 km 2 of barren land, 183.86 km 2 of built-up areas, 65.46 km 2 of vegetation, and 11.87 km 2 of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 65.41 km 2 , 7.73 km 2 , and 0.47 km 2 , respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 41.46 km 2 , 18.03 km 2 , and 1.35 km 2 , respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 42.56 km 2 , 20.62 km 2 , and 2.95 km 2 , respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 0.86 km 2 , 0.65 km 2 , and 0.74 km 2 , respectively. The matrix shows that from 1989 to 2019, 105.79 km 2 of barren land, 156.73 km 2 of built-up areas, 23.96 km 2 of vegetation, and 8.75 km 2 of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 173.04 km 2 , 12.55 km 2 , and 0.98 km 2 , respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 71.28 km 2 , 15.41 km 2 , and 1.24 km 2 , respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 48.65 km 2 , 57.13 km 2 , and 1.82 km 2 , respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 1.70 km 2 , 0.88 km 2 , and 2.79 km 2 , respectively. Land surface temperature Variation in Hyderabad Over the Last 40 Years (in 10-year Intervals) Temperature data for the region was analyzed from 1989 to 2019 in 10-year intervals. The mean temperatures for the years 1989, 1999, 2009, and 2019 were 29.9, 30.51, 28.57, and 31.67 degrees Celsius, respectively (Fig. 3 , Table 3 ). Temperature exhibited an increasing trend over this period, with the exception of the year 2009. Table 3 LST data of Hyderabad city Land surface temperature 2019 2009 1999 1989 Min. Max. Mean Std. dev. Min. Max. Mean Std. dev. Min. Max. Mean Std. dev. Min. Max. Mean Std. dev. Waterbody 23.11 37.22 26.13 2.57 21.5 32.46 23.18 1.32 21.5 33.26 24.43 1.57 20.62 34.46 23.04 1.51 Vegetation 23.47 37.5 30.92 1.92 21.5 34.06 27.53 1.77 21.5 38 28.31 2.4 21.06 35.65 27.72 2.39 Barren land 23.7 40.25 32.34 1.5 23.25 35.65 29.65 1.54 22.82 36.83 31.75 1.72 23.25 35.65 31.26 1.21 Built-up 23.7 41.61 31.61 1.07 21.5 36.44 28.33 1.36 22.38 37.61 30.24 1.43 21.5 36.05 29.85 1.49 The maximum temperatures ranged from 36.05 degrees Celsius in 1989 to 41.61 degrees Celsius in 2019. Meanwhile, the minimum temperatures varied from 20.61 degrees Celsius in 1989 to 23.11 degrees Celsius in 2019. Temperature data for different land cover classes was computed for the years 1989, 1999, 2009, and 2019. From 1989 to 1999, there was an increase in mean and minimum temperatures for water bodies, whereas maximum temperature decreased. Vegetation and built-up areas experienced increases in mean and maximum temperatures. Barren land showed an increase in mean and maximum temperatures, with a decrease in minimum temperature. From 1999 to 2009, mean and maximum temperatures decreased for water bodies and vegetation, while minimum temperature remained constant. Barren land exhibited an increase in mean and maximum temperatures along with an increase in minimum temperature. Built-up areas displayed decreases in mean, minimum, and maximum temperatures. From 2009 to 2019, all Land use/land cover classes experienced increases in mean, minimum, and maximum temperatures. Normalized Difference Built-Up Index (NDBI) NDBI values for the study area were calculated from 1989 to 2019 (Fig. 4 ). Higher NDBI values indicate higher urban density within the built-up class. A decreasing trend in the mean NDBI value from 1989 to 2019 suggests a decrease in built-up area density despite an overall increase in built-up extent. This decrease in NDBI values implies a lower density of built-up areas, even though the total built-up area has increased (Yasin et al., 2022 ). Normalized Difference Vegetation Index (NDVI) NDVI values for the vegetation class were compared from 1989 to 2019. The trend in the mean NDVI value fluctuated: it increased from 1989 to 1999, decreased from 1999 to 2009, and then increased again from 2009 to 2019 (Fig. 5 ). These fluctuations may indicate changes in vegetation density, stress, or health. Overall, the total NDVI value decreased from 1989 to 2019, suggesting a decrease in vegetation density, stress, or health (Xu et al., 2016 ). Normalized Difference Water Index (NDWI) NDWI values for water bodies were compared for the years 1989, 1999, 2009, and 2019. The mean NDWI value for water bodies increased from 1989 to 1999 and then decreased up to 2019 (Fig. 6 ). This pattern suggests a fluctuation in water quality, with an improvement from 1989 to 1999 followed by a subsequent decline (Kafrawy et al., 2017 ). Normalized Difference Barren Land Index (NDBaI) NDBaI values for the region were calculated from 1989 to 2019 (Fig. 7 ). The mean NDBaI value decreased over this period, indicating a shift from primary barren land to cultivated barren land (Moisa et al., 2022 ; Zhao and Chen, 2005 ). Contribution Index (CI) The Contribution Index (CI) links urban expansion and Land Surface Temperature (LST) changes. It reflects the cooling or heating effect of different land cover classes based on their proportion in the area. Positive values indicate a contribution to warming, while negative values indicate mitigating factors. Water bodies consistently exhibit negative values (-0.14 to -0.1) over the years, indicating a cooling effect on the surrounding region, acting as heat sinks when temperatures rise (Table 4 ). The decreasing negative values over time may be attributed to increasing water pollution (as reflected in the declining NDWI values). Vegetation also has negative values, signifying a mitigating effect, but these values decrease over time (-0.42 to -0.06). This decrease suggests a reduction in vegetation density, stress, or health during urbanization, as indicated by declining NDVI values. The contribution of barren land to city heating decreases over time (0.58 to 0.22). Urban wastelands, often featuring cemented surfaces and heat-trapping structures, contribute to the Urban Heat Island (UHI) effect. The reduction in the heating effect of barren land may be attributed to the conversion of primary barren land to cultivated barren land, as indicated by the decreasing NDBaI values. Urban built-up areas have a minimal and fluctuating cooling effect, implying that while urban growth contributes to the UHI effect, it does not significantly impact LST in terms of heating or cooling. Table 4 Contribution Index of Land Use/Land Cover Classes in the City Class / Year 1989 1999 2009 2019 CI (Water) -0.14 -0.15 -0.11 -0.1 CI (Vegetation) -0.42 -0.3 -0.15 -0.06 CI (Barren Land) 0.58 0.55 0.37 0.22 CI (Built-up) -0.02 -0.11 -0.12 -0.03 Conclusion The analysis of land cover and incoming radiation at different times of the day yields valuable insights into the dynamics of evolving landscapes and their implications for urban thermal comfort and climate change. The built-up area has surged from 35.81–56.49%, whereas barren land, vegetation, and water bodies have seen reductions from 42.73–33.42%, 19.39–8.20%, and 2.07–8.20%, respectively. In Hyderabad, the built-up area and the dry/barren lands are observed to be increasing as a result of decreasing vegetated lands (Sultana and Satyanarayana, 2018). LULC analysis reveals an expansion in urban cover but a decrease in urban density, accompanied by a decline in the NDBI value. Furthermore, stress in vegetation, water pollution, and the transformation of primary barren land into cultivated barren land were observed in the city. LST exhibited correlations with various land cover classes throughout the study period. The Contribution Index illuminated spatial and temporal changes in LST across different land use and land cover classes. Barren land consistently demonstrated a positive contribution (0.58 to 0.22) to LST, while other classes (water bodies (-0.42 to -0.06), vegetation (-0.42 to -0.06), and built-up areas (-0.02 to -0.03) displayed negative contributions to land surface temperature. This study underscores the significance of multi-temporal satellite imagery for change detection and pattern analysis in environmental planning decisions. It emphasizes the necessity for comprehensive urban management strategies that account for the impact of urbanization on other land surfaces and temperature. Open spaces, such as green areas, play a crucial role in mitigating the effects of heat islands and require vigilant monitoring and maintenance. The green areas in an urban region are gradually decreasing with population increases and an expanding urban scale. However, the green areas are very useful to improve the thermal environment in summer (Saito et al., 1990 ). Urban areas tend to have higher nighttime air temperatures and lower daytime air temperatures. This is also attributed to factors like latent heat flux and cloud cover (Touchaei and Wang, 2015 ). The study, however, is limited by its omission of considerations for urban structure, pollution impact, and wind dynamics in the city. Climate resilient city index, population dynamics, forecast study and Nature base solutions will further contribute in holistic analysis of the city. Declarations Funding: The authors did not receive support from any organization for the submitted work. Competing Interest declaration: Authors have no conflict of interest to declare. Availability of data and material: Not applicable. References Akbari, H. (2009). Cooling our communities. A guidebook on tree planting and light-colored surfacing. Arnfield, A. J. (2003). Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of climatology, 23(1), 1-26. Bahl, H. D., and Padmanabhamurty, B. (1979). Heat island studies at Delhi. Mausam, 30(1), 119-122. Bokaiea, M., M. K. Zarkesha, P. D. Arastehb, and H. Ali. (2016). “Assessment of Urban Heat Island Based on the Relationship between Land Surface Temperature and Land Use/Land Cover in Tehran.” Sustainable Cities and Society 23: 94–104. doi: 10.1016/j.scs.2016.03.009. Bridgman H., Warner R. and Dodson J., (1995) "Urban Biophysical Environments". Oxford University Press, Melbourne, p. 152, 1995. Bunai, T., Rokhmatuloh, A. W., Wibowo, A., and Shidiq, I. P. A. (2017). Comparison spatial pattern of land surface temperature with mono window algorithm and split window algorithm: a case study in south Tangerang, Indonesia. Series: Earth and Environmental Science, 149. Chatterjee, S., Khan, A., Dinda, A., Mithun, S., Khatun, R., Akbari, H., and Wang, Y. (2019). Simulating micro-scale thermal interactions in different building environments for mitigating urban heat islands. Science of the Total Environment, 663, 610-631. Chen, F., Yang, X., and Gallo et al. 1999 Zhu, W. (2014). WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China. Atmospheric Research, 138, 364-377. Chen, X. L., H. M. Zhao, P. X. Li, and Z. Y. Yin. (2006). “Remote Sensing Image-Based Analysis of the Relationship between Urban Heat Island and Land Use/Cover Changes.” Remote Sensing of Environment 104: 133–146. doi: 10.1016/j.rse.2005.11.016. Cui L., Shi J., 2012. Urbanization and its environmental effects in Shanghai, China. Urban Climate 2: 1–15. DOI 10.1016/j.uclim.2012.10.008. Doan, Q. V., and Kusaka, H. (2015). Numerical study on regional climate change due to the rapid urbanization of greater Ho Chi Minh City's metropolitan area over the past 20 years. International Journal of Climatology, 36(10), 3633-3650. Eludoyin, O. M., I. O. Adelekan, R. Webster, and A. O. Eludoyin. (2013). “Air Temperature, Relative Humidity, Climate Regionalization and Thermal Comfort of Nigeria.” International Journal of Climatology. http://onlinelibrary.wiley.com/doi/10.1002/joc.3817. Estoque, R. C., Y. Murayama, and S. W. Myint. (2017). “Effects of Landscape Composition and Pattern on Land Surface Temperature: An Urban Heat Island Study in the Megacities of Southeast Asia.” Science of the Total Environment 577: 349–359. doi: 10.1016/j.scitotenv.2016.10.195. Gallo, K. P., T. W. Owen, D. R. Easterling, and P. F. Jamason. (1999). “Temperature Trends of the U.S. Historical Climatology Network Based on Satellite-Designated Land Use/Land Cover.” Journal of Climate 12: 1344–1348. doi:10.1175/1520-0442(1999)0122.0.CO;2. Gangadharan, V. K., Sasidharan, N. V., and Santhosh, K. (1999). A STUDY ON HEAT ISLAND INTENSITIES AT THIRUVANTHAPURAM ON A COLD WINTER NIGHT. Mausam, 50(1), 106-108. Hahs A.K., McDonnell M.J., McCarthy M.A., Vesk P.A., Corlett R.T., Norton B.A., Clemants S.E., Duncan R.P., Thompson K., Schwartz M.W., Williams N.S.G., (2009). A global synthesis of plant extinction rates in urban areas. Ecology Letters 12: 1165–1173. DOI 10.1111/j.1461- 0248.2009.01372.x. Hamdi, R. and G. Schayes, (2008). “Sensitivity Study of the Urban Heat Island Intensity to Urbancharacteristics.” International Journal of Climatology 28: 973–982. doi:10.1002/(ISSN) 1097-0088. Helmers D.P. (2010). Housing growth in and near United States protected areas limits their conservation value. Proceedings of the National Academy of Sciences 107(2): 940–945. DOI 10.1073/pnas.0911131107. Henderson-Sellers, A., and Gornitz, V. (1984). Possible climatic impacts of land cover transformations, with particular emphasis on tropical deforestation. Climatic Change, 6(3), 231-257. Howard, L., (1818). The Climate of London Deduced from Meteorological Observations. London, UK: W. Phillips. INDIA, P. (2011). Census of India 2011 provisional population totals. New Delhi: Office of the Registrar General and Census Commissioner. Jago-on K.A.B., Kaneko S., Fujukura R., Fujiwara A., Imai T., Matsumoto T., 2009. Urbanization and subsurface environmental issues: An attempt at DPSIR model application in Asian cities. Science of the total Environment 407(9): 3089–3104. DOI 10.1016/j.scitotenv.2008.08.004. Kafrawy, S. B. E., Donia, N. S., & Mohamed, A. M. (2017). Water quality assessment based on CWQI and NDWI indices in Mariout Lake, Egypt. MOJ Ecology Environmental Sciences, 2(5), 00039. Kawashima, S. (1994). “Relation between Vegetation, Surface Temperature and Surface Composition in the Tokyo Region during winter.” Remote Sensing of Environment 50: 52–60. doi:10.1016/ 0034-4257(94)90094-9. Khan, M. A., and Syed, N. A. (2015). Image processing techniques for automatic detection of tumor in human brain using SVM. Int J Adv Res Comput Commun Eng, 4(4). Kim Y.H., Baik J.J., (2005). Spatial and temporal structure of urban heat island in Seoul. Journal of Applied Meteorology 44: 591–605. DOI 10.1175/JAM2226.1. Landsat Project Science Office. (2002).” Landsat 7 Science Data User’s Handbook.” Goddard Space Flight Center, NASA, Washington, DC. https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/ 08/Landsat7_Handbook.pdf. Liu J.G., Diamond J. (2005). China’s environment in a globalizing world. Nature 435: 1179–1186. DOI 10.1038/4351179a. Moisa, M. B., Dejene, I. N., & Gemeda, D. O. (2022). Geospatial technology–based analysis of land use land cover dynamics and its effects on land surface temperature in Guder River sub-basin, Abay Basin, Ethiopia. Applied Geomatics, 14(3), 451-463. Norman, Michael John Thornley; Pearson, C.J; Searle, P.G.E (1995). The ecology of tropical food crops. Cambridge University Press. pp. 249–251. Ohwo O., Abotutu A., (2015). Environmental Impact of Urbanisation in Nigeria. British Journal of Applied Science and Technology 9(3): 212–221. DOI 10.9734/bjast/2015/18148. Oke T.R (1987). "Boundary Layer Climates". London. Methuen, pp.33-76. Oke T.R, (1995). "The heat island of the urban boundary layer: characteristics, causes and effects". In J.E. Cermak et al (eds.). Wind climate in cities, pp.81-107. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly journal of the royal meteorological society, 108(455), 1-24. Oke, T. R., and Cleugh, H. A., (1987). Urban heat storage derived as energy balance residuals. Boundary-Layer Meteorology, 39(3), 233-245. Omran, E. E. (2012). “Detection of Land-Use and Surface Temperature Change at Different Resolutions.” Journal of Geographic Information System 4: 189–203. doi:10.4236/jgis.2012.43024. Parker, D. E. (2010). Urban heat island effects on estimates of observed climate change. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 123-133. Qiao, Z., G. Tian, and L. Xiao. (2013). “Diurnal and Seasonal Impacts of Urbanization on Theurban Thermal Environment: A Case Study of Beijing Using MODIS Data.” ISPRS Journal of Photogrammetry and Remote Sensing 85: 93–101. doi: 10.1016/j.isprsjprs.2013.08.010. Radeloff V.C., Stewart S.I., Hawbaker T.J., Gimmi U., Pidgeon A.M., Flather C.H., Hammer R.B., Ramachandraia, C. (2013). Drinking water: issues in access and equity. PDF). Jointactionforwater.org. Archived from the original (PDF) on, 10 November 2013. Retrieved 18 November 2012. Rao, P. K. (1972). “Remote Sensing of Urban “Heat Islands” from an Environmentalsatellite.” Bulletin of the American Meteorological Society 53: 647−648. Sabiha Sultana and A.N.V. Satyanarayana (2018): Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization, International Journal of Remote Sensi.ng, DOI: 10.1080/01431161.2018.1466072. Saito I., Ishihara O. and Katayama T. (1990). "Study of the effect of green areas on the thermal environment in an urban area", Energy and Buildings, Vol. 15-16, pp.443-446. Schell L.M., Smith M.T. and Billsborough A., "Human biological approaches to the study of third world urbanism" (1993). In Schell.L.M. Smith M.T. and Billsborough A. (eds.). Urban ecology and health in the 3rd world, Cambridge University Press, pp. 1-9. Seto K.C., Fragkias M., Guneralp B., Reilly M.K. (2011). A meta-analysis of global urban land expansion. PLoS one 6(8): 1–9. DOI 10.1371/journal.pone.0023777. Sobrino, J. A., R. Oltra-Carrió, G. Sòria, R. Bianchi, and M. Paganini. (2012). “Impact of Spatial Resolution and Satellite Overpass Time on Evaluation of the Surface Urban Heat Island Effects.” Remote Sensing of Environment 117: 50–56. doi: 10.1016/j.rse.2011.04.042. Tian, B., Wang, L., Kashiwaya, K., & Koike, K. (2015). Combination of well-logging temperature and thermal remote sensing for characterization of geothermal resources in Hokkaido, northern Japan. Remote Sensing, 7(3), 2647-2667. Touchaei, A. G., and Wang, Y., (2015). Characterizing urban heat island in Montreal (Canada)—effect of urban morphology. Sustainable Cities and Society, 19, 395-402. UN [United Nations Department of Economic and Social Affairs, Population Division], (2014). The 2014 Revision World Urbanization Prospects. United Nations, New York. Online: un.org/en/desa/2014-revision-world-urbanization-prospects (accessed 12 March 2020). UN [United Nations Department of Economic and Social Affairs, Population Division], (2018). The 2018 revision world urbanization prospects. United Nations, New York. Online: un.org/development/desa/publications (accessed 12 March 2020). Vancutsem, C., P. Ceccato, T. Dinku, and S. J. Connor. (2010). “Evaluation of MODIS Land Surface Temperature Data to Estimate Air Temperature in Different Ecosystems over Africa.” Remote Sensing of Environment 114: 449–465. doi: 10.1016/j.rse.2009.10.002. Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., & Zhao, S. (2015). An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote sensing, 7(4), 4268-4289. Weng, Q. (2009). “Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: Methods, Applications, and Trends (Review Article).” ISPRS Journal of Photogrammetry and Remote Sensing 64: 335–344. doi: 10.1016/j.isprsjprs.2009.03.007. Xu, Y., Yang, J., & Chen, Y. (2016). NDVI-based vegetation responses to climate change in an arid area of China. Theoretical and Applied Climatology, 126, 213-222. Yasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., & Noor, N. M. (2022, October). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. In IOP Conference Series: Earth and Environmental Science (Vol. 1067, No. 1, p. 012037). IOP Publishing. Yimene, Ababu Minda (2004). An African Indian community in Hyderabad. Cuvillier Verlag. pp. 5–6. ISBN 978-3-86537-206-2. Zhao S.Q., Da L.J., Tang Z.Y., Fang H.J., Song K., Fang J.Y. (2006). Ecological consequences of rapid urban expansion: Shanghai, China. Frontier Ecology and Environment 4(7): 341– 346. DOI 10.1890/1540-9295(2006)004[0341: ECORUE] 2.0. CO; 2. Zhao, H., and Chen, X. (2005, July). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International geoscience and remote sensing symposium (Vol. 3, p. 1666). 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University","correspondingAuthor":false,"prefix":"","firstName":"Pratyush","middleName":"","lastName":"Verma","suffix":""},{"id":273765309,"identity":"fd75b054-aa1d-402b-92d6-468a58bc1571","order_by":2,"name":"Bhawna Yadav","email":"","orcid":"","institution":"Jawaharlal Nehru University","correspondingAuthor":false,"prefix":"","firstName":"Bhawna","middleName":"","lastName":"Yadav","suffix":""},{"id":273765310,"identity":"b0eef38b-193f-4089-9978-39cfd00427a7","order_by":3,"name":"Saumitra 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14:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3873203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3873203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51428162,"identity":"80f38335-b5ae-4bbd-902f-f1380c14f96d","added_by":"auto","created_at":"2024-02-21 12:11:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":636327,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map of Hyderabad city\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/6a44fef1d120e2f2cf06f8f1.jpg"},{"id":51428160,"identity":"52da43b3-6c1f-4c1d-8564-98390f29cdd3","added_by":"auto","created_at":"2024-02-21 12:11:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":590543,"visible":true,"origin":"","legend":"\u003cp\u003eSVM Classification of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/d58681f59a176988d6d67af1.jpg"},{"id":51428050,"identity":"2fcc204d-fdf1-48b0-bf83-a9e28a574c16","added_by":"auto","created_at":"2024-02-21 12:03:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":508463,"visible":true,"origin":"","legend":"\u003cp\u003eLand Surface Temperature (LST) of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/fbf2ba51fc3dc335836f8789.jpg"},{"id":51428047,"identity":"e35427ec-aae9-43ed-bea3-5ca057e27c4e","added_by":"auto","created_at":"2024-02-21 12:03:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":575333,"visible":true,"origin":"","legend":"\u003cp\u003eNDBI of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/8b156d457162ca6d09168018.jpg"},{"id":51428049,"identity":"09daf5d3-c5e1-4ca9-a750-503e15faf860","added_by":"auto","created_at":"2024-02-21 12:03:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":621794,"visible":true,"origin":"","legend":"\u003cp\u003eNDVI of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/59be4c85e68f1bdb659df368.jpg"},{"id":51428161,"identity":"da962182-5d0d-48ca-8bb6-5ff68f0b3eaa","added_by":"auto","created_at":"2024-02-21 12:11:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":542090,"visible":true,"origin":"","legend":"\u003cp\u003eNDWI of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/3950473ba9b8c46397faddb5.jpg"},{"id":51428044,"identity":"ed1ade54-8579-4ee4-b69b-47978581f81e","added_by":"auto","created_at":"2024-02-21 12:03:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":466922,"visible":true,"origin":"","legend":"\u003cp\u003eNDBaI of Hyderabad Region for the Years (a) 2019, (b) 2009, (c) 1999 and (d) 1989\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/e5b932d28b9b2e9144a6c07a.jpg"},{"id":57138478,"identity":"ada82227-418d-4b9b-ab7c-d8bb56119b20","added_by":"auto","created_at":"2024-05-25 13:48:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4597020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3873203/v1/576ca87d-f15f-4c83-a9e5-48bb3ef9ad1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change has various impacts on the world. One of these impacts is the occurrence of severe heat storms for extended durations during the summer. These events create intensely uncomfortable conditions in urban areas. In recent years, numerous scientific studies have been conducted to examine the nature, temperature variations, and patterns of change on a global scale, encompassing local, regional, and global levels (Khan and Chatterjee, 2019). The climatic changes observed in urban centers result from both natural processes and human-induced alterations to the Earth's surface and atmospheric properties (Henderson and Gornitz, 1984).\u003c/p\u003e \u003cp\u003eIn the late 1950s, urban areas accounted for 30% of the global population, a percentage that grew to 55% by 2018 and is projected to reach 68% by 2050 (UN, 2014; UN, 2018). The global population grew only six times in the last 200 years, and the urban population increased 128 times (Schell et al., 1993). This rapid urbanization has significantly impacted society, the environment, and humanity as a whole. Urbanization has had a profound effect on urban societies and living standards. It has also placed substantial pressure on the environment to sustain the growing urban population. Numerous studies have highlighted that urbanization has been a driving force behind the social and economic development of urban centers, transforming them into efficient, convenient, and innovative marketplaces, albeit with consequences for surrounding suburban and rural areas (Seto et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cui and Shi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu and Diamond, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These studies have shown that urbanization has led to increased demand for housing, resulting in subpar living conditions, inadequate housing, reduced natural vegetation due to decreased permeable surfaces, altered surface albedo, loss of biodiversity, increased water and air pollution, and diminished water supply (Ohwo and Abotutu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hahs et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cui and Shi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, it has altered land use and land cover patterns, modifying natural biogeochemical and water cycles, fragmenting natural habitats, leading to biodiversity loss, rapid depletion of productive farmland, and causing local weather variations due to changes in surface albedo (Seto et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Radeloff et al., 2010; Jago-on et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yuan, 2008).\u003c/p\u003e \u003cp\u003eUrban development, combined with modifications to the lithosphere and atmosphere, has inadvertently resulted in climatic changes. Dense urban materials absorb heat and create impermeable surfaces, while tall buildings alter albedo, trapping more radiation, increasing air stagnation, and leading to heightened air pollution and the formation of Cloud Condensation Nuclei (CCN). Additionally, human activities release heat, further contributing to the urban heat environment (Oke, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis inadvertent impact of urbanization and climatic changes, along with infrastructural development, can render urban environments vulnerable. The Urban Heat Island (UHI) effect, has become a growing concern for urban climatologists (Doan and Kusaka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The UHI effect is characterized by elevated air temperatures in urban landscapes surrounded by cooler temperatures in suburban or rural areas. The heat island effect is most prominently noticed around the minimum temperature epoch, when the temperature curve is more or less flat (Bahl and Padmanabhamurty \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Gangadharan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e. Oke (1995) defined UHI as the temperature differences between the urban and surrounding rural stations. Urban Heat Island (UHI), a consequence of urbanization was first observed by (Howard, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1818\u003c/span\u003e; Oke, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Numerous studies have indicated that various properties of the UHI are directly linked to the structural features of the urban landscape. As Bridgman et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) pointed out, \"buildings impact the urban environment in five significant ways: by replacing vegetated areas, generating artificial heat, introducing block-like buildings, emitting pollutants, and draining rainfall rapidly.\"\u003c/p\u003e \u003cp\u003eIn recent years, extensive research has been conducted on the UHI in diverse climate zones to investigate the UHI phenomenon's mechanisms and the impact of urbanization on UHI effects in cities (Doan and Kusaka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Most studies encompass urban modifications to local climate, such as UHI effects and changes in LULC, at an urban scale. This implies that urban temperatures tend to be higher in the urban core compared to its suburban natural surroundings, particularly at night (Oke and Cleugh, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Arnfield, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Parker, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChen et al. (2014) identified factors contributing to the formation of Urban Heat Islands (UHI): (a) alterations in the land surface physics due to extensive urbanization, resulting in changes to urban albedo, thermal emissivity, and material conductivity; (b) human-induced heat generation from various activities; (c) reduced surface evapotranspiration caused by urban concretization; and (d) modifications in flow characteristics and near-surface atmospheric processes due to factors like a low sky view factor (SVF) and urban geometries (Kim and Baik, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The ongoing development poses a threat to the conservation value of protected areas, potentially leading to a decline in biodiversity (Helmers, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Urban regions experience heightened heat levels due to multiple factors, with some, such as climate and topography, beyond human control. However, there are two modifiable factors\u0026mdash;vegetation quantity and surface color\u0026mdash;that significantly contribute to the additional heat associated with human activities (Akbari, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have employed different approaches to represent urban climate and changes in Land Surface Temperature (LST), which can be categorized into ground-based and satellite observations. Traditional ground-based observations rely on weather stations to estimate variations in near-surface air temperature between rural and urban areas (Eludoyin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vancutsem et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rao, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Ground-based measurements require a significant number of weather stations within the study area to yield meaningful results. In contrast, satellite observations offer a more promising alternative, as they provide LST data at suitable temporal and spatial resolutions for studying UHI (Sobrino et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Weng, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hamdi and Schayes, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Qiao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thermal infrared (TIR) imagery has been employed for detecting urban sprawl and UHI intensities (Landsat Project Science Office, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rao, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Gallo et al., 1995). NDVI values serve as indicators of vegetation density, facilitating the assessment of changes in (LULC) patterns to analyze UHI intensities. Numerous studies have explored the relationship between vegetation density and LST, providing insights into estimating UHI intensity using Landsat data (Estoque et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bokaiea et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Omran, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kawashima, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1994\u003c/span\u003e;).\u003c/p\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eHyderabad is both the capital and the largest city of Telangana. The study area encompasses the region surrounding Hyderabad, situated at latitude 17\u0026deg; 16' 56'' to 17\u0026deg; 33' 40'' N and longitude 78\u0026deg; 14' 38'' to 78\u0026deg; 38' 28'' E, covering an area of 686 square kilometers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Hyderabad is characterized by hilly terrain with numerous artificial lakes and is situated on the banks of Musi, which is a tributary of Krishna River. With a population of 6.9\u0026nbsp;million people (INDIA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) it ranks as the fourth-most populous city in the country. Situated on a sloping terrain of pink and grey granite the city has an average altitude of 542 meters (Ramachandraia, 2013). Some of the notable artificial lakes in the area include Hussain Sagar, Osman Sagar, and Himayat Sagar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHyderabad experiences a tropical wet and dry climate. The region falls within the moderate (26\u0026deg;C-32\u0026deg;C) to strong heat stress (32\u0026deg;C to 38\u0026deg;C) range according to the Universal Thermal Climate Index. It maintains an annual average temperature of 26.6℃, with monthly average temperatures ranging between 21℃ to 33℃. Summers are characterized by hot and humid conditions, often exceeding 40℃ in May. The months of December and January are the coldest, with temperatures occasionally dropping to 10℃ (Norman et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). On June 2, 1966, Hyderabad recorded its highest temperature of 45.4℃, while the lowest temperature ever recorded was 6.1℃ on January 8, 1946.\u003c/p\u003e \u003cp\u003eThe majority of Hyderabad's rainfall occurs during the southwest monsoon period between June and September. The heaviest 24-hour rainfall recorded was 241.5 mm on August 24, 2000 (IMD). Annually, the city receives 2,731 hours of sunshine, with the highest daily sunlight exposure occurring in February (Yimene and Minda, 2004).\u003c/p\u003e \u003cp\u003eCloud cover varies throughout the year, with the months from June to October experiencing around 50% cloud cover, while January to March has significantly less at only 25%. Clear skies are often observed in January, February, and March. The monsoon months of July, August, and September are characterized by very high humidity levels exceeding 75%, while the months of March, April, and May are dry and experience lower humidity levels ranging from 25\u0026ndash;30% (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hyderabad-india-online.com/2011/06/climatic-conditions-hyderabad\u003c/span\u003e\u003cspan address=\"http://hyderabad-india-online.com/2011/06/climatic-conditions-hyderabad\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWind patterns in Hyderabad vary by season, with southern winds prevailing for three months (February to April), western winds for four and a half months (mid-May to September end), and eastern winds for four months (October to January). Wind speeds are higher during the windier months of June, July, and August, with average speeds of 10 miles per hour, while the rest of the months experience calmer conditions with average wind speeds of 6.3 miles per hour (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://weatherspark.com/y/109450/Average-Weather-in-Hyderabad-India-Year-Round\u003c/span\u003e\u003cspan address=\"https://weatherspark.com/y/109450/Average-Weather-in-Hyderabad-India-Year-Round\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eLandsat 8 provides 11 multi-spectral bands, while Landsat 4\u0026ndash;5 offers 7 bands, categorized into visible, near-infrared, shortwave radiation, and thermal infrared bands. Landsat 4\u0026ndash;5 is equipped with the Multispectral Scanner (MSS) system and the Thematic Mapper (TM), while Landsat 8 utilizes the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The data for the years 1989, 1999, 2009, and 2019 were obtained from the United States Geological Survey (USGS) earthexplorer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These bands were then stacked using ERDAS IMAGINE software and utilized to calculate Land Surface Temperature (LST) through ArcGIS software.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSatellite Data for the Study Area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePath/Row\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCloud Cover\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 8 OLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e25-02-2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e05:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144/048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 4\u0026ndash;5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e13-02-2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04:55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144/048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 4\u0026ndash;5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e18-02-1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04:48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144/048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 4\u0026ndash;5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e22-02-1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04:49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144/048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSupport Vector Machine Classification\u003c/h2\u003e \u003cp\u003eSVM is a well-established supervised machine learning method frequently used for classification and regression tasks. SVM has proven highly effective for classifying high-dimensional data. In its decision function, SVM is a binary classifier based on supervised learning which gives better performance than other classifiers. SVM classifies between two classes by constructing a hyperplane in high-dimensional feature space which can be used for classification (Khan and Syed, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This technique employs a kernel trick to transform data and determine an optimal boundary between potential outcomes. SVM can utilize both linear and non-linear kernels. SVM classification was conducted using ArcGIS software. Initially, signature files (training samples) were created based on visual feature identification. These signature files served as references for training the SVM classifier (source: ArcGIS SVM Classifier) and evaluating classification accuracy (ArcGIS Accuracy Assessment Points). The final classification was performed using the \"classify raster\" tool in ArcGIS (ArcGIS Classify Raster). A total of 55 sample points were employed to assess classification accuracy (Compute Confusion Matrix).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndices Used\u003c/h2\u003e \u003cp\u003eThe following indices were computed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNDVI = (NIR - Red) / (NIR\u0026thinsp;+\u0026thinsp;Red) (Eq.\u0026nbsp;1)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNDWI = (Green - Red) / (Green\u0026thinsp;+\u0026thinsp;Red) (Eq.\u0026nbsp;2)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNDBI = (SWIR \u0026ndash; NIR) / (SWIR\u0026thinsp;+\u0026thinsp;NIR) (Eq.\u0026nbsp;3)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNDBaI = (NIR \u0026ndash; SWIR1) / (NIR\u0026thinsp;+\u0026thinsp;SWIR1) (Eq.\u0026nbsp;4)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRed - central wavelength 0.560\u0026micro;m\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGreen - central wavelength 0.655\u0026micro;m\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNIR - central wavelength 0.865\u0026micro;m\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSWIR - central wavelength 1.610\u0026micro;m\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSWIR1 - central wavelength 2.200\u0026micro;m\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTwo such algorithms employed estimating land surface temperature for our present study are the Mono Window Algorithm and the Split Window Algorithm:\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMono Window Algorithm\u003c/h2\u003e \u003cp\u003eThe Mono-window algorithm developed by (Qin et al., 2015) (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used for LST calculation using Landsat TM data and can be expressed as follows:\u003c/p\u003e \u003cp\u003eTs = [a10(1\u0026thinsp;\u0026minus;\u0026thinsp;C10\u0026thinsp;\u0026minus;\u0026thinsp;D10) + (b10 (1\u0026thinsp;\u0026minus;\u0026thinsp;C10\u0026thinsp;\u0026minus;\u0026thinsp;D10)\u0026thinsp;+\u0026thinsp;C10\u0026thinsp;+\u0026thinsp;D10) T10\u0026thinsp;\u0026minus;\u0026thinsp;D10Ta] / C10 C10\u0026thinsp;=\u0026thinsp;τ10ε10 D10\u0026thinsp;=\u0026thinsp;(1\u0026thinsp;\u0026minus;\u0026thinsp;τ10) [1+(1\u0026thinsp;\u0026minus;\u0026thinsp;ε10) τ10 (Eq.\u0026nbsp;5)\u003c/p\u003e \u003cp\u003eHere, a10 = -62.7182 and b10\u0026thinsp;=\u0026thinsp;0.4339 represent model constants for temperature variations from 0 to 50\u0026deg;C. ε10 is the emissivity for Landsat 8 OLI-TIRS, determined from the NDVI, and τ10 is the atmospheric transmittance for Landsat 8 OLI-TIRS. Ts is the effective mean atmospheric temperature for the tropical model, with near-surface air temperature obtained from meteorological stations on the Landsat acquisition date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eSplit Window Algorithm\u003c/h2\u003e \u003cp\u003eThe Split-window algorithm (Tian et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used for LST calculation based on Landsat 8 dataset developed by Rosenstein et al. (2014). This algorithm incorporates atmospheric transmittance and emissivity as inputs. The Split Window algorithm is more accurate than mono window algorithm because it has got smaller error value (Bunai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The formula for estimating land surface temperature (Ts) is as follows:\u003c/p\u003e \u003cp\u003eTs\u0026thinsp;=\u0026thinsp;A0\u0026thinsp;+\u0026thinsp;A1 * T10 - A2 * T11 (Eq.\u0026nbsp;6)\u003c/p\u003e \u003cp\u003eWhere Ts represents the LST value in degrees Celsius (\u0026deg;C), and T10 and T11 are the brightness temperatures from bands 10 and 11, respectively. The coefficients A0, A1, and A2 are determined based on emissivity and atmospheric transmittance values for both TIRS bands.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Result and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSupport Vector Machine Classification\u003c/h2\u003e \u003cp\u003eSVM has was classified the Hyderabad city into water bodies, vegetation, barren land, and built-up areas for the years 1989, 1999, 2009, and 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Accuracy and kappa coefficients range from 85\u0026ndash;93% and 0.89 to 0.94, respectively. In 2019, SVM classification reveals a further decrease in the percentage share of water bodies (1.89%) and barren land (33.42%), coupled with an increase in built-up areas (56.49%) and vegetation (8.20%). Once again, built-up areas dominated the study area in 2019.\u003c/p\u003e \u003cp\u003eSVM classification for 2009 shows a decrease in the percentage share of water bodies (2.02%) and barren land (34.46%), while built-up areas (49.10%) and vegetation (14.42%) slightly increased. Built-up areas became the predominant land type in 2009.\u003c/p\u003e \u003cp\u003eComparing SVM classification for 1999 to 1989, there is an increase in the percentage share of water bodies (2.45%), built-up areas (39.55%), and barren land (44.40%), with a decrease in vegetation from 19.39\u0026ndash;13.60%. In 1999, barren land continued to dominate the city.\u003c/p\u003e \u003cp\u003eSVM classification for 1989 reveals four distinct classes: water bodies (2.07%), built-up areas (35.81%), barren land (42.73%), and vegetation (19.39%) within the study area. Barren land occupies the largest portion, followed by built-up areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLand Use/Land Cover Change Matrix (1989\u0026ndash;2019)\u003c/h2\u003e \u003cp\u003eThe Land Use/Land Cover Change matrix illustrates that 99.70 km\u003csup\u003e2\u003c/sup\u003e of barren land, 243.33 km\u003csup\u003e2\u003c/sup\u003e of built-up areas, 36.07 km\u003csup\u003e2\u003c/sup\u003e of vegetation, and 9.52 km\u003csup\u003e2\u003c/sup\u003e of water bodies remained unchanged from 2009 to 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was a conversion of barren land into built-up areas, vegetation, and water bodies amounting to 127.82 km\u003csup\u003e2\u003c/sup\u003e, 6.50 km\u003csup\u003e2\u003c/sup\u003e, and 0.50 km\u003csup\u003e2\u003c/sup\u003e, respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, covering 82.64 km\u003csup\u003e2\u003c/sup\u003e, 9.87 km\u003csup\u003e2\u003c/sup\u003e, and 1.33 km\u003csup\u003e2\u003c/sup\u003e, respectively. Vegetation converted into barren land, built-up areas, and water bodies, encompassing 43.90 km\u003csup\u003e2,\u003c/sup\u003e 15.82 km\u003csup\u003e2\u003c/sup\u003e, and 1.42 km\u003csup\u003e2\u003c/sup\u003e, respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 1.15 km\u003csup\u003e2\u003c/sup\u003e, 0.80 km\u003csup\u003e2\u003c/sup\u003e, and 2.28 km\u003csup\u003e2\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe matrix shows that from 1999 to 2009, 182.92 km\u003csup\u003e2\u003c/sup\u003e of barren land, 229.17 km\u003csup\u003e2\u003c/sup\u003e of built-up areas, 51.48 km\u003csup\u003e2\u003c/sup\u003e of vegetation, and 11.48 km\u003csup\u003e2\u003c/sup\u003e of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 92.99 km\u003csup\u003e2\u003c/sup\u003e, 27.20 km\u003csup\u003e2\u003c/sup\u003e, and 0.50 km\u003csup\u003e2\u003c/sup\u003e, respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 25.61 km\u003csup\u003e2\u003c/sup\u003e, 15.14 km\u003csup\u003e2\u003c/sup\u003e, and 0.62 km\u003csup\u003e2\u003c/sup\u003e, respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 25.12 km\u003csup\u003e2\u003c/sup\u003e, 14.21 km\u003csup\u003e2\u003c/sup\u003e, and 1.14 km\u003csup\u003e2\u003c/sup\u003e, respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 0.91 km\u003csup\u003e2\u003c/sup\u003e, 0.84 km\u003csup\u003e2\u003c/sup\u003e, and 3.41 km\u003csup\u003e2\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Use/Land Cover Change Matrix from 1989 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009\u0026ndash;2019 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2009 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1989\u0026ndash;1999 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1989\u0026ndash;2019 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land-Built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land-Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land-Waterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up-Barren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up-Built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up-Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up-Waterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation-Barren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation-Built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation-Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation-Waterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody-Barren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody-Built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody-Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody-Waterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe matrix shows that from 1989 to 1999, 218.84 km\u003csup\u003e2\u003c/sup\u003e of barren land, 183.86 km\u003csup\u003e2\u003c/sup\u003e of built-up areas, 65.46 km\u003csup\u003e2\u003c/sup\u003e of vegetation, and 11.87 km\u003csup\u003e2\u003c/sup\u003e of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 65.41 km\u003csup\u003e2\u003c/sup\u003e, 7.73 km\u003csup\u003e2\u003c/sup\u003e, and 0.47 km\u003csup\u003e2\u003c/sup\u003e, respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 41.46 km\u003csup\u003e2\u003c/sup\u003e, 18.03 km\u003csup\u003e2\u003c/sup\u003e, and 1.35 km\u003csup\u003e2\u003c/sup\u003e, respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 42.56 km\u003csup\u003e2\u003c/sup\u003e, 20.62 km\u003csup\u003e2\u003c/sup\u003e, and 2.95 km\u003csup\u003e2\u003c/sup\u003e, respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 0.86 km\u003csup\u003e2\u003c/sup\u003e, 0.65 km\u003csup\u003e2\u003c/sup\u003e, and 0.74 km\u003csup\u003e2\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe matrix shows that from 1989 to 2019, 105.79 km\u003csup\u003e2\u003c/sup\u003e of barren land, 156.73 km\u003csup\u003e2\u003c/sup\u003e of built-up areas, 23.96 km\u003csup\u003e2\u003c/sup\u003e of vegetation, and 8.75 km\u003csup\u003e2\u003c/sup\u003e of water bodies remained unchanged. There was a conversion of barren land into built-up areas, vegetation, and water bodies, covering 173.04 km\u003csup\u003e2\u003c/sup\u003e, 12.55 km\u003csup\u003e2\u003c/sup\u003e, and 0.98 km\u003csup\u003e2\u003c/sup\u003e, respectively. Similarly, built-up areas transformed into barren land, vegetation, and water bodies, encompassing 71.28 km\u003csup\u003e2\u003c/sup\u003e, 15.41 km\u003csup\u003e2\u003c/sup\u003e, and 1.24 km\u003csup\u003e2\u003c/sup\u003e, respectively. Vegetation converted into barren land, built-up areas, and water bodies, amounting to 48.65 km\u003csup\u003e2\u003c/sup\u003e, 57.13 km\u003csup\u003e2\u003c/sup\u003e, and 1.82 km\u003csup\u003e2\u003c/sup\u003e, respectively. Water bodies also underwent changes into barren land, built-up areas, and vegetation, totalling 1.70 km\u003csup\u003e2\u003c/sup\u003e, 0.88 km\u003csup\u003e2\u003c/sup\u003e, and 2.79 km\u003csup\u003e2\u003c/sup\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLand surface temperature Variation in Hyderabad Over the Last 40 Years (in 10-year Intervals)\u003c/h2\u003e \u003cp\u003eTemperature data for the region was analyzed from 1989 to 2019 in 10-year intervals. The mean temperatures for the years 1989, 1999, 2009, and 2019 were 29.9, 30.51, 28.57, and 31.67 degrees Celsius, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Temperature exhibited an increasing trend over this period, with the exception of the year 2009.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLST data of Hyderabad city\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e \u003cp\u003eLand surface temperature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e23.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e35.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e27.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e23.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e35.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e31.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e36.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e29.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe maximum temperatures ranged from 36.05 degrees Celsius in 1989 to 41.61 degrees Celsius in 2019. Meanwhile, the minimum temperatures varied from 20.61 degrees Celsius in 1989 to 23.11 degrees Celsius in 2019.\u003c/p\u003e \u003cp\u003eTemperature data for different land cover classes was computed for the years 1989, 1999, 2009, and 2019. From 1989 to 1999, there was an increase in mean and minimum temperatures for water bodies, whereas maximum temperature decreased. Vegetation and built-up areas experienced increases in mean and maximum temperatures. Barren land showed an increase in mean and maximum temperatures, with a decrease in minimum temperature.\u003c/p\u003e \u003cp\u003eFrom 1999 to 2009, mean and maximum temperatures decreased for water bodies and vegetation, while minimum temperature remained constant. Barren land exhibited an increase in mean and maximum temperatures along with an increase in minimum temperature. Built-up areas displayed decreases in mean, minimum, and maximum temperatures.\u003c/p\u003e \u003cp\u003eFrom 2009 to 2019, all Land use/land cover classes experienced increases in mean, minimum, and maximum temperatures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNormalized Difference Built-Up Index (NDBI)\u003c/h2\u003e \u003cp\u003eNDBI values for the study area were calculated from 1989 to 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher NDBI values indicate higher urban density within the built-up class. A decreasing trend in the mean NDBI value from 1989 to 2019 suggests a decrease in built-up area density despite an overall increase in built-up extent. This decrease in NDBI values implies a lower density of built-up areas, even though the total built-up area has increased (Yasin et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNormalized Difference Vegetation Index (NDVI)\u003c/h2\u003e \u003cp\u003eNDVI values for the vegetation class were compared from 1989 to 2019. The trend in the mean NDVI value fluctuated: it increased from 1989 to 1999, decreased from 1999 to 2009, and then increased again from 2009 to 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These fluctuations may indicate changes in vegetation density, stress, or health. Overall, the total NDVI value decreased from 1989 to 2019, suggesting a decrease in vegetation density, stress, or health (Xu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNormalized Difference Water Index (NDWI)\u003c/h2\u003e \u003cp\u003eNDWI values for water bodies were compared for the years 1989, 1999, 2009, and 2019. The mean NDWI value for water bodies increased from 1989 to 1999 and then decreased up to 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This pattern suggests a fluctuation in water quality, with an improvement from 1989 to 1999 followed by a subsequent decline (Kafrawy et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNormalized Difference Barren Land Index (NDBaI)\u003c/h2\u003e \u003cp\u003eNDBaI values for the region were calculated from 1989 to 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The mean NDBaI value decreased over this period, indicating a shift from primary barren land to cultivated barren land (Moisa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao and Chen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eContribution Index (CI)\u003c/h2\u003e \u003cp\u003eThe Contribution Index (CI) links urban expansion and Land Surface Temperature (LST) changes. It reflects the cooling or heating effect of different land cover classes based on their proportion in the area. Positive values indicate a contribution to warming, while negative values indicate mitigating factors. Water bodies consistently exhibit negative values (-0.14 to -0.1) over the years, indicating a cooling effect on the surrounding region, acting as heat sinks when temperatures rise (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The decreasing negative values over time may be attributed to increasing water pollution (as reflected in the declining NDWI values).\u003c/p\u003e \u003cp\u003eVegetation also has negative values, signifying a mitigating effect, but these values decrease over time (-0.42 to -0.06). This decrease suggests a reduction in vegetation density, stress, or health during urbanization, as indicated by declining NDVI values.\u003c/p\u003e \u003cp\u003eThe contribution of barren land to city heating decreases over time (0.58 to 0.22). Urban wastelands, often featuring cemented surfaces and heat-trapping structures, contribute to the Urban Heat Island (UHI) effect. The reduction in the heating effect of barren land may be attributed to the conversion of primary barren land to cultivated barren land, as indicated by the decreasing NDBaI values.\u003c/p\u003e \u003cp\u003eUrban built-up areas have a minimal and fluctuating cooling effect, implying that while urban growth contributes to the UHI effect, it does not significantly impact LST in terms of heating or cooling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution Index of Land Use/Land Cover Classes in the City\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass / Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI (Water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI (Vegetation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI (Barren Land)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI (Built-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe analysis of land cover and incoming radiation at different times of the day yields valuable insights into the dynamics of evolving landscapes and their implications for urban thermal comfort and climate change. The built-up area has surged from 35.81\u0026ndash;56.49%, whereas barren land, vegetation, and water bodies have seen reductions from 42.73\u0026ndash;33.42%, 19.39\u0026ndash;8.20%, and 2.07\u0026ndash;8.20%, respectively. In Hyderabad, the built-up area and the dry/barren lands are observed to be increasing as a result of decreasing vegetated lands (Sultana and Satyanarayana, 2018). LULC analysis reveals an expansion in urban cover but a decrease in urban density, accompanied by a decline in the NDBI value. Furthermore, stress in vegetation, water pollution, and the transformation of primary barren land into cultivated barren land were observed in the city.\u003c/p\u003e \u003cp\u003eLST exhibited correlations with various land cover classes throughout the study period. The Contribution Index illuminated spatial and temporal changes in LST across different land use and land cover classes. Barren land consistently demonstrated a positive contribution (0.58 to 0.22) to LST, while other classes (water bodies (-0.42 to -0.06), vegetation (-0.42 to -0.06), and built-up areas (-0.02 to -0.03) displayed negative contributions to land surface temperature.\u003c/p\u003e \u003cp\u003eThis study underscores the significance of multi-temporal satellite imagery for change detection and pattern analysis in environmental planning decisions. It emphasizes the necessity for comprehensive urban management strategies that account for the impact of urbanization on other land surfaces and temperature. Open spaces, such as green areas, play a crucial role in mitigating the effects of heat islands and require vigilant monitoring and maintenance. The green areas in an urban region are gradually decreasing with population increases and an expanding urban scale. However, the green areas are very useful to improve the thermal environment in summer (Saito et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Urban areas tend to have higher nighttime air temperatures and lower daytime air temperatures. This is also attributed to factors like latent heat flux and cloud cover (Touchaei and Wang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The study, however, is limited by its omission of considerations for urban structure, pollution impact, and wind dynamics in the city. Climate resilient city index, population dynamics, forecast study and Nature base solutions will further contribute in holistic analysis of the city.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration:\u003c/strong\u003e Authors have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbari, H. (2009). Cooling our communities. A guidebook on tree planting and light-colored surfacing.\u003c/li\u003e\n\u003cli\u003eArnfield, A. J. (2003). Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of climatology, 23(1), 1-26.\u003c/li\u003e\n\u003cli\u003eBahl, H. D., and Padmanabhamurty, B. (1979). Heat island studies at Delhi. Mausam, 30(1), 119-122.\u003c/li\u003e\n\u003cli\u003eBokaiea, M., M. K. Zarkesha, P. D. Arastehb, and H. Ali. (2016). \u0026ldquo;Assessment of Urban Heat Island Based on the Relationship between Land Surface Temperature and Land Use/Land Cover in Tehran.\u0026rdquo; Sustainable Cities and Society 23: 94\u0026ndash;104. doi: 10.1016/j.scs.2016.03.009.\u003c/li\u003e\n\u003cli\u003eBridgman H., Warner R. and Dodson J., (1995) \u0026quot;Urban Biophysical Environments\u0026quot;. Oxford University Press, Melbourne, p. 152, 1995.\u003c/li\u003e\n\u003cli\u003eBunai, T., Rokhmatuloh, A. W., Wibowo, A., and Shidiq, I. P. A. (2017). Comparison spatial pattern of land surface temperature with mono window algorithm and split window algorithm: a case study in south Tangerang, Indonesia. Series: Earth and Environmental Science, 149.\u003c/li\u003e\n\u003cli\u003eChatterjee, S., Khan, A., Dinda, A., Mithun, S., Khatun, R., Akbari, H., and Wang, Y. (2019). Simulating micro-scale thermal interactions in different building environments for mitigating urban heat islands. Science of the Total Environment, 663, 610-631.\u003c/li\u003e\n\u003cli\u003eChen, F., Yang, X., and Gallo et al. 1999 Zhu, W. (2014). WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China. Atmospheric Research, 138, 364-377.\u003c/li\u003e\n\u003cli\u003eChen, X. L., H. M. Zhao, P. X. Li, and Z. Y. Yin. (2006). \u0026ldquo;Remote Sensing Image-Based Analysis of the Relationship between Urban Heat Island and Land Use/Cover Changes.\u0026rdquo; Remote Sensing of Environment 104: 133\u0026ndash;146. doi: 10.1016/j.rse.2005.11.016.\u003c/li\u003e\n\u003cli\u003eCui L., Shi J., 2012. Urbanization and its environmental effects in Shanghai, China. Urban Climate 2: 1\u0026ndash;15. DOI 10.1016/j.uclim.2012.10.008.\u003c/li\u003e\n\u003cli\u003eDoan, Q. V., and Kusaka, H. (2015). Numerical study on regional climate change due to the rapid urbanization of greater Ho Chi Minh City\u0026apos;s metropolitan area over the past 20 years. International Journal of Climatology, 36(10), 3633-3650.\u003c/li\u003e\n\u003cli\u003eEludoyin, O. M., I. O. Adelekan, R. Webster, and A. O. Eludoyin. (2013). \u0026ldquo;Air Temperature, Relative Humidity, Climate Regionalization and Thermal Comfort of Nigeria.\u0026rdquo; International Journal of Climatology. http://onlinelibrary.wiley.com/doi/10.1002/joc.3817.\u003c/li\u003e\n\u003cli\u003eEstoque, R. C., Y. Murayama, and S. W. Myint. (2017). \u0026ldquo;Effects of Landscape Composition and Pattern on Land Surface Temperature: An Urban Heat Island Study in the Megacities of Southeast Asia.\u0026rdquo; Science of the Total Environment 577: 349\u0026ndash;359. doi: 10.1016/j.scitotenv.2016.10.195.\u003c/li\u003e\n\u003cli\u003eGallo, K. P., T. W. Owen, D. R. Easterling, and P. F. Jamason. (1999). \u0026ldquo;Temperature Trends of the U.S. Historical Climatology Network Based on Satellite-Designated Land Use/Land Cover.\u0026rdquo; Journal of Climate 12: 1344\u0026ndash;1348. doi:10.1175/1520-0442(1999)0122.0.CO;2.\u003c/li\u003e\n\u003cli\u003eGangadharan, V. K., Sasidharan, N. V., and Santhosh, K. (1999). A STUDY ON HEAT ISLAND INTENSITIES AT THIRUVANTHAPURAM ON A COLD WINTER NIGHT. Mausam, 50(1), 106-108.\u003c/li\u003e\n\u003cli\u003eHahs A.K., McDonnell M.J., McCarthy M.A., Vesk P.A., Corlett R.T., Norton B.A., Clemants S.E., Duncan R.P., Thompson K., Schwartz M.W., Williams N.S.G., (2009). A global synthesis of plant extinction rates in urban areas. Ecology Letters 12: 1165\u0026ndash;1173. DOI 10.1111/j.1461- 0248.2009.01372.x.\u003c/li\u003e\n\u003cli\u003eHamdi, R. and G. Schayes, (2008). \u0026ldquo;Sensitivity Study of the Urban Heat Island Intensity to Urbancharacteristics.\u0026rdquo; International Journal of Climatology 28: 973\u0026ndash;982. doi:10.1002/(ISSN) 1097-0088.\u003c/li\u003e\n\u003cli\u003eHelmers D.P. (2010). Housing growth in and near United States protected areas limits their conservation value. Proceedings of the National Academy of Sciences 107(2): 940\u0026ndash;945. DOI 10.1073/pnas.0911131107.\u003c/li\u003e\n\u003cli\u003eHenderson-Sellers, A., and Gornitz, V. (1984). Possible climatic impacts of land cover transformations, with particular emphasis on tropical deforestation. Climatic Change, 6(3), 231-257.\u003c/li\u003e\n\u003cli\u003eHoward, L., (1818). The Climate of London Deduced from Meteorological Observations. London, UK: W. Phillips.\u003c/li\u003e\n\u003cli\u003eINDIA, P. (2011). Census of India 2011 provisional population totals. New Delhi: Office of the Registrar General and Census Commissioner.\u003c/li\u003e\n\u003cli\u003eJago-on K.A.B., Kaneko S., Fujukura R., Fujiwara A., Imai T., Matsumoto T., 2009. Urbanization and subsurface environmental issues: An attempt at DPSIR model application in Asian cities. Science of the total Environment 407(9): 3089\u0026ndash;3104. DOI 10.1016/j.scitotenv.2008.08.004.\u003c/li\u003e\n\u003cli\u003eKafrawy, S. B. E., Donia, N. S., \u0026amp; Mohamed, A. M. (2017). Water quality assessment based on CWQI and NDWI indices in Mariout Lake, Egypt. MOJ Ecology Environmental Sciences, 2(5), 00039.\u003c/li\u003e\n\u003cli\u003eKawashima, S. (1994). \u0026ldquo;Relation between Vegetation, Surface Temperature and Surface Composition in the Tokyo Region during winter.\u0026rdquo; Remote Sensing of Environment 50: 52\u0026ndash;60. doi:10.1016/ 0034-4257(94)90094-9.\u003c/li\u003e\n\u003cli\u003eKhan, M. A., and Syed, N. A. (2015). Image processing techniques for automatic detection of tumor in human brain using SVM. Int J Adv Res Comput Commun Eng, 4(4).\u003c/li\u003e\n\u003cli\u003eKim Y.H., Baik J.J., (2005). Spatial and temporal structure of urban heat island in Seoul. Journal of Applied Meteorology 44: 591\u0026ndash;605. DOI 10.1175/JAM2226.1.\u003c/li\u003e\n\u003cli\u003eLandsat Project Science Office. (2002).\u0026rdquo; Landsat 7 Science Data User\u0026rsquo;s Handbook.\u0026rdquo; Goddard Space Flight Center, NASA, Washington, DC. https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/ 08/Landsat7_Handbook.pdf.\u003c/li\u003e\n\u003cli\u003eLiu J.G., Diamond J. (2005). China\u0026rsquo;s environment in a globalizing world. Nature 435: 1179\u0026ndash;1186. DOI 10.1038/4351179a.\u003c/li\u003e\n\u003cli\u003eMoisa, M. B., Dejene, I. N., \u0026amp; Gemeda, D. O. (2022). Geospatial technology\u0026ndash;based analysis of land use land cover dynamics and its effects on land surface temperature in Guder River sub-basin, Abay Basin, Ethiopia. Applied Geomatics, 14(3), 451-463.\u003c/li\u003e\n\u003cli\u003eNorman, Michael John Thornley; Pearson, C.J; Searle, P.G.E (1995). The ecology of tropical food crops. Cambridge University Press. pp. 249\u0026ndash;251. \u003c/li\u003e\n\u003cli\u003eOhwo O., Abotutu A., (2015). Environmental Impact of Urbanisation in Nigeria. British Journal of Applied Science and Technology 9(3): 212\u0026ndash;221. DOI 10.9734/bjast/2015/18148.\u003c/li\u003e\n\u003cli\u003eOke T.R (1987). \u0026quot;Boundary Layer Climates\u0026quot;. London. Methuen, pp.33-76.\u003c/li\u003e\n\u003cli\u003eOke T.R, (1995). \u0026quot;The heat island of the urban boundary layer: characteristics, causes and effects\u0026quot;. In J.E. Cermak et al (eds.). Wind climate in cities, pp.81-107.\u003c/li\u003e\n\u003cli\u003eOke, T. R. (1982). The energetic basis of the urban heat island. Quarterly journal of the royal meteorological society, 108(455), 1-24.\u003c/li\u003e\n\u003cli\u003eOke, T. R., and Cleugh, H. A., (1987). Urban heat storage derived as energy balance residuals. Boundary-Layer Meteorology, 39(3), 233-245.\u003c/li\u003e\n\u003cli\u003eOmran, E. E. (2012). \u0026ldquo;Detection of Land-Use and Surface Temperature Change at Different Resolutions.\u0026rdquo; Journal of Geographic Information System 4: 189\u0026ndash;203. doi:10.4236/jgis.2012.43024.\u003c/li\u003e\n\u003cli\u003eParker, D. E. (2010). Urban heat island effects on estimates of observed climate change. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 123-133.\u003c/li\u003e\n\u003cli\u003eQiao, Z., G. Tian, and L. Xiao. (2013). \u0026ldquo;Diurnal and Seasonal Impacts of Urbanization on Theurban Thermal Environment: A Case Study of Beijing Using MODIS Data.\u0026rdquo; ISPRS Journal of Photogrammetry and Remote Sensing 85: 93\u0026ndash;101. doi: 10.1016/j.isprsjprs.2013.08.010.\u003c/li\u003e\n\u003cli\u003eRadeloff V.C., Stewart S.I., Hawbaker T.J., Gimmi U., Pidgeon A.M., Flather C.H., Hammer R.B., Ramachandraia, C. (2013). Drinking water: issues in access and equity. PDF). Jointactionforwater.org. Archived from the original (PDF) on, 10 November 2013. Retrieved 18 November 2012.\u003c/li\u003e\n\u003cli\u003eRao, P. K. (1972). \u0026ldquo;Remote Sensing of Urban \u0026ldquo;Heat Islands\u0026rdquo; from an Environmentalsatellite.\u0026rdquo; Bulletin of the American Meteorological Society 53: 647\u0026minus;648.\u003c/li\u003e\n\u003cli\u003eSabiha Sultana and A.N.V. Satyanarayana (2018): Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization, International Journal of Remote Sensi.ng, DOI: 10.1080/01431161.2018.1466072.\u003c/li\u003e\n\u003cli\u003eSaito I., Ishihara O. and Katayama T. (1990). \u0026quot;Study of the effect of green areas on the thermal environment in an urban area\u0026quot;, Energy and Buildings, Vol. 15-16, pp.443-446.\u003c/li\u003e\n\u003cli\u003eSchell L.M., Smith M.T. and Billsborough A., \u0026quot;Human biological approaches to the study of third world urbanism\u0026quot; (1993). In Schell.L.M. Smith M.T. and Billsborough A. (eds.). Urban ecology and health in the 3rd world, Cambridge University Press, pp. 1-9.\u003c/li\u003e\n\u003cli\u003eSeto K.C., Fragkias M., Guneralp B., Reilly M.K. (2011). A meta-analysis of global urban land expansion. PLoS one 6(8): 1\u0026ndash;9. DOI 10.1371/journal.pone.0023777.\u003c/li\u003e\n\u003cli\u003eSobrino, J. A., R. Oltra-Carri\u0026oacute;, G. S\u0026ograve;ria, R. Bianchi, and M. Paganini. (2012). \u0026ldquo;Impact of Spatial Resolution and Satellite Overpass Time on Evaluation of the Surface Urban Heat Island Effects.\u0026rdquo; Remote Sensing of Environment 117: 50\u0026ndash;56. doi: 10.1016/j.rse.2011.04.042.\u003c/li\u003e\n\u003cli\u003eTian, B., Wang, L., Kashiwaya, K., \u0026amp; Koike, K. (2015). Combination of well-logging temperature and thermal remote sensing for characterization of geothermal resources in Hokkaido, northern Japan. Remote Sensing, 7(3), 2647-2667.\u003c/li\u003e\n\u003cli\u003eTouchaei, A. G., and Wang, Y., (2015). Characterizing urban heat island in Montreal (Canada)\u0026mdash;effect of urban morphology. Sustainable Cities and Society, 19, 395-402.\u003c/li\u003e\n\u003cli\u003eUN [United Nations Department of Economic and Social Affairs, Population Division], (2014). The 2014 Revision World Urbanization Prospects. United Nations, New York. Online: un.org/en/desa/2014-revision-world-urbanization-prospects (accessed 12 March 2020).\u003c/li\u003e\n\u003cli\u003eUN [United Nations Department of Economic and Social Affairs, Population Division], (2018). The 2018 revision world urbanization prospects. United Nations, New York. Online: un.org/development/desa/publications (accessed 12 March 2020).\u003c/li\u003e\n\u003cli\u003eVancutsem, C., P. Ceccato, T. Dinku, and S. J. Connor. (2010). \u0026ldquo;Evaluation of MODIS Land Surface Temperature Data to Estimate Air Temperature in Different Ecosystems over Africa.\u0026rdquo; Remote Sensing of Environment 114: 449\u0026ndash;465. doi: 10.1016/j.rse.2009.10.002.\u003c/li\u003e\n\u003cli\u003eWang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., \u0026amp; Zhao, S. (2015). An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote sensing, 7(4), 4268-4289.\u003c/li\u003e\n\u003cli\u003eWeng, Q. (2009). \u0026ldquo;Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: Methods, Applications, and Trends (Review Article).\u0026rdquo; ISPRS Journal of Photogrammetry and Remote Sensing 64: 335\u0026ndash;344. doi: 10.1016/j.isprsjprs.2009.03.007.\u003c/li\u003e\n\u003cli\u003eXu, Y., Yang, J., \u0026amp; Chen, Y. (2016). NDVI-based vegetation responses to climate change in an arid area of China. Theoretical and Applied Climatology, 126, 213-222.\u003c/li\u003e\n\u003cli\u003eYasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., \u0026amp; Noor, N. M. (2022, October). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. In IOP Conference Series: Earth and Environmental Science (Vol. 1067, No. 1, p. 012037). IOP Publishing.\u003c/li\u003e\n\u003cli\u003eYimene, Ababu Minda (2004). An African Indian community in Hyderabad. Cuvillier Verlag. pp. 5\u0026ndash;6. ISBN 978-3-86537-206-2.\u003c/li\u003e\n\u003cli\u003eZhao S.Q., Da L.J., Tang Z.Y., Fang H.J., Song K., Fang J.Y. (2006). Ecological consequences of rapid urban expansion: Shanghai, China. Frontier Ecology and Environment 4(7): 341\u0026ndash; 346. DOI 10.1890/1540-9295(2006)004[0341: ECORUE] 2.0. CO; 2.\u003c/li\u003e\n\u003cli\u003eZhao, H., and Chen, X. (2005, July). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International geoscience and remote sensing symposium (Vol. 3, p. 1666).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land surface temperature, Land use/ land cover, Support Vector Machine, Indices, Hyderabad city","lastPublishedDoi":"10.21203/rs.3.rs-3873203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3873203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand Surface Temperature (LST) is a crucial parameter for assessing the thermal comfort of urban residents. This study investigates the impact of land use/land cover changes on the variations in land surface temperature from 1989 to 2019 at 10-year intervals in Hyderabad city, Telangana. The mono window and split window algorithms were employed to derive LST, while the contribution index was utilized to analyze changes in the contribution of land use/land cover (LULC) to LST. The built-up area has witnessed a notable increase from 35.81\u0026ndash;56.49%, accompanied by corresponding decreases in barren land (42.73\u0026ndash;33.42%), vegetation (19.39\u0026ndash;8.20%), and water bodies (2.07% to 8.20). The study further indicates that barren land significantly contributes to LST, with a decreasing trend observed from 1989 to 2019. The mitigating effects of water bodies (-0.14 to -0.1) and vegetation (-0.42 to -0.06) on LST have diminished over the same period. Additionally, a decline in Normalized Difference Vegetation Index (NDVI) for vegetation and Normalized Difference water Index (NDWI) for water bodies reflects increased stress and pollution in their respective LULC areas. Furthermore, the decrease in the Normalized Difference Barren Land Index (NDBaI) and Normalized Difference Built-up Index (NDBI) depicts urban expansion and the transformation of primary barren land to cultivation. This research enhances our understanding of how shifting landscapes influence a material's surface energy budget. Analyzing the interplay between land cover and incoming radiation throughout the day provides insights into the effects of climate change.\u003c/p\u003e","manuscriptTitle":"Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 12:03:19","doi":"10.21203/rs.3.rs-3873203/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c80ac870-ed7a-49e7-a041-7bfff0d81729","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-25T13:40:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 12:03:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3873203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3873203","identity":"rs-3873203","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}