Investigating Urban Heat Island Effects and Mitigation Strategies in Ahmedabad, India

Investigating Urban Heat Island Effects and Mitigation Strategies in Ahmedabad, India | 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 Investigating Urban Heat Island Effects and Mitigation Strategies in Ahmedabad, India Durgesh Singh, Pradeep Kumar Rajput This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5309216/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 An urban heat island is an urban area that is significantly warmer due to anthropogenic activity and unplanned development of city that is surrounded rural area. The temperature difference usually is high at night than the day temperature. The urban heat island is particularly noticeable during the summer and winter months. The main cause of the urban heat island effect is the form the disturb of natural setting and modification of land surface area. As the per the United nation report worldwide rapid growth of urbanization and globalization brought more than 54% of world’s population in urban areas which is only around 33% 50 years back and it expected to increases up 66% by the end of 2050. In this paper to investigate the relationship between land surface temperature and biophysical parameter in the selected area, satellite data used for extraction of biophysical and LST parameters for the study of urban heat island and its effects. To investigate and understand the effects land surface temperature, form the period of 1990 2000 and 2019 satellite data are used to retrieve the land surface temperature (LST), Normalized vegetation index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Built-up and Bareness Index (NDBaL). The biophysical parameters of different time period are used to analysis the behavior change over the LST. Land cover indices were derived in order established the relationship between LST and the indices, in the result a higher level of LST was found to be associated with the lower NDVI in this research. Urban planning LST vegetation and environmental parameters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Highlights The study investigates Urban Heat Island (UHI) effects in Ahmedabad, India, focusing on temperature disparities between urban and rural areas. Key findings reveal that rapid urbanization and loss of vegetation contribute significantly to increased temperatures in the city. The research highlights the effectiveness of mitigation strategies such as enhancing urban green spaces, implementing cool roofs, and promoting sustainable urban planning. Remote sensing and GIS techniques are employed to analyze land use changes and their impact on UHI. The study also emphasizes the role of community awareness and policy interventions in mitigating UHI effects, ultimately aiming to improve urban resilience and reduce energy consumption in Ahmedabad. The findings provide valuable insights for sustainable urban development in similar climates. 1. INTRODUCTION An urban heat island (UHI) is an urban or metropolitan area that experiences significantly higher temperatures than its surrounding rural areas due to human activities. The temperature difference is usually more pronounced at night than during the day and is most noticeable when winds are weak. UHI effects are most significant during summer and winter. [ 1 ] The alteration and use of land surfaces is the primary cause of the urban heat island effect; waste heat from energy use is a secondary factor. As urban populations grow, the area of UHIs expands and their average temperature increases. [ 2 ] Essentially, a heat island refers to any area, whether populated or not, that is consistently hotter than its surroundings. According to a United Nations report, rapid urbanization and globalization have increased the world's urban population from around 33% fifty years ago to over 54% today, with projections of up to 66% by 2050. [ 3 ] The report also suggests that Asian and African countries will experience faster rates of urbanization compared to other continents. While urbanization brings prosperity and development, it also negatively impacts the global ecological environment. Among the environmental effects of rapid urbanization, the urban thermal environment, exemplified by the UHI phenomenon, has become a significant urban environmental issue. [ 4 ] Research in the United States shows a relationship between extreme temperatures and mortality, with heat posing a greater risk in northern cities than in southern regions. Concerns have been raised about the potential contribution of UHIs to global warming. Studies from China and India indicate that the UHI effect contributes to climate warming by about 30%. However, a 1999 study comparing urban and rural areas found that UHI effects have little impact on global mean temperature trends. [ 5 ] Many studies indicate that the severity of UHIs increases with the progression of climate change. Research has shown a relationship between UHIs and patterns of land cover changes, with vegetation and water presence reducing UHI intensity, while increased urbanization intensifies it. Various indices such as the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) have been used to represent land cover changes over analyzed time periods. [ 6 ] The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator used to analyze remote sensing measurements, typically from space platforms, to assess the presence of live green vegetation in a target location. To improve upon NDVI, Huete developed the Soil-Adjusted Vegetation Index (SAVI), which accounts for the differential extinction of red and near-infrared light through vegetation canopies. SAVI minimizes soil brightness influences in spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The Normalized Difference Water Index (NDWI) is used to monitor changes related to water content in water bodies and to assess whether a target location is experiencing floods or scarcity. The Difference Normalized Urban areas tend to show higher reflectance in the shortwave-infrared (SWIR) region than in the near-infrared (NIR) region; this is why the Built-up Index (NDBI) emphasizes these areas. [ 7 ] The surface and atmosphere's thermal, radiative, moisture, and aerodynamic qualities change as green spaces are replaced by structures and roadways. Urban construction materials have different thermal and radiative properties compared to rural or vegetated areas, resulting in greater absorption and storage of the sun’s energy in urban surfaces. Additionally, the height and arrangement of buildings affect the rate at which the absorbed energy escapes at night. As a result, metropolitan areas continue to have relatively higher nighttime air temperatures because they cool more slowly than rural locations. [ 9 ] The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator used to analyze remote sensing measurements, typically from space platforms, to assess vegetation health. Additional factors contributing to the Urban Heat Island (UHI) effect include scattered and emitted radiation from atmospheric pollutants in urban areas, waste heat production from air conditioning and refrigeration systems, industrial processes, motorized vehicular traffic (i.e., anthropogenic heat), and the obstruction of rural air flows by the windward faces of built-up surfaces. The impacts of urbanization, such as pollution production, waste heat from human activities (notably from air conditioners and internal combustion engines), modifications to the physical and chemical properties of the atmosphere, and soil surface covering, have become more apparent, leading to the UHI phenomenon.[ 10 ] There is a direct relationship between UHI intensity peaks and heat-related illnesses and fatalities, as thermal discomfort affects the human cardiovascular and respiratory systems. 2. STUDY AREA Ahmedabad, located in the vibrant state of Gujarat, India, is a city rich in historical, cultural, and economic significance. With a heritage that stretches back centuries, it has grown into a bustling metropolis while maintaining its traditional charm. The city's foundation by Sultan Ahmed Shah in the 15th century marked the beginning of its prominence, and it became an important center of trade and commerce during the Mughal era. Ahmedabad also played a pivotal role in India's independence movement, with the Sabarmati Ashram serving as a key site in Mahatma Gandhi's efforts. Today, Ahmedabad stands as Gujarat's economic powerhouse, with industries such as textiles, pharmaceuticals, and information technology driving its economy. The city's entrepreneurial spirit has attracted significant investments, both domestic and international, while its thriving informal sector provides essential livelihoods to a large portion of the population. Ahmedabad's rapid urbanization has led to considerable infrastructural development, including notable projects like the Sabarmati Riverfront and the Bus Rapid Transit System (BRTS), improving public spaces and transportation connectivity. However, urban sprawl and inadequate planning raise concerns about sustainability and equitable growth. The city's cultural heritage, recognized by its UNESCO World Heritage City designation in 2017, includes remarkable architectural landmarks like the Jama Masjid and Sidi Saiyyed Mosque, alongside traditional pols (housing clusters). Festivals such as Uttarayan and Navratri reflect Ahmedabad's vibrant cultural fabric, attracting tourists worldwide. Despite its many advancements, the city faces environmental challenges, particularly air and water pollution due to industrial activity and population growth. Climate change further aggravates these issues, highlighting the need for climate adaptation and mitigation strategies. Ahmedabad exemplifies the fusion of tradition and modernity, where its rich historical past coexists with contemporary aspirations. As the city navigates the complexities of urbanization and globalization, efforts towards sustainable development, inclusive growth, and the preservation of its unique identity are crucial for its future progress. 3. MATERIALS AND METHODS 3.1 Data Used The data description covers imagery captured by three generations of Landsat satellites data such as Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8. Each satellite features advanced sensors capable of capturing multispectral imagery across various wavelengths, enabling comprehensive earth observation. Landsat 5 TM, launched in 1984 and operational until 2013, provided seven spectral bands with spatial resolutions ranging from 15 to 120 meters. Landsat 7 ETM+, launched in 1999, introduced enhancements such as a new panchromatic band and improved thermal sensitivity. Although an issue with its scan line corrector caused systematic gaps in the imagery, Landsat 7 continued to supply valuable data until the launch of Landsat 8. Landsat 8, launched in 2013, offers significant advancements, including improved radiometric and spatial resolution compared to its predecessors. Equipped with the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 8 provides a total of eleven spectral bands, including two thermal bands, allowing for more detailed and accurate observation of the Earth's surface. These datasets from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 are invaluable resources for various applications, including land cover mapping, environmental monitoring, urban planning, agricultural management, and scientific research. 3.2 Estimation of parameters Figure:2 show the outline extraction of parameter use satellite data. here use of thermal infrared bands from different Landsat image types, including band 6 of Landsat 5 TM, Landsat 7 ETM+, and band 10 of Landsat 8, were leveraged for Land Surface Temperature (LST) estimation in the inter-municipal grouping of Guelma. Due to striping issues, only band 10 of Landsat 8 was utilized from the Landsat OLI-TIRS thermal bands. A single window algorithm, as proposed by Xiaolei et al. (2014) and based on NDVI, was employed to derive Land Surface Emissivity (LSE). The following steps outline the process utilized to retrieve LST from thermal and NDVI images 3.2.1 Radiance image calculation Radiance is a measure of the energy emitted by a surface or object in a given direction per unit area and solid angle. It is commonly used in remote sensing to quantify the amount of electromagnetic radiation (light) detected by a sensor. Calculating radiance from digital numbers (DN) in remote sensing imagery requires specific calibration parameters. Here's a general process to calculate radiance from satellite image data. Eq. (1)(2) $$\:{\varvec{L}}_{\varvec{\lambda\:}=\:\:\:}\frac{({\varvec{L}\varvec{M}\varvec{A}\varvec{X}}_{\varvec{\lambda\:}}-{\varvec{L}\varvec{M}\varvec{I}\varvec{N}}_{\varvec{\lambda\:}})}{({\varvec{Q}\varvec{C}\varvec{A}\varvec{L}}_{\varvec{m}\varvec{a}\varvec{x}}-{\varvec{Q}\varvec{C}\varvec{A}\varvec{L}}_{\varvec{m}\varvec{i}\varvec{n}})}\mathbf{*}\left(\varvec{Q}\varvec{C}\varvec{A}\varvec{L}-{\varvec{Q}\varvec{C}\varvec{A}\varvec{L}}_{\varvec{m}\varvec{i}\varvec{n}}\right)+{\varvec{L}\varvec{M}\varvec{I}\varvec{N}}_{\varvec{\lambda\:}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(1\right)$$ $$\:{\varvec{L}}_{\varvec{\lambda\:}}=\:{\varvec{M}}_{\varvec{L}}\mathbf{*}\:{\varvec{Q}}_{\varvec{C}\varvec{A}\varvec{L}}+{\varvec{A}}_{\varvec{L}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(2\right)}$$ 3.2.2 Radiance temperature calculation In remote sensing research, radiance temperature is critical for monitoring surface energy balances, climate studies, and land surface temperature (LST) estimations. For example, in a study by Li et al. (2013), researchers used thermal infrared satellite data to estimate surface temperatures across urban areas, providing insights into the urban heat island effect. Similarly, Kalma et al. (2008) focused on how radiance temperature can be employed to measure evapotranspiration in agricultural fields, improving water resource management. In many research papers, radiance temperature is calculated by correcting for atmospheric effects and sensor-specific factors. Weng et al. (2004) explored the relationship between radiance temperature, land surface temperature, and emissivity, showing how corrections for atmospheric interference can improve the accuracy of temperature measurements in satellite imagery. These methods help derive accurate surface temperature data from thermal sensors such as those on Landsat or MODIS satellites. Eq. (3) $$\:\varvec{T}=\:\frac{\varvec{k}2}{\begin{array}{c}In\left(\varvec{K}1+1\right)\:\:\:\:\\\:L\lambda\:\end{array}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(3\right)$$ 3.2.3 Emissivity calculation There are several methods to calculate emissivity, ranging from laboratory-based measurements to satellite-based retrievals. Laboratory measurements typically involve spectrometers that measure emitted radiation at different wavelengths, which are then compared to a black body reference. However, for large-scale environmental studies, satellite-based methods are more practical. Zhang et al. (2009) proposed using thermal infrared satellite data to calculate surface emissivity, where they employed the Temperature Emissivity Separation (TES) algorithm. This method involves using multiple spectral bands to estimate both temperature and emissivity simultaneously, making it possible to derive these parameters from remote sensing data in various environmental conditions. Eq. (4) (5) $$\:{\varvec{p}}_{\varvec{v}}=(\:\:\frac{\varvec{N}\varvec{V}\varvec{D}\varvec{I}-{\varvec{N}\varvec{V}\varvec{D}\varvec{I}}_{\varvec{m}\varvec{i}\varvec{n}}}{{\varvec{N}\varvec{D}\varvec{V}\varvec{I}}_{\varvec{m}\varvec{a}\varvec{x}}-{\varvec{N}\varvec{D}\varvec{V}\varvec{I}}_{\varvec{m}\varvec{i}\varvec{n}}}{)}^{2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.(4)$$ $$\:\varvec{\epsilon\:}=0.004{\varvec{p}}_{\varvec{\nu\:}+0.986}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(5\right)$$ LST calculation LST is widely used to study the Urban Heat Island effect, where urban areas tend to be significantly warmer than their rural surroundings due to human activities and infrastructure. Voogt and Oke (2003) provided one of the foundational frameworks for analyzing UHI using satellite-derived LST. Recent studies like Li et al. (2021) and Peng et al. (2022) have further explored how LST data can inform urban planning and green infrastructure development to mitigate UHI effects. Eq. (6) $$\:\varvec{L}\varvec{S}\varvec{T}=\frac{{\varvec{T}}_{\varvec{\kappa\:}}}{1+\left(\frac{\varvec{\lambda\:}{\varvec{T}}_{\varvec{\kappa\:}}}{\varvec{p}}\right)\varvec{I}\varvec{n}\:\varvec{\epsilon\:}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(6\right)$$ 3.2.5 Biophysical indices Biophysical indices related to radiation play an essential role in understanding the energy balance of ecosystems. Indices like the Photochemical Reflectance Index (PRI) are used to assess photosynthetic efficiency and light use efficiency in vegetation. PRI is sensitive to changes in the xanthophyll cycle pigments, which are directly related to plant stress responses to excess light or water limitations (Gamon et al., 1997). This index is useful in studying how ecosystems respond to environmental stresses such as drought, heat waves, or nutrient deficiencies, making it a powerful tool for assessing the impact of climate change on plant productivity. Eq. (7), (8), (9), (10) $$\:\varvec{N}\varvec{V}\varvec{D}\varvec{I}=\frac{\left(\varvec{N}\varvec{I}\varvec{R}-\varvec{R}\varvec{E}\varvec{D}\right)}{\left(\varvec{N}\varvec{I}\varvec{R}+\varvec{R}\varvec{E}\varvec{D}\right)\:\:\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(7\right)$$ $$\:\varvec{N}\varvec{D}\varvec{B}\varvec{I}=\:\frac{(\varvec{M}\varvec{I}\varvec{R}-\varvec{N}\varvec{I}\varvec{R})}{(\varvec{M}\varvec{I}\varvec{R}+\varvec{N}\varvec{I}\varvec{R})}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varvec{e}\varvec{q}.\left(8\right)$$ $$\:NDBal=\:\frac{SWIR-TIRS1}{SWIR+TIRS1\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:eq.\left(9\right)$$ $$\:NDWI=\frac{\left(GREEN-NIR\right)}{\:\left(GREEN+NIR\right)\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:eq.\left(10\right)$$ 4. RESULTS AND DISCUSSION 4.2 Consequence of Urban Heat Island The urban heat island (UHI) phenomenon describes the tendency for urban areas to experience higher temperatures than surrounding rural regions due to human activities and continuous urbanization. A primary factor in the formation and intensification of UHI is the replacement of natural land covers, such as vegetation and water bodies, with impervious surfaces like asphalt and concrete [ 11 ]. These surfaces absorb and retain heat, leading to increased surface temperatures. Additionally, the dense concentration of buildings and infrastructure in urban areas reduces vegetation cover, which in turn decreases evapotranspiration and cooling effects. Other contributors to UHI include heat emissions from vehicles, industries, and air conditioning systems, as well as changes in local wind patterns and atmospheric circulation [ 12 ]. The impacts of UHI are significant for the environment, society, and public health related problems occurs. Higher temperatures in urban areas can increase the incidence of heat-related illnesses and mortality, particularly during heatwaves, posing greater risks to vulnerable populations such as the elderly, children, and individuals with pre-existing health conditions. UHI effects can also disrupt ecosystems, alter biodiversity patterns, and negatively impact agricultural productivity. Additionally, UHI can worsen air pollution levels by promoting the formation of ground-level ozone and other pollutants, which can have harmful effects on respiratory health and exacerbate conditions like asthma to human. study the causes and consequences of UHI requires comprehensive future urban planning and mitigation strategies. These may include increasing green spaces and vegetation cover through urban forestry initiatives, promoting sustainable building design and materials to reduce heat absorption and improve energy efficiency, implementing cool roof and cool pavement technologies to mitigate surface heat retention, and integrating green infrastructure such as green roofs and permeable pavements to enhance urban cooling and storm water management. Additionally, community-based adaptation measures such as public awareness campaigns, heat emergency response plans, and the provision of cooling centers can help mitigate the health impacts of UHI on vulnerable populations. 4.3 City Planning and Development City planning and development is a multifaceted process that involves shaping the physical, social, economic, and environmental aspects of urban areas. This process includes the formulation, implementation, and evaluation of strategies and policies aimed at guiding urban growth and transformation in a sustainable and equitable manner. Effective city planning and development are crucial for creating livable, resilient, and inclusive urban environments that cater to the needs and aspirations of both current and future generations needs planning. City planning and development encompass various components such as land use planning, infrastructure development, transportation systems, housing provision, environmental management, economic development, and social equity [ 13 ]. These components are interconnected and interdependent, requiring integrated and holistic approaches. Land use planning is a fundamental aspect of city planning, involving the allocation and regulation of land for residential, commercial, industrial, recreational, and green spaces [ 14 ]. It aims to optimize land utilization, promote compact and mixed-use development, prevent urban sprawl, and protect valuable natural and cultural assets. To reduce the effects of heat island, land use planning fosters sustainable development, promotes social inclusion, and enhances the quality of life for urban residents. Infrastructure development, another key component, includes the provision of essential services and facilities such as water supply, sanitation, energy, transportation, telecommunications, and waste management. Investments in infrastructure are vital for supporting urban growth, enhancing connectivity, improving accessibility, and ensuring the well-being of urban populations. Sustainable and resilient infrastructure is essential for mitigating climate change impacts, reducing vulnerability to natural hazards, and promoting environmental sustainability [ 15 ]. Hence, city planning and development play a crucial role in shaping the future of urban areas, influencing the quality of life, sustainability, and resilience of cities. Effective city planning and development require integrated and participatory approaches that involve collaboration between government agencies, private sector stakeholders, civil society organizations, and local communities. By adopting sustainable, inclusive, and equitable practices, cities can create vibrant, resilient, and livable urban environments that promote the well-being and prosperity of all residents. 4.3 Mitigation Planning Mitigation planning for land surface temperature (LST) involves strategic interventions aimed at reducing the impacts of urban heat islands (UHIs), heatwaves, and climate change on human health, ecosystems, and urban infrastructure. LST, a key indicator of land surface thermal properties, is influenced by factors such as land cover, land use, vegetation, building materials, and urban morphology. As urbanization intensifies globally, mitigating the adverse effects of rising LST has become crucial in urban planning and climate resilience strategies. [ 16 ] Urban areas typically experience higher LST than surrounding rural areas due to the urban heat island effect, characterized by the absorption and retention of solar radiation by built-up surfaces, reduced vegetation cover, and anthropogenic heat emissions. Elevated LST contributes to various environmental and societal challenges, including increased energy consumption, heat-related illnesses, reduced air quality, and biodiversity loss. Therefore, effective mitigation planning strategies are essential to alleviate these impacts and create healthier, more sustainable urban environments. One effective approach to mitigating the impacts of elevated Land Surface Temperature (LST) is through urban greening initiatives that increase vegetation covers and enhance natural cooling mechanisms. Green spaces such as parks, urban forests, and green roofs help mitigate Urban Heat Island (UHI) effects by providing shade, evaporative cooling, and reducing surface temperatures through transpiration. Research indicates that the strategic placement of green infrastructure can significantly lower LST and improve thermal comfort in urban areas. [ 17 ] Additionally, cool roofing and cool pavement technologies are emerging as effective strategies to reduce LST. These materials, characterized by higher solar reflectance and lower thermal conductivity compared to traditional surfaces, reduce the absorption of solar radiation and lower surface temperatures. Implementing these technologies at a city scale can lead to substantial reductions in LST, energy consumption, and greenhouse gas emissions. Moreover, land use planning and urban design strategies are crucial in mitigating LST by promoting compact, mixed-use development patterns, preserving green spaces, and incorporating passive cooling measures into building design. Techniques such as increased building setbacks, optimizing street orientation, and using high-albedo materials can minimize heat retention and enhance natural ventilation in urban environments. [ 18 ] In conclusion, mitigation planning in the context of LST is essential for addressing the challenges posed by urban heat islands, heatwaves, and climate change in cities worldwide. By implementing a combination of nature-based solutions, cool technologies, urban design strategies, community engagement initiatives, and advanced technologies, cities can effectively reduce LST, enhance urban livability, and build climate-resilient communities. However, successful mitigation planning requires holistic, interdisciplinary approaches that integrate scientific knowledge, stakeholder perspectives, and policy frameworks to create sustainable, equitable, and resilient urban environments for current and future generations. 4.4 Vegetation Structure The relationship between vegetation structure and land surface temperature (LST) is a crucial aspect of understanding the dynamics of terrestrial ecosystems and their response to environmental changes. Vegetation structure encompasses various physical characteristics of plant communities, including canopy cover, leaf area index (LAI), canopy height, and biomass distribution [ 19 ]. These structural attributes influence the energy balance of the land surface, thereby affecting LST patterns at local, regional, and global scales. Canopy cover, or the proportion of ground covered by vegetation canopy, plays a significant role in modulating LST by influencing the amount of incoming solar radiation absorbed or reflected by the land surface. Dense vegetation canopies intercept a larger fraction of solar radiation, leading to lower surface temperatures through shading and evapotranspiration processes. In contrast, sparse vegetation cover allows more solar radiation to reach the land surface, resulting in higher LST due to increased absorption of solar energy [ 20 ]. Leaf area index (LAI) is another important vegetation structural parameter that influences LST dynamics. LAI is the total leaf area per unit ground area and is strongly correlated with canopy density and photosynthetic activity. High LAI values indicate dense vegetation canopies with abundant leaf cover, which can reduce LST by enhancing evaporative cooling through transpiration. Conversely, low LAI values are associated with sparse vegetation cover and higher LST due to reduced shading and transpiration [ 21 ]. Canopy height, or the vertical extent of vegetation, also influences LST by affecting the exchange of energy and moisture between the land surface and the atmosphere. Tall vegetation canopies intercept more solar radiation and promote greater turbulence within the boundary layer, leading to enhanced cooling through convective heat transfer and increased evapotranspiration rates. Shorter vegetation, on the other hand, may have limited cooling effects and can contribute to higher LST by allowing more solar radiation to reach the land surface. Biomass distribution within vegetation canopies can further modulate LST patterns by altering the partitioning of energy and water fluxes within the ecosystem [ 22 ]. Biomass distribution affects the distribution of surface roughness elements, which in turn influences aerodynamic resistance and heat exchange processes between the land surface and the atmosphere [ 23 ]. Moreover, biomass distribution impacts the spatial variability of evapotranspiration rates and surface albedo, both of which play critical roles in regulating LST. Several studies have investigated the relationship between vegetation structure and LST across different ecosystems and land cover types. examined the impact of canopy cover and LAI on LST in urban areas using remote sensing data. In conclusion, vegetation structure plays a critical role in shaping land surface temperature patterns through its influence on energy balance processes and surface-atmosphere interactions [ 24 ]. Understanding the complex interactions between vegetation structure and LST is essential for ecosystem monitoring, climate modeling, and land management practices. Incorporating vegetation structural parameters into LST modeling frameworks can improve our ability to predict and mitigate the impacts of environmental changes on terrestrial ecosystems and human communities. [ 25 ] 4.5 Spatial Distribution of LST NDVI NDWI NDBaL and NDBI 4.6 Association between LST and NDVI The figure:6 show a linear regression model where the coefficient of determination in 1990 R² is 0.7986. This indicates that approximately 79.86% of the variability in NDVI can be explained by its linear relationship with LST. The regression equation suggests a negative relationship between LST and NDVI, meaning that higher LST values are associated with lower NDVI values. This inverse relationship is commonly observed in vegetation studies, as high temperatures can stress vegetation, resulting in decreased photosynthetic activity and, consequently, lower NDVI values. The coefficient of determination in 2000 R² indicates the strength of the linear relationship between LST and NDVI. In this case, an R² value of 0.7986 suggests a relatively strong correlation between the two variables, indicating that the model provides a good fit to the observed data points. However, it is important to consider other factors that may influence NDVI variability, as the model might not capture all sources of variation. The R 2 value of 2019 is 0.9578 suggests that about 95.78% of the variability in NDVI, the dependent variable, can be attributed to changes in LST, the independent variable, in this linear regression model. This high R 2 indicates a robust fit of the model to the data, demonstrating its ability to accurately predict NDVI based on LST. This underscores the significant and strong relationship observed between NDVI and LST in the year 2000, emphasizing temperature's crucial role in influencing vegetation health and dynamics. An R² value of 0.8857 indicates that approximately 88.57% of the variation in land surface temperature (LST) can be explained by normalized difference vegetation index (NDVI) using the linear regression model. This strong R² suggests a robust relationship between NDVI and LST for the year 2019, highlighting how vegetation density influences surface temperature dynamics. This insight is valuable for applications such as environmental monitoring, ecosystem analysis, and agricultural management, offering significant understanding into land surface processes based on vegetation indices. 4.7 Association between LST and NDWI The provided correlation coefficients (R 2 values) between Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST) for the years 1990, 2000, and 2019 indicate the strength and direction of their relationship. In 1990, the R 2 value of 0.9653 suggests a very strong positive correlation between NDWI and LST. This indicates that as the water presence, as indicated by NDWI, increases, the land surface temperature tends to increase as well. This could be attributed to the fact that water bodies, being good absorbers of solar radiation, can contribute to higher temperatures in their vicinity. In 2000, the R 2 value of 0.9227 indicates a strong positive correlation between NDWI and LST, similar to the trend observed in 1990. Again, this suggests that areas with higher water presence tend to have higher land surface temperatures. In 2019, the R 2 value of 0.8709 suggests a slightly weaker but still significant positive correlation between NDWI and LST compared to the previous years. However, the correlation remains strong, indicating that the relationship between water presence and land surface temperature persists over time. Overall, these findings underscore the consistent positive relationship between NDWI and LST over the examined years, highlighting the influence of water bodies on land surface temperatures in the respective years. 4.8 Association Between LST and NDBI This research explores the relationship between the Normalized Difference Built-up Index (NDBI) and land surface temperature (LST) in Ahmedabad city, revealing a strong positive correlation between these variables. The coefficient of determination (R²) for the relationship between NDBI and LST is presented for the years 1990, 2000, and 2019. For 2019, the R² value is 0.8975, indicating a strong positive correlation. This high R² value, close to 1, implies that 89.75% of the variation in LST can be attributed to the variation in NDBI for that year. The Normalized Difference Built-up Index (NDBI) is used to measure the extent of built-up areas in satellite images, while Land Surface Temperature (LST) indicates the temperature of the Earth's surface as detected by satellite sensors. A strong positive correlation between NDBI and LST implies that regions with higher concentrations of built-up areas typically exhibit higher surface temperatures. This is often due to the heat-retaining characteristics of construction materials like concrete and asphalt, which result in elevated surface temperatures compared to areas covered by natural vegetation or bare soil. Understanding the relationship between NDBI and LST is valuable for various applications, including urban heat island monitoring, land use planning, and climate change studies. By analyzing the correlation between these two variables, researchers can gain insights into the spatial distribution of urbanization and its impacts on local climate conditions. 4.9 Association between NDBaL and LST The coefficient of determination (R²) values obtained for the relationship between the Normalized Difference Built-up Index (NDBaI) and Land Surface Temperature (LST) across different years demonstrate a strong correlation between these variables. In 1990, the R² value of 0.9982 indicates that approximately 99.82% of the variability in LST is explained by variations in NDBaI. Similarly, in 2000 and 2019, the R² values of 0.9866 and 0.9825 respectively show that around 98.66% and 98.25% of the variability in LST can be attributed to changes in NDBaI. The high R² values demonstrate a strong relationship between NDBaI and LST over the three years examined. NDBaI, a metric derived from satellite imagery that indicates the presence and density of built-up areas, proves to be a reliable predictor of land surface temperature. This strong correlation implies that urbanization and the density of built-up areas significantly impact land surface temperature patterns. The high R² values demonstrate a strong relationship between NDBaI and LST over the three years examined. NDBaI, a metric derived from satellite imagery that indicates the presence and density of built-up areas, proves to be a reliable predictor of land surface temperature. This strong correlation implies that urbanization and the density of built-up areas significantly impact land surface temperature patterns. In conclusion, the analysis underscores the strong relationship between NDBaI and LST across multiple years, as evidenced by the consistently high R² values. This highlights the importance of considering built-up density as a key factor influencing land surface temperature patterns, and it emphasizes the potential of NDBaI as a valuable tool for assessing urban heat island effects and informing urban planning strategies aimed at mitigating temperature-related impacts in urban areas. 4.10 Association between Biophysical Parameter and LST for the Year 1990 Table 1 Correlation Matrix of Environmental Parameters for the Year 1990 1990 Year LST NDVI NDBI NDBaI NDWI LST 1 NDVI -0.89362 1 NDBI 0.996885 -0.90986 1 NDBaI 0.9991 -0.90136 0.997836 1 NDWI 0.9825 -0.80393 0.969206 0.980026 1 The table:1 shows a correlation analysis for the year 1990 between Land Surface Temperature (LST) and several spectral indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Bareness Index (NDBaI), and the Normalized Difference Water Index (NDWI). The correlation coefficients indicate the strength and direction of the relationships between these variables. The correlation coefficient between LST and NDVI is -0.89362, showing a strong negative relationship. This means that as NDVI rises (reflecting denser vegetation), LST generally falls, which is consistent with the expected cooling effect of vegetation on surface temperatures. On the other hand, the correlation coefficients between LST and urban-related indices (NDBI and NDBaI) are significantly high, indicating strong positive correlations. This suggests that as urbanization or built-up areas increase, LST tends to increase as well, aligning with the urban heat island effect where urban areas have higher temperatures than their surroundings. Additionally, the correlation between LST and NDWI, which indicates water presence, is moderately negative. This implies that areas with higher water content generally have lower surface temperatures, likely due to the cooling effect of water bodies. These correlation values offer important insights into the relationships between LST and various land cover types, helping to understand surface temperature dynamics and informing urban planning, environmental management, and climate change mitigation efforts.. 4.11 Association between Biophysical Parameter and LST for the Year 2000 Table 2 Correlation Matrix of Environmental Parameters for the Year 2000 2000 Year LST NDVI NDBI NDBaI NDWI LST 1 NDVI -0.97869 1 NDBI 0.952897 -0.96596 1 NDBaI 0.993283 -0.95756 0.952648 1 NDWI 0.960556 -0.88821 0.897225 0.980234 1 The table:2 showcases a correlation analysis examining the relationship between Land Surface Temperature (LST) and several spectral indices, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Difference Water Index (NDWI). The correlation coefficients highlight the extent of the linear association between these variables. Land Surface Temperature (LST) shows a strong negative correlation with the Normalized Difference Vegetation Index (NDVI) (-0.97869), indicating that areas with more vegetation cover tend to have lower temperatures. On the other hand, there are positive correlations between LST and the Normalized Difference Built-up Index (NDBI) (0.952897), Normalized Difference Bareness Index (NDBaI) (0.993283), and Normalized Difference Water Index (NDWI) (0.960556). This suggests that built-up areas, bare surfaces, and water bodies generally have higher temperatures. Additionally, the strong negative correlations between NDVI and the other indices (NDBI, NDBaI, and NDWI) indicate that densely vegetated areas typically have lower levels of built-up surfaces, bare ground, and water bodies. This aligns with ecological principles, where vegetated areas provide cooling effects through transpiration and shading. Overall, the correlation analysis highlights the complex relationships between land surface temperature and various land cover types, underscoring the importance of considering multiple spectral indices to understand the thermal behavior of different land surfaces. 4.12 Association between Biophysical Parameter and LST for the Year 2019 Table 3 Correlation Matrix of Environmental Parameters for the Year 2019 2019 Year LST NDVI NDBI NDBaI NDWI LST 1 NDVI -0.9411 1 NDBI 0.94735 -0.8239 1 NDBaI 0.991199 -0.93799 0.968742 1 NDWI 0.933233 -0.76708 0.975519 0.934193 1 Table:3 The correlation analysis of environmental parameters for 2019 reveals significant relationships. There is a strong negative correlation (-0.9411) between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), indicating that higher LST corresponds to lower NDVI, reflecting reduced vegetation density. Conversely, LST shows strong positive correlations with both Normalized Difference Built-up Index (NDBI) (0.94735) and Normalized Difference Bareness Index (NDBaI) (0.991199), suggesting higher temperatures are associated with increased built-up areas and bareness, which absorb more heat. NDVI exhibits moderate negative correlations with NDBI (-0.8239), NDBaI (-0.93799), and Normalized Difference Water Index (NDWI) (-0.76708), indicating that areas with denser vegetation tend to have fewer built-up areas, less bareness, and lower water presence. Notably, NDBI and NDBaI are strongly positively correlated (0.968742), indicating that built-up areas and bareness often coexist. Overall, these findings underscore the complex interplay among LST, vegetation density, built-up areas, bareness, and water presence, providing valuable insights into the environmental dynamics of the study area in 2019. 5. CONCULSION The coefficient of determination R² values for the years 1990, 2000, and 2019 indicate the strength of the relationship between the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) for those respective years. A higher R² value signifies a stronger correlation between the two indices, suggesting a more robust relationship between built-up areas and vegetation cover. In 1990, the R² value of 0.8278 suggests a relatively strong correlation between NDBI and NDVI, indicating that changes in built-up areas are closely associated with changes in vegetation cover. This could imply that urbanization processes and land use changes during this period significantly impacted vegetation patterns.By 2000, the relationship between NDBI and NDVI strengthened further, with an R² value of 0.9331. This suggests a highly significant correlation between built-up areas and vegetation cover, indicating that urban expansion and land development activities during this period had a profound impact on the surrounding vegetation.However, in 2019, the R² value decreased to 0.6788, indicating a weaker correlation between NDBI and NDVI compared to previous years. This could suggest a shift in land use dynamics or changes in environmental factors that influenced vegetation patterns independently of urbanization. Alternatively, it could also indicate limitations in the applicability of NDBI and NDVI indices for capturing the complexity of land cover changes in more recent years.Overall, the analysis of NDBI versus NDVI provides valuable insights into the dynamics of urbanization and vegetation cover over time. The variations in R² values across different years highlight the changing relationships between built-up areas and vegetation, reflecting the dynamic nature of land use and environmental processes. These findings are essential for urban planning, environmental management, and sustainable development initiatives, as they contribute to a better understanding of the interactions between urbanization and ecosystem dynamics. The outcomes of this study underscore the intensification of Urban Heat Island (UHI) effects, attributed to rapid urbanization between 1990 and 2019. The positive correlation observed between Land Surface Temperature (LST) and Normalized Difference Built-up Index (NDBI) further validates the impact of urbanization on temperature rise. While infrastructural enhancements have led to vegetation losses in some areas, there were also instances of vegetation improvement noted in certain regions. This indicates a complex interplay between urban development and greenery preservation. In conclusion, the proliferation of man-made constructions in urbanized regions and the reduction in vegetation cover due to infrastructural enhancements significantly contribute to temperature rise in the study region. To effectively mitigate the effects of UHIs, proactive measures should be implemented both before and during the development phases of townships. These measures could include the integration of green spaces, such as parks and urban forests, into urban planning initiatives, the implementation of green infrastructure like green roofs and permeable pavements, and the adoption of sustainable land use practices to preserve and enhance vegetation cover. By prioritizing these strategies, urban areas can mitigate the impacts of UHIs, enhance thermal comfort, and promote overall environmental sustainability. Declarations Author Contribution Pradeep Kumar Rajput: Dr. Rajput contributed to the conceptualization of the study, performed the remote sensing and GIS analysis, and played a key role in data collection, processing, and interpretation. He also led the manuscript writing, drafting the sections on Urban Heat Island effects, spatiotemporal analysis, and mitigation strategies. Additionally, he was responsible for coordinating the research work and corresponding with the journal.Durgesh Singh: He was involved in reviewing and editing the manuscript, providing critical feedback on the study's structure and scientific relevance. Dr. Singh also contributed to the literature review and discussion on urban planning and policy implications of the findings Data availability All data generated or analyzed during this research are included in the presented Tables and Figures in this manuscript. References Carlson, T. N. (1986). Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sensing Reviews, 197–247. Elhadi K. Mustafa, Y. C. (2020). Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms. Advances in Civil Engineering . Mehdi Bokaie, M. K. (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, 94–104. ozelkhan, E. (2013). Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data. European Journal of remote sensing . Sumit Khandelwal, R. G. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 87–94. Chen, D., Zhang, F., Zhang, M., Meng, Q., Jim, C.Y., Shi, J., Tan, M.L. and Ma, X., 2022. Landscape and vegetation traits of urban green space can predict local surface temperature. Science of The Total Environment , 825 , p.154006. Engel, R.A., Millard-Ball, A. and Turner, V.K., 2023. Contributions of roads to surface temperature: evidence from Southern California. Environmental Research Communications , 5 (1), p.015004. Santamouris, M. (2015). Urban Climate Mitigation Techniques. Routledge. Heidarinejad, G., & Kleerekoper, L. (2017). Urban heat island mitigation strategies: A state-of-the-art review on Kuala Lumpur, Singapore and Hong Kong. Journal of Building Engineering, 11, 1–13. Harlan, S. L., Brazel, A. J., Prashad, L., Stefanov, W. L., & Larsen, L. (2006). Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine, 63(11), 2847–2863. Newman, P., & Thornley, A. (2017). Urban planning in Europe: International competition, national systems, and planning projects. Routledge. Carmona, M., Tiesdell, S., Heath, T., & Oc, T. (2010). Public places, urban spaces: The dimensions of urban design. Routledge. Cervero, R., & Murakami, J. (2010). Rail and property development in Hong Kong: Experiences and extensions. Transportation Research Record, 1743(1), 89–97. Ahern, J. (2013). Urban landscape sustainability and resilience: The promise and challenges of integrating ecology with urban planning and design. Landscape Ecology, 28(6), 1203–1212. Hajer, M. A. (2016). Smart urban governance: The case of Amsterdam. In Smart Urbanism (pp. 29–40). Routledge. Florida, R. (2017). The new urban crisis: How our cities are increasing inequality, deepening segregation, and failing the middle class-and what we can do about it. Basic Books Anguelovski, I., Connolly, J. J. T., Garcia-Lamarca, M., Cole, H., Pearsall, H., & Newell, J. P. (2016). Urban planning, poverty and inequality: resolving the paradox in the global south. Routledge. Chen, L., Ng, E., An, X., Ren, C., & Wang, Z. (2020). Evaluating the cooling effects of urban greenery in relation to the spatial configurations of street canyons. Building and Environment, 167, 106437. Oke, T. R. (2017). The heat island of the urban boundary layer: characteristics, causes and effects. In Remote Sensing of Urban Heat Islands (pp. 3–19). CRC Press. Pomerantz, M., Rosado, P., Levinson, R., & Akbari, H. (2019). Effects of heat islands and heat waves on people living in urban and rural areas. Journal of Extreme Events, 6(02), 1950006. Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. Rahman, M. M., Shi, Z., Zhang, Y., & Zhu, X. (2019). Impact of urban greenery on land surface temperature using remote sensing: A case study in Dhaka, Bangladesh. Remote Sensing, 11(12), 1486. Li, Z., Strahler, A. H., & Jupp, D. L. (2017). Retrieval of leaf area index from high resolution satellite images over forest areas. Remote Sensing of Environment, 190, 279–290. Gorgani, S.A., Panahi, M. and Rezaie, F., 2013, November. The Relationship between NDVI and LST in the urban area of Mashhad, Iran. In International conference on civil engineering architecture & urban sustainable development (Vol. 2013). Fatemi, M. and Narangifard, M., 2019. Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City. Arabian Journal of Geosciences , 12 (4), p.127. Yue, W., Xu, J., Tan, W. and Xu, L., 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM + data. International journal of remote sensing, 28 (15), pp.3205–3226. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5309216","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376434526,"identity":"73d7b23a-5622-498b-9013-99d3439c81c7","order_by":0,"name":"Durgesh Singh","email":"","orcid":"","institution":"Chhatrapati Shahu Ji Maharaj University","correspondingAuthor":false,"prefix":"","firstName":"Durgesh","middleName":"","lastName":"Singh","suffix":""},{"id":376434527,"identity":"f614616b-308f-4cbd-a0f6-58199c52ff75","order_by":1,"name":"Pradeep Kumar Rajput","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACHijNDyISCkjRItkA0mJAihaDA2CSCB3mPGcff/jYZpNnfH514ocHBgzy/GIH8Gux7G03k5zZllZsduPtZgmgwwxnzk7Ar8XgPBsbM8+Zw4nbbpzdANKSYHCbsBbmz3/O/E/cPOPs5h/EaTnbxiDNUHEgcQN/7zbibLHsOcYm2VORnDjjBu82iwQDCcJ+MedJY/7ww8Ausb//7OabPyps5PmlCTkMzpIAq5TArxxVC/8BwqpHwSgYBaNgZAIA/9dEv7wCFjcAAAAASUVORK5CYII=","orcid":"","institution":"Chhatrapati Shahu Ji Maharaj University","correspondingAuthor":true,"prefix":"","firstName":"Pradeep","middleName":"Kumar","lastName":"Rajput","suffix":""}],"badges":[],"createdAt":"2024-10-22 07:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5309216/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5309216/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69053034,"identity":"582c8f57-d588-4bf9-b676-8eb8c36d4327","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113270,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area location map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/8f4eb367b141d32fbd59413f.png"},{"id":69053680,"identity":"e51834a0-ff5b-4c89-88ed-e097c2940f3f","added_by":"auto","created_at":"2024-11-15 05:39:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76123,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology Flow chart\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/d9d152db3204c7d4588fb30e.png"},{"id":69054413,"identity":"cb5c0a0c-f522-47cb-ac04-833985514092","added_by":"auto","created_at":"2024-11-15 06:03:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384410,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 1 The analysis examines land surface temperature variations over the city of Ahmedabad from the period spanning 1990 to 2019\u003c/p\u003e","description":"","filename":"1a.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/086f3386d54ac3e53b3ce16d.png"},{"id":69053037,"identity":"98950d8a-195c-461e-8527-9a6d7c8dab16","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":349233,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 2 The Normalized Difference Vegetation Index (NDVI) for Ahmedabad city was analyzed over the period from 1990 to 2019.\u003c/p\u003e","description":"","filename":"2a.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/fa7f4d6272dea9d146997d74.png"},{"id":69054414,"identity":"ebee34bc-d891-467e-a605-7d7e33592de5","added_by":"auto","created_at":"2024-11-15 06:03:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":556923,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 3 The Normalized Difference Water Index (NDWI) was calculated for the city of Ahmedabad spanning the period from 1990 to 2019.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/f3bef1714ad894710965afda.png"},{"id":69053046,"identity":"998e79ce-7bbe-43ee-ac8a-788f3e628dc3","added_by":"auto","created_at":"2024-11-15 05:31:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":491208,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 4 The variation in Normalized Difference Built-up and Bareness Index (NDBaL) was calculated for the city of Ahmedabad spanning the period from 1990 to 2019.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/95f2047305010463d6fd467c.png"},{"id":69053683,"identity":"de420904-385a-4365-bfbb-3f7eae769191","added_by":"auto","created_at":"2024-11-15 05:39:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":500194,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 5 The analysis focuses on the Normalized Difference Built-up Index (NDBI) over the city of Ahmedabad spanning the period from 1990 to 2019.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/74bc5bd04c3d3425661ee17e.png"},{"id":69053045,"identity":"4013051e-6ed3-4c93-885a-bef54393e64d","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53332,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 6 LST and NDVI correlation charts for 1990, 2000, and 2019\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/41a6aafadc1f002677afb246.png"},{"id":69053043,"identity":"1d24d5c0-abfb-4fcc-bdce-80913bdb44fa","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":45902,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 7 LST and NDWI correlation charts for 1990, 2000, and 2019\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/b41abcc830ca01c6ce988fe6.png"},{"id":69053682,"identity":"73002626-49ab-492f-b6b4-2ea47b40fa8f","added_by":"auto","created_at":"2024-11-15 05:39:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":49807,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 8 LST and NDBI correlation charts for 1990, 2000, and 2019\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/112c39231752f6106766096c.png"},{"id":69053035,"identity":"ab28f9c8-3f74-4c02-9997-d738e25f748d","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":50405,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: \u0026nbsp;9 LST and NDBaL correlation charts for 1990, 2000, and 2019\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/4f51c49f75f3ce4e40d08f5e.png"},{"id":69053685,"identity":"81ae6de4-b5c3-4db1-b54a-ff001158ad38","added_by":"auto","created_at":"2024-11-15 05:39:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":339627,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 10 Grid Map of Correlation Between LST, NDVI, NDBI, NDBaL, and NDWI for the Year 1990\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/db88ca2677f43bbcca4a6d89.png"},{"id":69053040,"identity":"3d075c0d-e3c2-40d0-bd1f-fe8eaff0fa7f","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":316174,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 11 Grid Map of Correlation Between LST, NDVI, NDBI, NDBaL, and NDWI for the Year 2000\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/cd314483f1163c120794e8ef.png"},{"id":69053044,"identity":"5751b43a-3a63-404b-816d-fcbb3c88662f","added_by":"auto","created_at":"2024-11-15 05:31:53","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":360998,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: 12 Grid Map of Correlation Between LST, NDVI, NDBI, NDBaL, and NDWI for the Year 2019\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/89a25527ad9809400f11b259.png"},{"id":84250694,"identity":"808da783-e103-48b2-8e18-49c7aace5c89","added_by":"auto","created_at":"2025-06-09 18:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4014814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5309216/v1/047379ad-5240-49d0-bdd7-03d28e7a641d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Urban Heat Island Effects and Mitigation Strategies in Ahmedabad, India","fulltext":[{"header":" Highlights","content":"\u003cp\u003eThe study investigates Urban Heat Island (UHI) effects in Ahmedabad, India, focusing on temperature disparities between urban and rural areas. Key findings reveal that rapid urbanization and loss of vegetation contribute significantly to increased temperatures in the city. The research highlights the effectiveness of mitigation strategies such as enhancing urban green spaces, implementing cool roofs, and promoting sustainable urban planning. Remote sensing and GIS techniques are employed to analyze land use changes and their impact on UHI. The study also emphasizes the role of community awareness and policy interventions in mitigating UHI effects, ultimately aiming to improve urban resilience and reduce energy consumption in Ahmedabad. The findings provide valuable insights for sustainable urban development in similar climates.\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eAn urban heat island (UHI) is an urban or metropolitan area that experiences significantly higher temperatures than its surrounding rural areas due to human activities. The temperature difference is usually more pronounced at night than during the day and is most noticeable when winds are weak. UHI effects are most significant during summer and winter. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The alteration and use of land surfaces is the primary cause of the urban heat island effect; waste heat from energy use is a secondary factor. As urban populations grow, the area of UHIs expands and their average temperature increases. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Essentially, a heat island refers to any area, whether populated or not, that is consistently hotter than its surroundings. According to a United Nations report, rapid urbanization and globalization have increased the world's urban population from around 33% fifty years ago to over 54% today, with projections of up to 66% by 2050. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] The report also suggests that Asian and African countries will experience faster rates of urbanization compared to other continents. While urbanization brings prosperity and development, it also negatively impacts the global ecological environment. Among the environmental effects of rapid urbanization, the urban thermal environment, exemplified by the UHI phenomenon, has become a significant urban environmental issue. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Research in the United States shows a relationship between extreme temperatures and mortality, with heat posing a greater risk in northern cities than in southern regions. Concerns have been raised about the potential contribution of UHIs to global warming. Studies from China and India indicate that the UHI effect contributes to climate warming by about 30%. However, a 1999 study comparing urban and rural areas found that UHI effects have little impact on global mean temperature trends. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Many studies indicate that the severity of UHIs increases with the progression of climate change. Research has shown a relationship between UHIs and patterns of land cover changes, with vegetation and water presence reducing UHI intensity, while increased urbanization intensifies it. Various indices such as the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) have been used to represent land cover changes over analyzed time periods. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator used to analyze remote sensing measurements, typically from space platforms, to assess the presence of live green vegetation in a target location. To improve upon NDVI, Huete developed the Soil-Adjusted Vegetation Index (SAVI), which accounts for the differential extinction of red and near-infrared light through vegetation canopies. SAVI minimizes soil brightness influences in spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The Normalized Difference Water Index (NDWI) is used to monitor changes related to water content in water bodies and to assess whether a target location is experiencing floods or scarcity. The Difference Normalized Urban areas tend to show higher reflectance in the shortwave-infrared (SWIR) region than in the near-infrared (NIR) region; this is why the Built-up Index (NDBI) emphasizes these areas. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The surface and atmosphere's thermal, radiative, moisture, and aerodynamic qualities change as green spaces are replaced by structures and roadways. Urban construction materials have different thermal and radiative properties compared to rural or vegetated areas, resulting in greater absorption and storage of the sun\u0026rsquo;s energy in urban surfaces. Additionally, the height and arrangement of buildings affect the rate at which the absorbed energy escapes at night. As a result, metropolitan areas continue to have relatively higher nighttime air temperatures because they cool more slowly than rural locations. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator used to analyze remote sensing measurements, typically from space platforms, to assess vegetation health. Additional factors contributing to the Urban Heat Island (UHI) effect include scattered and emitted radiation from atmospheric pollutants in urban areas, waste heat production from air conditioning and refrigeration systems, industrial processes, motorized vehicular traffic (i.e., anthropogenic heat), and the obstruction of rural air flows by the windward faces of built-up surfaces. The impacts of urbanization, such as pollution production, waste heat from human activities (notably from air conditioners and internal combustion engines), modifications to the physical and chemical properties of the atmosphere, and soil surface covering, have become more apparent, leading to the UHI phenomenon.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] There is a direct relationship between UHI intensity peaks and heat-related illnesses and fatalities, as thermal discomfort affects the human cardiovascular and respiratory systems.\u003c/p\u003e"},{"header":"2. STUDY AREA","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAhmedabad, located in the vibrant state of Gujarat, India, is a city rich in historical, cultural, and economic significance. With a heritage that stretches back centuries, it has grown into a bustling metropolis while maintaining its traditional charm. The city's foundation by Sultan Ahmed Shah in the 15th century marked the beginning of its prominence, and it became an important center of trade and commerce during the Mughal era. Ahmedabad also played a pivotal role in India's independence movement, with the Sabarmati Ashram serving as a key site in Mahatma Gandhi's efforts. Today, Ahmedabad stands as Gujarat's economic powerhouse, with industries such as textiles, pharmaceuticals, and information technology driving its economy. The city's entrepreneurial spirit has attracted significant investments, both domestic and international, while its thriving informal sector provides essential livelihoods to a large portion of the population. Ahmedabad's rapid urbanization has led to considerable infrastructural development, including notable projects like the Sabarmati Riverfront and the Bus Rapid Transit System (BRTS), improving public spaces and transportation connectivity. However, urban sprawl and inadequate planning raise concerns about sustainability and equitable growth. The city's cultural heritage, recognized by its UNESCO World Heritage City designation in 2017, includes remarkable architectural landmarks like the Jama Masjid and Sidi Saiyyed Mosque, alongside traditional pols (housing clusters). Festivals such as Uttarayan and Navratri reflect Ahmedabad's vibrant cultural fabric, attracting tourists worldwide. Despite its many advancements, the city faces environmental challenges, particularly air and water pollution due to industrial activity and population growth. Climate change further aggravates these issues, highlighting the need for climate adaptation and mitigation strategies. Ahmedabad exemplifies the fusion of tradition and modernity, where its rich historical past coexists with contemporary aspirations. As the city navigates the complexities of urbanization and globalization, efforts towards sustainable development, inclusive growth, and the preservation of its unique identity are crucial for its future progress.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. MATERIALS AND METHODS","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Used\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data description covers imagery captured by three generations of Landsat satellites data such as Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8. Each satellite features advanced sensors capable of capturing multispectral imagery across various wavelengths, enabling comprehensive earth observation. Landsat 5 TM, launched in 1984 and operational until 2013, provided seven spectral bands with spatial resolutions ranging from 15 to 120 meters. Landsat 7 ETM+, launched in 1999, introduced enhancements such as a new panchromatic band and improved thermal sensitivity. Although an issue with its scan line corrector caused systematic gaps in the imagery, Landsat 7 continued to supply valuable data until the launch of Landsat 8. Landsat 8, launched in 2013, offers significant advancements, including improved radiometric and spatial resolution compared to its predecessors. Equipped with the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 8 provides a total of eleven spectral bands, including two thermal bands, allowing for more detailed and accurate observation of the Earth's surface. These datasets from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 are invaluable resources for various applications, including land cover mapping, environmental monitoring, urban planning, agricultural management, and scientific research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Estimation of parameters\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFigure:2 show the outline extraction of parameter use satellite data. here use of thermal infrared bands from different Landsat image types, including band 6 of Landsat 5 TM, Landsat 7 ETM+, and band 10 of Landsat 8, were leveraged for Land Surface Temperature (LST) estimation in the inter-municipal grouping of Guelma. Due to striping issues, only band 10 of Landsat 8 was utilized from the Landsat OLI-TIRS thermal bands. A single window algorithm, as proposed by Xiaolei et al. (2014) and based on NDVI, was employed to derive Land Surface Emissivity (LSE). The following steps outline the process utilized to retrieve LST from thermal and NDVI images\u003c/span\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Radiance image calculation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRadiance is a measure of the energy emitted by a surface or object in a given direction per unit area and solid angle. It is commonly used in remote sensing to quantify the amount of electromagnetic radiation (light) detected by a sensor. Calculating radiance from digital numbers (DN) in remote sensing imagery requires specific calibration parameters. Here's a general process to calculate radiance from satellite image data. Eq.\u0026nbsp;(1)(2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{L}}_{\\varvec{\\lambda\\:}=\\:\\:\\:}\\frac{({\\varvec{L}\\varvec{M}\\varvec{A}\\varvec{X}}_{\\varvec{\\lambda\\:}}-{\\varvec{L}\\varvec{M}\\varvec{I}\\varvec{N}}_{\\varvec{\\lambda\\:}})}{({\\varvec{Q}\\varvec{C}\\varvec{A}\\varvec{L}}_{\\varvec{m}\\varvec{a}\\varvec{x}}-{\\varvec{Q}\\varvec{C}\\varvec{A}\\varvec{L}}_{\\varvec{m}\\varvec{i}\\varvec{n}})}\\mathbf{*}\\left(\\varvec{Q}\\varvec{C}\\varvec{A}\\varvec{L}-{\\varvec{Q}\\varvec{C}\\varvec{A}\\varvec{L}}_{\\varvec{m}\\varvec{i}\\varvec{n}}\\right)+{\\varvec{L}\\varvec{M}\\varvec{I}\\varvec{N}}_{\\varvec{\\lambda\\:}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(1\\right)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{L}}_{\\varvec{\\lambda\\:}}=\\:{\\varvec{M}}_{\\varvec{L}}\\mathbf{*}\\:{\\varvec{Q}}_{\\varvec{C}\\varvec{A}\\varvec{L}}+{\\varvec{A}}_{\\varvec{L}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(2\\right)}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Radiance temperature calculation\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn remote sensing research, radiance temperature is critical for monitoring surface energy balances, climate studies, and land surface temperature (LST) estimations. For example, in a study by Li et al. (2013), researchers used thermal infrared satellite data to estimate surface temperatures across urban areas, providing insights into the urban heat island effect. Similarly, Kalma et al. (2008) focused on how radiance temperature can be employed to measure evapotranspiration in agricultural fields, improving water resource management. In many research papers, radiance temperature is calculated by correcting for atmospheric effects and sensor-specific factors. Weng et al. (2004) explored the relationship between radiance temperature, land surface temperature, and emissivity, showing how corrections for atmospheric interference can improve the accuracy of temperature measurements in satellite imagery. These methods help derive accurate surface temperature data from thermal sensors such as those on Landsat or MODIS satellites. Eq.\u0026nbsp;(3)\u003c/span\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{T}=\\:\\frac{\\varvec{k}2}{\\begin{array}{c}In\\left(\\varvec{K}1+1\\right)\\:\\:\\:\\:\\\\\\:L\\lambda\\:\\end{array}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(3\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Emissivity calculation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThere are several methods to calculate emissivity, ranging from laboratory-based measurements to satellite-based retrievals. Laboratory measurements typically involve spectrometers that measure emitted radiation at different wavelengths, which are then compared to a black body reference. However, for large-scale environmental studies, satellite-based methods are more practical. Zhang et al. (2009) proposed using thermal infrared satellite data to calculate surface emissivity, where they employed the Temperature Emissivity Separation (TES) algorithm. This method involves using multiple spectral bands to estimate both temperature and emissivity simultaneously, making it possible to derive these parameters from remote sensing data in various environmental conditions. Eq.\u0026nbsp;(4) (5)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equd\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{p}}_{\\varvec{v}}=(\\:\\:\\frac{\\varvec{N}\\varvec{V}\\varvec{D}\\varvec{I}-{\\varvec{N}\\varvec{V}\\varvec{D}\\varvec{I}}_{\\varvec{m}\\varvec{i}\\varvec{n}}}{{\\varvec{N}\\varvec{D}\\varvec{V}\\varvec{I}}_{\\varvec{m}\\varvec{a}\\varvec{x}}-{\\varvec{N}\\varvec{D}\\varvec{V}\\varvec{I}}_{\\varvec{m}\\varvec{i}\\varvec{n}}}{)}^{2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.(4)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Eque\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{\\epsilon\\:}=0.004{\\varvec{p}}_{\\varvec{\\nu\\:}+0.986}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(5\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLST calculation\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLST is widely used to study the Urban Heat Island effect, where urban areas tend to be significantly warmer than their rural surroundings due to human activities and infrastructure. Voogt and Oke (2003) provided one of the foundational frameworks for analyzing UHI using satellite-derived LST. Recent studies like Li et al. (2021) and Peng et al. (2022) have further explored how LST data can inform urban planning and green infrastructure development to mitigate UHI effects. Eq.\u0026nbsp;(6)\u003c/span\u003e \u003cdiv id=\"Equf\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{L}\\varvec{S}\\varvec{T}=\\frac{{\\varvec{T}}_{\\varvec{\\kappa\\:}}}{1+\\left(\\frac{\\varvec{\\lambda\\:}{\\varvec{T}}_{\\varvec{\\kappa\\:}}}{\\varvec{p}}\\right)\\varvec{I}\\varvec{n}\\:\\varvec{\\epsilon\\:}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(6\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Biophysical indices\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBiophysical indices related to radiation play an essential role in understanding the energy balance of ecosystems. Indices like the Photochemical Reflectance Index (PRI) are used to assess photosynthetic efficiency and light use efficiency in vegetation. PRI is sensitive to changes in the xanthophyll cycle pigments, which are directly related to plant stress responses to excess light or water limitations (Gamon et al., 1997). This index is useful in studying how ecosystems respond to environmental stresses such as drought, heat waves, or nutrient deficiencies, making it a powerful tool for assessing the impact of climate change on plant productivity. Eq.\u0026nbsp;(7), (8), (9), (10)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equg\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{N}\\varvec{V}\\varvec{D}\\varvec{I}=\\frac{\\left(\\varvec{N}\\varvec{I}\\varvec{R}-\\varvec{R}\\varvec{E}\\varvec{D}\\right)}{\\left(\\varvec{N}\\varvec{I}\\varvec{R}+\\varvec{R}\\varvec{E}\\varvec{D}\\right)\\:\\:\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(7\\right)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equh\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{N}\\varvec{D}\\varvec{B}\\varvec{I}=\\:\\frac{(\\varvec{M}\\varvec{I}\\varvec{R}-\\varvec{N}\\varvec{I}\\varvec{R})}{(\\varvec{M}\\varvec{I}\\varvec{R}+\\varvec{N}\\varvec{I}\\varvec{R})}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{e}\\varvec{q}.\\left(8\\right)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equi\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:NDBal=\\:\\frac{SWIR-TIRS1}{SWIR+TIRS1\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:eq.\\left(9\\right)$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equj\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:NDWI=\\frac{\\left(GREEN-NIR\\right)}{\\:\\left(GREEN+NIR\\right)\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:eq.\\left(10\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Consequence of Urban Heat Island\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe urban heat island (UHI) phenomenon describes the tendency for urban areas to experience higher temperatures than surrounding rural regions due to human activities and continuous urbanization. A primary factor in the formation and intensification of UHI is the replacement of natural land covers, such as vegetation and water bodies, with impervious surfaces like asphalt and concrete [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These surfaces absorb and retain heat, leading to increased surface temperatures. Additionally, the dense concentration of buildings and infrastructure in urban areas reduces vegetation cover, which in turn decreases evapotranspiration and cooling effects. Other contributors to UHI include heat emissions from vehicles, industries, and air conditioning systems, as well as changes in local wind patterns and atmospheric circulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The impacts of UHI are significant for the environment, society, and public health related problems occurs. Higher temperatures in urban areas can increase the incidence of heat-related illnesses and mortality, particularly during heatwaves, posing greater risks to vulnerable populations such as the elderly, children, and individuals with pre-existing health conditions. UHI effects can also disrupt ecosystems, alter biodiversity patterns, and negatively impact agricultural productivity. Additionally, UHI can worsen air pollution levels by promoting the formation of ground-level ozone and other pollutants, which can have harmful effects on respiratory health and exacerbate conditions like asthma to human. study the causes and consequences of UHI requires comprehensive future urban planning and mitigation strategies. These may include increasing green spaces and vegetation cover through urban forestry initiatives, promoting sustainable building design and materials to reduce heat absorption and improve energy efficiency, implementing cool roof and cool pavement technologies to mitigate surface heat retention, and integrating green infrastructure such as green roofs and permeable pavements to enhance urban cooling and storm water management. Additionally, community-based adaptation measures such as public awareness campaigns, heat emergency response plans, and the provision of cooling centers can help mitigate the health impacts of UHI on vulnerable populations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 City Planning and Development\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCity planning and development is a multifaceted process that involves shaping the physical, social, economic, and environmental aspects of urban areas. This process includes the formulation, implementation, and evaluation of strategies and policies aimed at guiding urban growth and transformation in a sustainable and equitable manner. Effective city planning and development are crucial for creating livable, resilient, and inclusive urban environments that cater to the needs and aspirations of both current and future generations needs planning. City planning and development encompass various components such as land use planning, infrastructure development, transportation systems, housing provision, environmental management, economic development, and social equity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These components are interconnected and interdependent, requiring integrated and holistic approaches. Land use planning is a fundamental aspect of city planning, involving the allocation and regulation of land for residential, commercial, industrial, recreational, and green spaces [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It aims to optimize land utilization, promote compact and mixed-use development, prevent urban sprawl, and protect valuable natural and cultural assets. To reduce the effects of heat island, land use planning fosters sustainable development, promotes social inclusion, and enhances the quality of life for urban residents. Infrastructure development, another key component, includes the provision of essential services and facilities such as water supply, sanitation, energy, transportation, telecommunications, and waste management. Investments in infrastructure are vital for supporting urban growth, enhancing connectivity, improving accessibility, and ensuring the well-being of urban populations. Sustainable and resilient infrastructure is essential for mitigating climate change impacts, reducing vulnerability to natural hazards, and promoting environmental sustainability [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hence, city planning and development play a crucial role in shaping the future of urban areas, influencing the quality of life, sustainability, and resilience of cities. Effective city planning and development require integrated and participatory approaches that involve collaboration between government agencies, private sector stakeholders, civil society organizations, and local communities. By adopting sustainable, inclusive, and equitable practices, cities can create vibrant, resilient, and livable urban environments that promote the well-being and prosperity of all residents.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Mitigation Planning\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMitigation planning for land surface temperature (LST) involves strategic interventions aimed at reducing the impacts of urban heat islands (UHIs), heatwaves, and climate change on human health, ecosystems, and urban infrastructure. LST, a key indicator of land surface thermal properties, is influenced by factors such as land cover, land use, vegetation, building materials, and urban morphology. As urbanization intensifies globally, mitigating the adverse effects of rising LST has become crucial in urban planning and climate resilience strategies. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Urban areas typically experience higher LST than surrounding rural areas due to the urban heat island effect, characterized by the absorption and retention of solar radiation by built-up surfaces, reduced vegetation cover, and anthropogenic heat emissions. Elevated LST contributes to various environmental and societal challenges, including increased energy consumption, heat-related illnesses, reduced air quality, and biodiversity loss. Therefore, effective mitigation planning strategies are essential to alleviate these impacts and create healthier, more sustainable urban environments.\u003c/p\u003e \u003cp\u003eOne effective approach to mitigating the impacts of elevated Land Surface Temperature (LST) is through urban greening initiatives that increase vegetation covers and enhance natural cooling mechanisms. Green spaces such as parks, urban forests, and green roofs help mitigate Urban Heat Island (UHI) effects by providing shade, evaporative cooling, and reducing surface temperatures through transpiration. Research indicates that the strategic placement of green infrastructure can significantly lower LST and improve thermal comfort in urban areas. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Additionally, cool roofing and cool pavement technologies are emerging as effective strategies to reduce LST. These materials, characterized by higher solar reflectance and lower thermal conductivity compared to traditional surfaces, reduce the absorption of solar radiation and lower surface temperatures. Implementing these technologies at a city scale can lead to substantial reductions in LST, energy consumption, and greenhouse gas emissions. Moreover, land use planning and urban design strategies are crucial in mitigating LST by promoting compact, mixed-use development patterns, preserving green spaces, and incorporating passive cooling measures into building design. Techniques such as increased building setbacks, optimizing street orientation, and using high-albedo materials can minimize heat retention and enhance natural ventilation in urban environments. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn conclusion, mitigation planning in the context of LST is essential for addressing the challenges posed by urban heat islands, heatwaves, and climate change in cities worldwide. By implementing a combination of nature-based solutions, cool technologies, urban design strategies, community engagement initiatives, and advanced technologies, cities can effectively reduce LST, enhance urban livability, and build climate-resilient communities. However, successful mitigation planning requires holistic, interdisciplinary approaches that integrate scientific knowledge, stakeholder perspectives, and policy frameworks to create sustainable, equitable, and resilient urban environments for current and future generations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Vegetation Structure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe relationship between vegetation structure and land surface temperature (LST) is a crucial aspect of understanding the dynamics of terrestrial ecosystems and their response to environmental changes. Vegetation structure encompasses various physical characteristics of plant communities, including canopy cover, leaf area index (LAI), canopy height, and biomass distribution [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These structural attributes influence the energy balance of the land surface, thereby affecting LST patterns at local, regional, and global scales. Canopy cover, or the proportion of ground covered by vegetation canopy, plays a significant role in modulating LST by influencing the amount of incoming solar radiation absorbed or reflected by the land surface. Dense vegetation canopies intercept a larger fraction of solar radiation, leading to lower surface temperatures through shading and evapotranspiration processes. In contrast, sparse vegetation cover allows more solar radiation to reach the land surface, resulting in higher LST due to increased absorption of solar energy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Leaf area index (LAI) is another important vegetation structural parameter that influences LST dynamics. LAI is the total leaf area per unit ground area and is strongly correlated with canopy density and photosynthetic activity. High LAI values indicate dense vegetation canopies with abundant leaf cover, which can reduce LST by enhancing evaporative cooling through transpiration. Conversely, low LAI values are associated with sparse vegetation cover and higher LST due to reduced shading and transpiration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Canopy height, or the vertical extent of vegetation, also influences LST by affecting the exchange of energy and moisture between the land surface and the atmosphere. Tall vegetation canopies intercept more solar radiation and promote greater turbulence within the boundary layer, leading to enhanced cooling through convective heat transfer and increased evapotranspiration rates. Shorter vegetation, on the other hand, may have limited cooling effects and can contribute to higher LST by allowing more solar radiation to reach the land surface. Biomass distribution within vegetation canopies can further modulate LST patterns by altering the partitioning of energy and water fluxes within the ecosystem [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Biomass distribution affects the distribution of surface roughness elements, which in turn influences aerodynamic resistance and heat exchange processes between the land surface and the atmosphere [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, biomass distribution impacts the spatial variability of evapotranspiration rates and surface albedo, both of which play critical roles in regulating LST. Several studies have investigated the relationship between vegetation structure and LST across different ecosystems and land cover types. examined the impact of canopy cover and LAI on LST in urban areas using remote sensing data. In conclusion, vegetation structure plays a critical role in shaping land surface temperature patterns through its influence on energy balance processes and surface-atmosphere interactions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Understanding the complex interactions between vegetation structure and LST is essential for ecosystem monitoring, climate modeling, and land management practices. Incorporating vegetation structural parameters into LST modeling frameworks can improve our ability to predict and mitigate the impacts of environmental changes on terrestrial ecosystems and human communities. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Spatial Distribution of LST NDVI NDWI NDBaL and NDBI\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Association between LST and NDVI\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe figure:6 show a linear regression model where the coefficient of determination in 1990 R\u0026sup2; is 0.7986. This indicates that approximately 79.86% of the variability in NDVI can be explained by its linear relationship with LST. The regression equation suggests a negative relationship between LST and NDVI, meaning that higher LST values are associated with lower NDVI values. This inverse relationship is commonly observed in vegetation studies, as high temperatures can stress vegetation, resulting in decreased photosynthetic activity and, consequently, lower NDVI values. The coefficient of determination in 2000 R\u0026sup2; indicates the strength of the linear relationship between LST and NDVI. In this case, an R\u0026sup2; value of 0.7986 suggests a relatively strong correlation between the two variables, indicating that the model provides a good fit to the observed data points. However, it is important to consider other factors that may influence NDVI variability, as the model might not capture all sources of variation. The R\u003csup\u003e2\u003c/sup\u003e value of 2019 is 0.9578 suggests that about 95.78% of the variability in NDVI, the dependent variable, can be attributed to changes in LST, the independent variable, in this linear regression model. This high R\u003csup\u003e2\u003c/sup\u003e indicates a robust fit of the model to the data, demonstrating its ability to accurately predict NDVI based on LST. This underscores the significant and strong relationship observed between NDVI and LST in the year 2000, emphasizing temperature's crucial role in influencing vegetation health and dynamics.\u003c/p\u003e \u003cp\u003eAn R\u0026sup2; value of 0.8857 indicates that approximately 88.57% of the variation in land surface temperature (LST) can be explained by normalized difference vegetation index (NDVI) using the linear regression model. This strong R\u0026sup2; suggests a robust relationship between NDVI and LST for the year 2019, highlighting how vegetation density influences surface temperature dynamics. This insight is valuable for applications such as environmental monitoring, ecosystem analysis, and agricultural management, offering significant understanding into land surface processes based on vegetation indices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Association between LST and NDWI\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe provided correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e values) between Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST) for the years 1990, 2000, and 2019 indicate the strength and direction of their relationship. In 1990, the R\u003csup\u003e2\u003c/sup\u003e value of 0.9653 suggests a very strong positive correlation between NDWI and LST. This indicates that as the water presence, as indicated by NDWI, increases, the land surface temperature tends to increase as well. This could be attributed to the fact that water bodies, being good absorbers of solar radiation, can contribute to higher temperatures in their vicinity. In 2000, the R\u003csup\u003e2\u003c/sup\u003e value of 0.9227 indicates a strong positive correlation between NDWI and LST, similar to the trend observed in 1990. Again, this suggests that areas with higher water presence tend to have higher land surface temperatures. In 2019, the R\u003csup\u003e2\u003c/sup\u003e value of 0.8709 suggests a slightly weaker but still significant positive correlation between NDWI and LST compared to the previous years. However, the correlation remains strong, indicating that the relationship between water presence and land surface temperature persists over time. Overall, these findings underscore the consistent positive relationship between NDWI and LST over the examined years, highlighting the influence of water bodies on land surface temperatures in the respective years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Association Between LST and NDBI\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis research explores the relationship between the Normalized Difference Built-up Index (NDBI) and land surface temperature (LST) in Ahmedabad city, revealing a strong positive correlation between these variables. The coefficient of determination (R\u0026sup2;) for the relationship between NDBI and LST is presented for the years 1990, 2000, and 2019. For 2019, the R\u0026sup2; value is 0.8975, indicating a strong positive correlation. This high R\u0026sup2; value, close to 1, implies that 89.75% of the variation in LST can be attributed to the variation in NDBI for that year.\u003c/p\u003e \u003cp\u003eThe Normalized Difference Built-up Index (NDBI) is used to measure the extent of built-up areas in satellite images, while Land Surface Temperature (LST) indicates the temperature of the Earth's surface as detected by satellite sensors. A strong positive correlation between NDBI and LST implies that regions with higher concentrations of built-up areas typically exhibit higher surface temperatures. This is often due to the heat-retaining characteristics of construction materials like concrete and asphalt, which result in elevated surface temperatures compared to areas covered by natural vegetation or bare soil.\u003c/p\u003e \u003cp\u003eUnderstanding the relationship between NDBI and LST is valuable for various applications, including urban heat island monitoring, land use planning, and climate change studies. By analyzing the correlation between these two variables, researchers can gain insights into the spatial distribution of urbanization and its impacts on local climate conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Association between NDBaL and LST\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe coefficient of determination (R\u0026sup2;) values obtained for the relationship between the Normalized Difference Built-up Index (NDBaI) and Land Surface Temperature (LST) across different years demonstrate a strong correlation between these variables. In 1990, the R\u0026sup2; value of 0.9982 indicates that approximately 99.82% of the variability in LST is explained by variations in NDBaI. Similarly, in 2000 and 2019, the R\u0026sup2; values of 0.9866 and 0.9825 respectively show that around 98.66% and 98.25% of the variability in LST can be attributed to changes in NDBaI.\u003c/p\u003e \u003cp\u003eThe high R\u0026sup2; values demonstrate a strong relationship between NDBaI and LST over the three years examined. NDBaI, a metric derived from satellite imagery that indicates the presence and density of built-up areas, proves to be a reliable predictor of land surface temperature. This strong correlation implies that urbanization and the density of built-up areas significantly impact land surface temperature patterns.\u003c/p\u003e \u003cp\u003eThe high R\u0026sup2; values demonstrate a strong relationship between NDBaI and LST over the three years examined. NDBaI, a metric derived from satellite imagery that indicates the presence and density of built-up areas, proves to be a reliable predictor of land surface temperature. This strong correlation implies that urbanization and the density of built-up areas significantly impact land surface temperature patterns.\u003c/p\u003e \u003cp\u003eIn conclusion, the analysis underscores the strong relationship between NDBaI and LST across multiple years, as evidenced by the consistently high R\u0026sup2; values. This highlights the importance of considering built-up density as a key factor influencing land surface temperature patterns, and it emphasizes the potential of NDBaI as a valuable tool for assessing urban heat island effects and informing urban planning strategies aimed at mitigating temperature-related impacts in urban areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.10 Association between Biophysical Parameter and LST for the Year 1990\u003c/h2\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\u003eCorrelation Matrix of Environmental Parameters for the Year 1990\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990 Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.89362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.996885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.90986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.90136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.80393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.969206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.980026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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 table:1 shows a correlation analysis for the year 1990 between Land Surface Temperature (LST) and several spectral indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Bareness Index (NDBaI), and the Normalized Difference Water Index (NDWI). The correlation coefficients indicate the strength and direction of the relationships between these variables.\u003c/p\u003e \u003cp\u003eThe correlation coefficient between LST and NDVI is -0.89362, showing a strong negative relationship. This means that as NDVI rises (reflecting denser vegetation), LST generally falls, which is consistent with the expected cooling effect of vegetation on surface temperatures. On the other hand, the correlation coefficients between LST and urban-related indices (NDBI and NDBaI) are significantly high, indicating strong positive correlations. This suggests that as urbanization or built-up areas increase, LST tends to increase as well, aligning with the urban heat island effect where urban areas have higher temperatures than their surroundings.\u003c/p\u003e \u003cp\u003eAdditionally, the correlation between LST and NDWI, which indicates water presence, is moderately negative. This implies that areas with higher water content generally have lower surface temperatures, likely due to the cooling effect of water bodies. These correlation values offer important insights into the relationships between LST and various land cover types, helping to understand surface temperature dynamics and informing urban planning, environmental management, and climate change mitigation efforts..\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.11 Association between Biophysical Parameter and LST for the Year 2000\u003c/h2\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\u003eCorrelation Matrix of Environmental Parameters for the Year 2000\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000 Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.97869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.952897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.96596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.95756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.88821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.897225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.980234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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 table:2 showcases a correlation analysis examining the relationship between Land Surface Temperature (LST) and several spectral indices, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Difference Water Index (NDWI). The correlation coefficients highlight the extent of the linear association between these variables.\u003c/p\u003e \u003cp\u003eLand Surface Temperature (LST) shows a strong negative correlation with the Normalized Difference Vegetation Index (NDVI) (-0.97869), indicating that areas with more vegetation cover tend to have lower temperatures. On the other hand, there are positive correlations between LST and the Normalized Difference Built-up Index (NDBI) (0.952897), Normalized Difference Bareness Index (NDBaI) (0.993283), and Normalized Difference Water Index (NDWI) (0.960556). This suggests that built-up areas, bare surfaces, and water bodies generally have higher temperatures. Additionally, the strong negative correlations between NDVI and the other indices (NDBI, NDBaI, and NDWI) indicate that densely vegetated areas typically have lower levels of built-up surfaces, bare ground, and water bodies. This aligns with ecological principles, where vegetated areas provide cooling effects through transpiration and shading. Overall, the correlation analysis highlights the complex relationships between land surface temperature and various land cover types, underscoring the importance of considering multiple spectral indices to understand the thermal behavior of different land surfaces.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.12 Association between Biophysical Parameter and LST for the Year 2019\u003c/h2\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\u003eCorrelation Matrix of Environmental Parameters for the Year 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019 Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.93799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.933233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.76708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.934193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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\u003eTable:3 The correlation analysis of environmental parameters for 2019 reveals significant relationships. There is a strong negative correlation (-0.9411) between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), indicating that higher LST corresponds to lower NDVI, reflecting reduced vegetation density. Conversely, LST shows strong positive correlations with both Normalized Difference Built-up Index (NDBI) (0.94735) and Normalized Difference Bareness Index (NDBaI) (0.991199), suggesting higher temperatures are associated with increased built-up areas and bareness, which absorb more heat.\u003c/p\u003e \u003cp\u003eNDVI exhibits moderate negative correlations with NDBI (-0.8239), NDBaI (-0.93799), and Normalized Difference Water Index (NDWI) (-0.76708), indicating that areas with denser vegetation tend to have fewer built-up areas, less bareness, and lower water presence. Notably, NDBI and NDBaI are strongly positively correlated (0.968742), indicating that built-up areas and bareness often coexist.\u003c/p\u003e \u003cp\u003eOverall, these findings underscore the complex interplay among LST, vegetation density, built-up areas, bareness, and water presence, providing valuable insights into the environmental dynamics of the study area in 2019.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCULSION","content":"\u003cp\u003eThe coefficient of determination R\u0026sup2; values for the years 1990, 2000, and 2019 indicate the strength of the relationship between the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) for those respective years. A higher R\u0026sup2; value signifies a stronger correlation between the two indices, suggesting a more robust relationship between built-up areas and vegetation cover. In 1990, the R\u0026sup2; value of 0.8278 suggests a relatively strong correlation between NDBI and NDVI, indicating that changes in built-up areas are closely associated with changes in vegetation cover. This could imply that urbanization processes and land use changes during this period significantly impacted vegetation patterns.By 2000, the relationship between NDBI and NDVI strengthened further, with an R\u0026sup2; value of 0.9331. This suggests a highly significant correlation between built-up areas and vegetation cover, indicating that urban expansion and land development activities during this period had a profound impact on the surrounding vegetation.However, in 2019, the R\u0026sup2; value decreased to 0.6788, indicating a weaker correlation between NDBI and NDVI compared to previous years. This could suggest a shift in land use dynamics or changes in environmental factors that influenced vegetation patterns independently of urbanization. Alternatively, it could also indicate limitations in the applicability of NDBI and NDVI indices for capturing the complexity of land cover changes in more recent years.Overall, the analysis of NDBI versus NDVI provides valuable insights into the dynamics of urbanization and vegetation cover over time. The variations in R\u0026sup2; values across different years highlight the changing relationships between built-up areas and vegetation, reflecting the dynamic nature of land use and environmental processes. These findings are essential for urban planning, environmental management, and sustainable development initiatives, as they contribute to a better understanding of the interactions between urbanization and ecosystem dynamics. The outcomes of this study underscore the intensification of Urban Heat Island (UHI) effects, attributed to rapid urbanization between 1990 and 2019. The positive correlation observed between Land Surface Temperature (LST) and Normalized Difference Built-up Index (NDBI) further validates the impact of urbanization on temperature rise. While infrastructural enhancements have led to vegetation losses in some areas, there were also instances of vegetation improvement noted in certain regions. This indicates a complex interplay between urban development and greenery preservation.\u003c/p\u003e \u003cp\u003eIn conclusion, the proliferation of man-made constructions in urbanized regions and the reduction in vegetation cover due to infrastructural enhancements significantly contribute to temperature rise in the study region. To effectively mitigate the effects of UHIs, proactive measures should be implemented both before and during the development phases of townships. These measures could include the integration of green spaces, such as parks and urban forests, into urban planning initiatives, the implementation of green infrastructure like green roofs and permeable pavements, and the adoption of sustainable land use practices to preserve and enhance vegetation cover. By prioritizing these strategies, urban areas can mitigate the impacts of UHIs, enhance thermal comfort, and promote overall environmental sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePradeep Kumar Rajput: Dr. Rajput contributed to the conceptualization of the study, performed the remote sensing and GIS analysis, and played a key role in data collection, processing, and interpretation. He also led the manuscript writing, drafting the sections on Urban Heat Island effects, spatiotemporal analysis, and mitigation strategies. Additionally, he was responsible for coordinating the research work and corresponding with the journal.Durgesh Singh: He was involved in reviewing and editing the manuscript, providing critical feedback on the study's structure and scientific relevance. Dr. Singh also contributed to the literature review and discussion on urban planning and policy implications of the findings\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this research are included in the presented Tables and Figures in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarlson, T. N. (1986). Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sensing Reviews, 197\u0026ndash;247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElhadi K. Mustafa, Y. C. (2020). Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms. \u003cem\u003eAdvances in Civil Engineering\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehdi Bokaie, M. K. (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, 94\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eozelkhan, E. (2013). Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data. \u003cem\u003eEuropean Journal of remote sensing\u003c/em\u003e .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSumit Khandelwal, R. G. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 87\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, D., Zhang, F., Zhang, M., Meng, Q., Jim, C.Y., Shi, J., Tan, M.L. and Ma, X., 2022. Landscape and vegetation traits of urban green space can predict local surface temperature. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e825\u003c/em\u003e, p.154006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngel, R.A., Millard-Ball, A. and Turner, V.K., 2023. Contributions of roads to surface temperature: evidence from Southern California. \u003cem\u003eEnvironmental Research Communications\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), p.015004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantamouris, M. (2015). Urban Climate Mitigation Techniques. Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeidarinejad, G., \u0026amp; Kleerekoper, L. (2017). Urban heat island mitigation strategies: A state-of-the-art review on Kuala Lumpur, Singapore and Hong Kong. Journal of Building Engineering, 11, 1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarlan, S. L., Brazel, A. J., Prashad, L., Stefanov, W. L., \u0026amp; Larsen, L. (2006). Neighborhood microclimates and vulnerability to heat stress. Social Science \u0026amp; Medicine, 63(11), 2847\u0026ndash;2863.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman, P., \u0026amp; Thornley, A. (2017). Urban planning in Europe: International competition, national systems, and planning projects. Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmona, M., Tiesdell, S., Heath, T., \u0026amp; Oc, T. (2010). Public places, urban spaces: The dimensions of urban design. Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCervero, R., \u0026amp; Murakami, J. (2010). Rail and property development in Hong Kong: Experiences and extensions. Transportation Research Record, 1743(1), 89\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhern, J. (2013). Urban landscape sustainability and resilience: The promise and challenges of integrating ecology with urban planning and design. Landscape Ecology, 28(6), 1203\u0026ndash;1212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajer, M. A. (2016). Smart urban governance: The case of Amsterdam. In Smart Urbanism (pp. 29\u0026ndash;40). Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorida, R. (2017). The new urban crisis: How our cities are increasing inequality, deepening segregation, and failing the middle class-and what we can do about it. Basic Books\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnguelovski, I., Connolly, J. J. T., Garcia-Lamarca, M., Cole, H., Pearsall, H., \u0026amp; Newell, J. P. (2016). Urban planning, poverty and inequality: resolving the paradox in the global south. Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., Ng, E., An, X., Ren, C., \u0026amp; Wang, Z. (2020). Evaluating the cooling effects of urban greenery in relation to the spatial configurations of street canyons. Building and Environment, 167, 106437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOke, T. R. (2017). The heat island of the urban boundary layer: characteristics, causes and effects. In Remote Sensing of Urban Heat Islands (pp. 3\u0026ndash;19). CRC Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePomerantz, M., Rosado, P., Levinson, R., \u0026amp; Akbari, H. (2019). Effects of heat islands and heat waves on people living in urban and rural areas. Journal of Extreme Events, 6(02), 1950006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335\u0026ndash;344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman, M. M., Shi, Z., Zhang, Y., \u0026amp; Zhu, X. (2019). Impact of urban greenery on land surface temperature using remote sensing: A case study in Dhaka, Bangladesh. Remote Sensing, 11(12), 1486.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z., Strahler, A. H., \u0026amp; Jupp, D. L. (2017). Retrieval of leaf area index from high resolution satellite images over forest areas. Remote Sensing of Environment, 190, 279\u0026ndash;290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorgani, S.A., Panahi, M. and Rezaie, F., 2013, November. The Relationship between NDVI and LST in the urban area of Mashhad, Iran. In \u003cem\u003eInternational conference on civil engineering architecture \u0026amp; urban sustainable development\u003c/em\u003e (Vol. 2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatemi, M. and Narangifard, M., 2019. Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), p.127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue, W., Xu, J., Tan, W. and Xu, L., 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM\u0026thinsp;+\u0026thinsp;data. International journal of remote sensing, \u003cem\u003e28\u003c/em\u003e(15), pp.3205\u0026ndash;3226.\u003c/span\u003e\u003c/li\u003e\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":"Urban planning, LST, vegetation, and environmental parameters","lastPublishedDoi":"10.21203/rs.3.rs-5309216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5309216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn urban heat island is an urban area that is significantly warmer due to anthropogenic activity and unplanned development of city that is surrounded rural area. The temperature difference usually is high at night than the day temperature. The urban heat island is particularly noticeable during the summer and winter months. The main cause of the urban heat island effect is the form the disturb of natural setting and modification of land surface area. As the per the United nation report worldwide rapid growth of urbanization and globalization brought more than 54% of world\u0026rsquo;s population in urban areas which is only around 33% 50 years back and it expected to increases up 66% by the end of 2050. In this paper to investigate the relationship between land surface temperature and biophysical parameter in the selected area, satellite data used for extraction of biophysical and LST parameters for the study of urban heat island and its effects. To investigate and understand the effects land surface temperature, form the period of 1990 2000 and 2019 satellite data are used to retrieve the land surface temperature (LST), Normalized vegetation index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Built-up and Bareness Index (NDBaL). The biophysical parameters of different time period are used to analysis the behavior change over the LST. Land cover indices were derived in order established the relationship between LST and the indices, in the result a higher level of LST was found to be associated with the lower NDVI in this research.\u003c/p\u003e","manuscriptTitle":"Investigating Urban Heat Island Effects and Mitigation Strategies in Ahmedabad, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 05:31:48","doi":"10.21203/rs.3.rs-5309216/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":"d363367c-2dd9-444c-a5e1-7a55ca1ee14b","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T18:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 05:31:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5309216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5309216","identity":"rs-5309216","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}