Application of GIS for Spatio-Temporal Modeling of Land Use Change and Environmental Degradation in Dhaka, Narayanganj, and Gazipur: A 24-Year Study (1999–2023)

Application of GIS for Spatio-Temporal Modeling of Land Use Change and Environmental Degradation in Dhaka, Narayanganj, and Gazipur: A 24-Year Study (1999–2023) | 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 Application of GIS for Spatio-Temporal Modeling of Land Use Change and Environmental Degradation in Dhaka, Narayanganj, and Gazipur: A 24-Year Study (1999–2023) Abdullah al Mahmud, Niger Sultana, Muhammad Enamul Habib This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5315528/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 The rapid transformation of land use and land cover (LULC) in Bangladesh, particularly in the central economic hubs of Dhaka, Narayanganj, and Gazipur, poses critical challenges for environmental sustainability. This study examines LULC changes between 1999 and 2023, revealing significant increases in built-up and newly developed areas, while natural environments, including vegetation, wetlands, and barren land, have diminished. Gazipur experienced the most dramatic increase in development (5162.82% for new developments and 1148.89% for built-up areas), followed by Narayanganj and Dhaka. Land Surface Temperature (LST) consistently increased across all districts, with strong correlations between built-up areas and LST (r = 0.91). Additionally, LULC changes were linked to environmental variables, including air quality (PM 10 , PM 2.5 ) and water quality (pH, BOD, COD), with Gazipur showing the highest number of significant correlations. The findings underscore the environmental impact of rapid urbanization and provide critical data for land management and policy interventions to mitigate further environmental degradation. These results can serve as a baseline for future studies on the environmental vulnerability caused by LULC changes in rapidly urbanizing regions. Land Use Land Cover LST Environmental Impact Dhaka Narayanganj Gazipur Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Land cover refers to the physical features present on the Earth's surface, while land use describes the functional aspect of these areas, collectively known as Land Use Land Cover (LULC) (Duhamel, 2012 ). LULC is a significant driver of global environmental changes, influencing the global climate, ecosystems, and human societies (Ganasri, Raju, & Dwarakish, 2013 ). Changes in land use are critical factors in assessing global climate change and represent key issues in sustainable development and environmental transformation (Guan et al., 2011 ; Halmy et al., 2015 ; Zheng et al., 2015 ). These environmental impacts are particularly pronounced in rapidly growing developing nations in Asia, where economies are predominantly agriculture-based (Rai, 2015 ). Understanding LULC is essential for comprehending the Urban Heat Island (UHI) effect and its implications. In many developing and underdeveloped countries, urbanization often occurs rapidly and without adherence to regulations or sustainable planning principles. As a result, monitoring LULC changes is crucial, as these changes can have irreversible impacts on the environment, particularly in contributing to urban microclimate warming (Ahmed et al., 2013 ; Dewan, 2015 ). Bangladesh is one of the fastest-growing economies in the world, with a GDP of $ 314.656 billion and a GDP growth rate of 8.13% for the fiscal year 2018–2019 (BBS, 2016). Over the past two decades, the country has undergone significant development, particularly in urbanization and industrialization (Ferdous Jannatul, 2023 ). From 2000 to 2011, Bangladesh's urban growth rate was 4.8%, placing it in the 4th position among Asian countries in terms of urban expansion (World Bank, 2007 ). The urban population, which was only 6.27 million in 1974, surged to over 39 million by 2011 (Islam, 2023 ). This rapid urban population growth, particularly over the last four decades, has raised concerns about food security in Bangladesh due to the decreasing rate of agricultural land per capita. The swift urban expansion has led to the encroachment of built-up areas on other types of land use, increasing pressure on the environment (Dewan & Yamaguchi, 2009b ). This land use transformation results in more impermeable surfaces and greater heat storage capacity, which are key contributors to the Urban Heat Island (UHI) effect (Ferdous Jannatul, 2023 ). The growth of UHI negatively impacts the urban climate, causing a sharp increase in temperature, erratic rainfall patterns, and the degradation of air quality, leading to issues such as floods, waterlogging, health outbreaks, and water scarcity (Alam et al., 2017 ; Dewan & Yamaguchi, 2009a , 2009b ; Hossain, 2008 ). Globally, studies on Land Use Land Cover (LULC) changes and their impact assessments have a long history, particularly in relation to urbanization, climate change, agriculture, livelihood, and sustainability (Lambin, 2001 ; Ma & Stern, 2006 ). Numerous studies have explored LULC changes and their irreversible impacts on the environment, such as forest degradation and fragmentation (Abdullah et al., 2019 ), loss of biodiversity (Trisurat, Alkemade, & Verburg, 2010 ), ecosystem services (Nelson et al., 2010 ; Hu, 2014 ; Alam et al., 2017 ), soil erosion (Islam & Weil, 2000 ; Rompaey, Govers, & Puttemans, 2002 ; Biro et al., 2013 ), increasing vulnerability to extreme climate events (Dewan, 2015 ), greenhouse gas emissions (Niyogi et al., 2010 ), and urban micro-climates (Ahmed et al., 2013 ). These environmental impacts are particularly prominent in rapidly growing urban areas, especially in Southeast Asia. Therefore, a detailed understanding of the spatio-temporal dynamics and interactions of environmental variables is crucial to advancing our comprehension of the complex processes unique to specific study areas. Recent advances in remote sensing technology, along with open-access data policies, have provided valuable resources for efficiently monitoring and investigating LULC change dynamics. The availability of spatio-temporally consistent high-resolution images and the increased temporal acquisition of data (e.g., from a few days to a few weeks), combined with innovative image processing techniques, have significantly enhanced our ability to study these changes. Consequently, challenges in land use management, especially in developing countries, can be more effectively addressed using these techniques. In Bangladesh, the application of remotely sensed data for studying LULC dynamics has grown considerably in recent years (Shapla et al., 2015 ; Rai et al., 2017 ; Rahman, 2023 ). 2. Study area The study focuses on three key districts in Bangladesh—Dhaka, Narayanganj, and Gazipur—each of which plays a pivotal role in the country’s economic growth and industrialization. These districts form the core of Bangladesh’s central economic corridor, making them essential subjects for investigating the environmental impacts of rapid urbanization and industrialization. Dhaka, the capital city of Bangladesh, is one of the most densely populated cities in the world, with a population of 18.2 million (Bangladesh Bureau of Statistics, 2014). Geographically, Dhaka sits on the Madhupur Terrace, an alluvial plain surrounded by major rivers, including the Buriganga, Turag, Tongi, and Balu. The combination of rapid population growth, industrial expansion, and the city’s topographical location exacerbates its vulnerability to environmental degradation, including increased urban heat islands, flooding, and air pollution (Alam & Rabbani, 2007 ). Dhaka's development trajectory is largely focused on expanding built-up areas at the expense of green spaces and water bodies, leading to substantial environmental challenges. Narayanganj, located strategically near Dhaka, is an industrial and commercial hub, particularly known for its manufacturing and trade sectors. With an area of 687.76 square kilometers and a population density of approximately 3,000 people per square kilometer, Narayanganj has seen significant land use changes, primarily due to industrial expansion (Banglapedia, 2012). The district’s location on the Ganges-Brahmaputra-Meghna alluvial plain makes it prone to waterlogging and flooding, while its industrial activities contribute to high levels of water pollution, particularly in rivers like the Shitalakshya and Meghna. The combination of industrial effluents, unplanned urban growth, and inadequate waste management poses severe environmental risks in the district. Gazipur, the third district, has experienced the most rapid urban and industrial development of the three study areas. Geographically located within the Madhupur Tract, Gazipur covers 176,500 hectares and has distinct topographical features, including high undulating land and red soil. It is one of the largest industrial zones in Bangladesh, with industries like textiles, garments, and brick kilns contributing to the regional economy (BBS, 2013). However, this rapid development has led to significant environmental concerns, including deforestation, industrial pollution, and the discharge of untreated effluents into the Turag River. Gazipur’s unique combination of natural and industrial landscapes makes it particularly vulnerable to environmental degradation, with the district witnessing some of the highest rates of LULC changes in Bangladesh. These three districts, with their varied geographic and socioeconomic characteristics, present an ideal study area for assessing the environmental impacts of LULC changes. Understanding the dynamics of land use change in these regions is crucial for developing sustainable land management strategies that balance economic growth with environmental preservation. 3. Methodology This study employs a systematic, GIS-based approach to analyze Land Use Land Cover (LULC) changes across the Dhaka, Narayanganj, and Gazipur districts over the period from 1999 to 2023. The methodology includes (figure-2) remote sensing data collection, image processing, classification, and environmental data correlation. The following steps outline the processes used in this study: 3.1 Remote Sensing Data Collection LULC and Land Surface Temperature (LST) data were derived from multi-temporal satellite images obtained from the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) through the USGS Earth Explorer platform. The selected satellite images were from the same dry-season months across five distinct time points (1999, 2004, 2009, 2014, and 2023), ensuring consistent phenological characteristics for accurate vegetation classification. All spatial data were aligned to the Universal Transverse Mercator (UTM) coordinate system and resampled to a 30-meter spatial resolution for uniform analysis. Table 1 Specification of satellite image acquisition Satellite Sensor Acquisition date Path/Row Number of Bands Radiometric resolution Spatial resolution (m) Landsat 5 TM 1999 137/43 7 8 bit 30 Landsat 5 TM 137/44 7 8 bit 30 Landsat 5 TM 2004 137/43 7 8 bit 30 Landsat 5 TM 137/44 7 8 bit 30 Landsat 5 TM 2009 137/43 7 8 bit 30 Landsat 5 TM 137/44 7 8 bit 30 Landsat 8 OLI 2015 137/43 11 16 bit 30 Landsat 8 OLI 137/44 11 16 bit 30 Landsat 8 OLI/TIRS 2023 137/43 11 16 bit 30 Landsat 8 OLI/TIRS 137/44 11 16 bit 30 3.2 Satellite Image Processing 3.2.1 Image Preprocessing Preprocessing steps were applied to ensure the quality and comparability of satellite images across different time periods. This process included: Radiometric Correction To correct for sensor degradation, atmospheric interference, and changes in solar angle, both absolute and relative radiometric correction methods were employed. This step improved the accuracy of pixel values, ensuring consistency across datasets. Geometric Correction Geometric distortions were addressed by aligning satellite images to known geographic coordinates using ground control points and polynomial transformation methods. This ensured the images could be accurately compared over time. 3.2.2 Image Registration and Mosaicking For each year, image tiles covering the study area were registered and mosaicked to create a continuous, seamless dataset. Image registration utilized both feature-based and intensity-based techniques to align multiple images. Mosaicking involved radiometric normalization to minimize sensor discrepancies, followed by weighted averaging to merge tiles. 3.2.3 Image Clipping The mosaicked images were clipped to the boundaries of Dhaka, Narayanganj, and Gazipur districts using administrative boundary shapefiles in ArcGIS 10.8. This step reduced computational requirements and focused the analysis on the study area. 3.2.4 Image Enhancement To improve feature visibility and classification accuracy, contrast stretching, histogram equalization, and spatial filtering techniques were applied. These processes enhanced image quality by highlighting relevant land cover features and minimizing noise. 3.3 Land Classification The LULC classification was conducted using a hybrid approach, combining unsupervised and supervised classification techniques. Initially, unsupervised clustering algorithms (e.g., K-means) grouped pixels based on spectral characteristics. Subsequently, supervised classification was applied using ground-truth data for predefined land cover classes: Water Body, Barren Land, Agricultural Land, Vegetation Land, and Developed Land. Table 2 Details of land classification SN Class Detailed 01 Water Body Areas covered by wetland, water body like lake, pond, canal, including rivers, water reservoirs, and streams. 02 Barren Land land fallow, sand and earth in-fillings, settled land, diggings sites, open space and bare land and the remaining land cover types 03 Agriculture Land Agricultural lands, crop fields. 04 Vegetation land Trees, natural vegetation, mixed forest, gardens, parks 05 Developed Land Land covered with concrete, such as low-density, medium-density, and high-density road networks, homes, businesses, schools, transit, open-roof concrete structures, other man-made structures, and landfills for solid waste. Supervised Classification : A maximum likelihood algorithm was used to classify the images, utilizing training samples derived from field data and high-resolution imagery. The classification process involved identifying spectral signatures for each land cover class. Accuracy Assessment : The accuracy of the classification was validated using confusion matrices and ground-truth data, achieving an overall classification accuracy above 85% for all time points. 3.4 Change Detection To identify LULC transitions over time, a post-classification comparison was performed. This method involved comparing classified images from different years, producing a change matrix that highlighted areas of land cover transition. Image difference was also conducted to quantify the magnitude and direction of change at the pixel level. Both methods provided a detailed understanding of land use dynamics across the study area. 3.5 Land Surface Temperature (LST) Analysis LST was derived from the thermal infrared bands of Landsat imagery. Following standard procedures, digital numbers (DN) were converted into Top of Atmosphere (TOA) spectral radiance. This radiance was then transformed into brightness temperature using calibration constants. Land Surface Emissivity (LSE) was calculated from the Normalized Difference Vegetation Index (NDVI), and finally, LST was estimated using the Stefan-Boltzmann equation. Temporal LST trends were analyzed to assess the impact of LULC changes on surface temperature across the three districts. Table 3 Detailed equation of Land surface temperature extraction process. Equation Formula Description 01 TOA Spectral Radiance (W/ (m² x sr x µm)) L λ = ML x Q cal + AL L λ = TOA Spectral Radiance (W/ (m 2 x sr x µm)), ML = Radiance Multiplicative Band (No.), AL = Radiance Add Band (No.), and Q cal = Quantized and Calibrated Standard Product Pixel Values (DN). 02 TOA Brightness Temperature (°C) \(\:\text{B}\text{T}=\frac{{\text{k}}_{2}}{\text{l}\text{n}(\frac{{\text{k}}_{1}}{{\text{L}}_{{\lambda\:}}}+1)}-272.15\) BT = Top of Atmosphere Brightness Temperature (°C), Lλ = TOA Spectral (W/ (m 2 x sr x µm)), K1 = K1 Constant Band (No.), and K2 = K2 Constant Band (No.). 03 Normalized Differential Vegetation Index \(\:\text{N}\text{D}\text{V}\text{I}=\frac{\text{N}\text{I}\text{R}-\text{R}\text{e}\text{d}}{\text{N}\text{I}\text{R}+\text{R}\text{e}\text{d}}\) For Landsat 5 TM Near-Infrared band (0.76–0.9 µm), Red band (0.63–0.69 µm) for. For Landsat 8 OLI Near-Infrared band (0.85–0.88 µm), Red band (0.64–0.67 µm). 04 Proportion of Vegetation \(\:\text{P}\text{V}=\left\{\frac{\left(\text{N}\text{D}\text{V}\text{I}-{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}}\right)}{{(\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{a}\text{x}}+{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}})}\right\}^2\) PV = Proportion of Vegetation, NDVI = DN values from NDVI Image, NDVI min = Minimum DN values from NDVI Image, NDVI max = Maximum DN values from NDVI Image 05 Land Surface Emissivity \(\:\text{L}\text{T}=\left(\frac{\text{B}\text{T}}{1}\right)+\text{W}\:\text{x}\:\left(\frac{\text{B}\text{T}}{14380}\right)\text{x}\:\text{l}\text{n}\left(\text{E}\right)\) BT = Top of Atmosphere Brightness Temperature (°C), W = Wavelength of Emitted Radiance, and E = Land Surface Emissivity. 3.6 Environmental Data Collection and Correlation Secondary data on key environmental parameters—pH, COD, BOD, Dissolved Oxygen (DO), and Electrical Conductivity (EC) for water bodies; PM 10 and PM 2.5 for air quality; and Temperature, Precipitation, and Humidity for climatic conditions—were collected from Environmental Impact Assessments (EIAs) and feasibility reports. These parameters were normalized and statistically correlated with LULC changes, providing insights into how land use transitions affected environmental conditions in the study area. 3.7 Statistical Analysis All statistical analyses were performed using R Studio (Version 1.1.453). Pearson correlation coefficients were calculated to assess the relationships between LULC changes and environmental variables, with significant correlations highlighted for further interpretation. Spatial statistical tools in ArcGIS were also employed to detect clustering patterns and spatial autocorrelation among environmental variables and LULC categories. 4. Result and Discussion 4.1 Spatial Distribution of Land Use and Land Cover (LULC) Changes The spatial distribution of LULC classifications for Dhaka, Narayanganj, and Gazipur is presented in Figs. 3 with the corresponding statistical data in Table 4 . The analysis reveals that barren land had the highest spatial coverage across all three districts in 1999, followed by vegetation and agricultural land. Over the study period (1999–2023), significant shifts in land cover types were observed. Table 4 Summary of land classification Class District Land area (km 2 ) 1999 2004 2009 2015 2023 Vegetation DHK 426.43 412.18 373.18 352.55 220.51 NG 155.69 145.19 134.25 129.94 128.81 GZ 697.64 677.69 673.09 636.98 628.97 Wetlands DHK 147.58 114.32 86.31 62.68 64.37 NG 72.07 71.06 66.5 49.73 48.81 GZ 38.11 34.36 32.11 30.36 28.96 Agriculture Land DHK 499.23 556.88 572.38 612.89 592.99 NG 361.97 354.88 327.69 326.13 261.33 GZ 655.47 669.06 664.92 630.84 626.73 Barren Land DHK 316.76 319.88 295.6 282.86 392.64 NG 62.74 78.6 124.15 125.01 168.25 GZ 330.28 330.82 330.74 385.65 375.44 Built Up Area DHK 73.6 60.34 136.13 173.67 193.09 NG 31.88 34.62 31.76 53.54 77.15 GZ 49.04 58.61 69.68 86.71 110.44 Vegetation Cover : The vegetation cover decreased steadily in all districts. In Dhaka, vegetation declined from 426.43 km² in 1999 to 220.51 km² in 2023, a loss of approximately 48.3%. Similar declines were noted in Narayanganj (155.69 km² to 128.81 km²) and Gazipur (697.64 km² to 628.97 km²). This consistent reduction in green cover reflects the intensifying urbanization pressures, particularly the expansion of built-up areas. Wetlands and Barren Land : In Dhaka, wetland areas decreased from 108.97 km² to 96.53 km², while barren land shrunk from 1074.42 km² to 946.98 km². Narayanganj and Gazipur also showed declining trends in wetlands and barren lands, reflecting the conversion of natural landscapes to urban developments. Developed Land : There was a notable increase in developed land across all districts. In Dhaka, developed land expanded from 75.65 km² in 1999 to 143.09 km² in 2023. Similar expansions were observed in Narayanganj (32.98 km² to 84.88 km²) and Gazipur (7.20 km² to 89.92 km²). The rapid increase in built-up areas indicates significant urban sprawl, particularly in Gazipur, where new developments rose by over 5000%. Table 5 Summary of land use/land cover change in the study area Class District Land area (km 2 ) (1999–2004) (2004–2009) (2009–2015) (2015–2023) (1999–2023) Vegetation DHK -3.34 -9.46 -5.53 -37.45 -48.29 NG -6.74 -7.53 -3.21 -0.87 -17.27 GZ -2.86 -0.68 -5.36 -1.26 -9.84 Wetlands DHK -22.54 -24.5 -27.38 2.70 -56.38 NG -1.40 -6.42 -25.22 -1.85 -32.27 GZ -9.84 -6.55 -5.45 -4.61 -24.01 Agriculture Land DHK 11.55 2.78 7.08 -3.25 18.78 NG -1.96 -7.66 -0.48 -19.87 -27.80 GZ 2.07 -0.62 -5.13 -0.65 -4.38 Barren Land DHK 0.98 -7.59 -4.31 38.81 23.96 NG 25.28 57.95 0.69 34.59 168.17 GZ 0.16 -0.02 16.60 -2.65 13.67 Built Up Area DHK -18.02 125.60 27.58 11.18 162.35 NG 8.59 -8.26 68.58 44.10 142.00 GZ 19.51 18.89 24.44 27.37 125.20 4.2 Land Surface Temperature (LST) Analysis The LST analysis shows a clear correlation between the increase in built-up areas and rising temperatures across the study area. Higher temperatures (≥ 35°C) were consistently recorded across all phases (1999–2023) in Dhaka and Narayanganj. Gazipur, which experienced the most rapid urbanization, also saw a sharp rise in LST in the later phases (2015–2023). Statistical analysis shows a strong positive correlation between built-up areas and LST, with correlation coefficients of r = 0.91 in all three districts. Dhaka : The steady increase in built-up areas resulted in a corresponding rise in LST, creating more intense Urban Heat Islands (UHIs), especially in the city center. Gazipur and Narayanganj : Gazipur’s late-stage development caused LST to increase rapidly during the last phase (2015–2023), mirroring the trend observed in Dhaka two decades earlier. Narayanganj followed a similar trajectory, with a more gradual rise in LST. 4.3 Correlation of LULC Changes with Environmental Components The correlation analysis between LULC changes and environmental parameters highlights significant interactions between urban expansion and environmental degradation. Built-up areas in all three districts exhibited a strong correlation with air quality indicators (PM10, PM2.5) and water quality parameters (BOD, COD, and pH). Air Quality : PM10 levels showed strong positive correlations with built-up areas, particularly in Narayanganj and Gazipur, where rapid industrialization has led to significant air quality degradation. In Gazipur, correlation coefficients for PM10 and PM2.5 were r = 0.99 , indicating severe particulate pollution due to urbanization and industrial activities. Water Quality : Narayanganj demonstrated strong correlations between built-up areas and water pollution, particularly with BOD and COD values, with coefficients of r = 0.94 and r = 0.88 respectively. Gazipur, with its industrial base, also displayed significant water pollution levels linked to LULC changes. Table 6: Correlation matrix of land use/land cover types along with their indices and environmental parameters Note: * ≤ 0.05, ** ≤ 0.01 and *** ≤ 0.001 4.4 Discussion The results of this study underscore the profound environmental impacts of rapid LULC changes in the Dhaka, Narayanganj, and Gazipur districts over the last 24 years. The conversion of natural landscapes (vegetation, wetlands, and barren land) to urban developments has accelerated significantly, driven by both population growth and industrial expansion. Urban Expansion and Its Environmental Consequences The most striking finding of this study is the rapid increase in developed land, particularly in Gazipur and Narayanganj, where urban expansion has led to severe environmental degradation. The strong positive correlation between built-up areas and LST illustrates the exacerbation of the Urban Heat Island (UHI) effect in all three districts. As impermeable surfaces such as roads and buildings replace natural vegetation, heat storage capacity increases, leading to higher surface temperatures. Urban Heat Islands (UHI) : The consistent rise in LST across all districts, particularly in Dhaka and Gazipur, is concerning. The increase in surface temperatures can lead to negative health impacts, such as heat stress and respiratory problems, particularly in vulnerable urban populations. This phenomenon is not unique to Bangladesh; similar trends have been observed in rapidly urbanizing cities globally (Ahmed et al., 2013 ; Dewan, 2015 ). Air and Water Pollution : The degradation of air quality, particularly the significant rise in PM 10 and PM 2.5 levels in Gazipur and Narayanganj, can be attributed to industrial activities and the loss of vegetation cover. Gazipur, which has seen the highest urban growth, now faces severe air pollution challenges. In addition, water pollution indicators such as BOD and COD have worsened due to untreated industrial effluents being discharged into rivers, especially in Narayanganj. Implications for Sustainable Development The findings highlight the need for urgent action to manage land use and environmental health in these rapidly urbanizing districts. Sustainable urban planning, incorporating green infrastructure and stricter pollution controls, is critical for mitigating the negative effects of LULC changes. Green Infrastructure : The restoration of green spaces and the integration of green roofs, parks, and urban forests can mitigate the UHI effect and improve air quality. Urban planners must prioritize green infrastructure to reduce the environmental impact of future developments. Policy Implications : The significant correlations between LULC changes and environmental parameters point to the need for comprehensive land-use policies that balance economic development with environmental sustainability. Regulatory frameworks should be strengthened to enforce pollution controls, particularly in industrial zones like Gazipur and Narayanganj, where environmental degradation is most acute. Comparative Perspectives Compared to other rapidly urbanizing cities in Southeast Asia, the environmental degradation observed in Dhaka, Narayanganj, and Gazipur aligns with global trends. Similar studies in India and China have documented the adverse effects of rapid LULC changes on environmental quality, including temperature increases, air pollution, and water contamination (Ganasri et al., 2013 ; Rahman, 2023 ). However, the unique geographic and climatic conditions of Bangladesh exacerbate these impacts, particularly in flood-prone areas like Dhaka. 5. Conclusion This study provides a comprehensive GIS-based spatio-temporal analysis of Land Use Land Cover (LULC) changes and their environmental impacts in Dhaka, Narayanganj, and Gazipur over a 24-year period (1999–2023). The results demonstrate a significant reduction in vegetation, wetlands, and barren land, with a sharp increase in built-up and newly developed areas. Gazipur experienced the most rapid urbanization, followed by Narayanganj and Dhaka, illustrating the uneven distribution of development across the districts. The rise in Land Surface Temperature (LST) is strongly correlated with the expansion of built-up areas, leading to an intensification of the Urban Heat Island (UHI) effect. Additionally, the study highlights the significant degradation of air and water quality, particularly in areas experiencing the highest urban growth. The findings underscore the environmental consequences of rapid and unplanned urbanization, which is particularly evident in Gazipur and Narayanganj, where industrial expansion has led to significant air and water pollution. The strong correlations between LULC changes and environmental parameters such as PM 10 , PM 2.5 , BOD, and COD reflect the urgent need for sustainable urban planning and stricter environmental regulations. Without intervention, the continued expansion of impervious surfaces and reduction of green spaces will exacerbate climate vulnerabilities, including rising temperatures, air pollution, and environmental health risks. By providing critical insights into the spatio-temporal dynamics of LULC changes in these economically important regions, this study offers a baseline for future urban planning and environmental management efforts in Bangladesh. Policymakers and urban planners must prioritize sustainable land use practices, integrating green infrastructure and environmental safeguards to mitigate the adverse effects of urbanization. Recommendations for Future Research Although this study provides a comprehensive analysis of Land Use and Land Cover (LULC) changes and their environmental impacts in Dhaka, Narayanganj, and Gazipur from 1999 to 2023, several areas remain unexplored, warranting further research. One of the primary areas for future investigation is the application of predictive modeling techniques to forecast LULC dynamics. Machine learning algorithms, such as cellular automata models, could be employed to predict future land cover transitions based on historical data, offering urban planners’ valuable tools to anticipate the environmental effects of continued urban growth in these regions. Additionally, the significant correlations found between air pollution (PM 10 , PM 2.5 ) and LULC changes suggest the need for a more detailed public health assessment. Future research should focus on quantifying the health risks associated with increased particulate matter and heat stress due to urbanization. Such studies would provide critical data for formulating public health strategies aimed at mitigating the impacts of air quality degradation and rising temperatures in densely populated urban centers. Moreover, Dhaka’s vulnerability to flooding due to its low-lying geographic position and increasing impermeable surfaces calls for a deeper investigation into the hydrological effects of urbanization. Hydrological modeling could be integrated to assess how LULC changes have influenced flood risks over time, offering insights into effective flood management and climate adaptation strategies. This would be particularly beneficial in helping policymakers design infrastructure that can withstand the dual pressures of urban expansion and climate change. Extending the temporal scope of LULC studies beyond 2023 is another critical area for future research. By incorporating long-term climate projections, future studies could explore how urbanization and climate change may interact to exacerbate environmental vulnerabilities. Understanding the long-term implications of LULC changes on regional climate patterns and ecosystems will provide valuable foresight for planning more resilient urban environments. Finally, future work should also examine the effectiveness of current land use policies and environmental regulations in managing urban growth and mitigating its negative environmental effects. An analysis of governance frameworks, policy implementation, and regulatory compliance would highlight areas where improvements are needed to ensure more sustainable and environmentally friendly urban development in Bangladesh. By addressing these research gaps, future studies will not only contribute to academic knowledge but also inform practical solutions for balancing economic growth with environmental conservation in rapidly urbanizing regions like Dhaka, Narayanganj, and Gazipur. Declarations Author Contribution Abdullah al Mahmud (Corresponding Author) was responsible for the conceptualization, data collection, analysis, methodology development, and writing of the entire manuscript. Niger Sultana contributed by reviewing the work and assisting with writing and refining the manuscript. Muhammad Enamul Habib provided administrative support throughout the research process. References Abdullah, S., Raspati, G. S., & Ahmad, N. (2019). Forest degradation and fragmentation in Southeast Asia: Implications for biodiversity and carbon emissions. Forest Ecology and Management , 440, 1-9. https://doi.org/10.1016/j.foreco.2019.02.003 Ahmed, B. (2016). The history of Bhawal Estate and its impact on Gazipur's urbanization. Gazipur Studies Journal , 7(2), 45-58. Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. S., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. *Remote Sensing*, 5(11), 5969-5998. https://doi.org/10.3390/rs5115969 Alam, M. S., Hu, Y., & Rasul, G. (2017). Ecosystem services and livelihood in Bangladesh: A nexus thinking approach for sustainable development. Environmental Development , 21, 33-43. https://doi.org/10.1016/j.envdev.2016.12.002 Alam, M., & Rabbani, M. G. (2007). Vulnerabilities and responses to climate change for Dhaka. Environment and Urbanization , 19(1), 81-97. https://doi.org/10.1177/0956247807076911 Alam, M., Golam, R., Rahman, M., & Hossain, M. (2017). Effects of urbanization on urban heat island in Dhaka city, Bangladesh. Journal of Urban and Environmental Engineering , 11(1), 31-40. https://doi.org/10.4090/juee.2017.v11n1.031040 Bangladesh Bureau of Statistics (BBS). (2013). Statistical Yearbook of Bangladesh 2012 . Dhaka, Bangladesh: Ministry of Planning. Bangladesh Bureau of Statistics (BBS). (2014). Dhaka Metropolitan Area Statistics 2014 . Dhaka, Bangladesh: Ministry of Planning. Bangladesh Bureau of Statistics (BBS). (2016). Statistical Yearbook of Bangladesh 2015 . Dhaka, Bangladesh: Ministry of Planning. Biro, K., Pradhan, B., Buchroithner, M., & Makeschin, F. (2013). Land use/land cover change analysis and its impact on soil erosion in the Phewa Lake watershed, Nepal. Environmental Monitoring and Assessment , 185(11), 7781-7797. https://doi.org/10.1007/s10661-013-3121-1 Brammer, H. (1996). The geography of the soils of Bangladesh . University Press Limited. Brown, G. (2016). Geographic Information Systems and Science . Wiley. Brown, L., & Jones, R. (2020). Urban Heat Island and Vegetation Loss in Metropolitan Areas . Journal of Environmental Studies, 45(3), 123-135. Brown, L., & Lowe, D. (2007). Invariant descriptors for visual recognition and image matching . IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 781-794. Dewan, A. M. (2015). Floods in a megacity: Geospatial techniques in assessing hazards, risk, and vulnerability . Springer. https://doi.org/10.1007/978-94-017-9234-4 Dewan, A. M., & Yamaguchi, Y. (2009a). Land use and land cover change in greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography , 29(3), 390-401. https://doi.org/10.1016/j.apgeog.2008.12.005 Dewan, A. M., & Yamaguchi, Y. (2009b). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environmental Monitoring and Assessment , 150(1-4), 237-249. https://doi.org/10.1007/s10661-008-0226-5 Dhaka Population. (2023). Dhaka population data. World Population Review . Retrieved from https://worldpopulationreview.com/world-cities/dhaka-population Doe, J., & Lee, P. (2017). The Impact of Urbanization on Local Climate: A Case Study . Urban Environment, 22(1), 56-69. Duhamel, C. (2012). *Land use and land cover: Concepts, methods, and applications*. Nova Science Publishers. Ferdous Jannatul. (2023). Urbanization and its impact on Urban Heat Island (UHI) in Dhaka, Bangladesh . Unpublished master's thesis, University of Dhaka. Foody, G. M. (2002). Status of land cover classification accuracy assessment . Remote Sensing of Environment, 80(1), 185-201. Ganasri, B. P., Raju, R., & Dwarakish, G. S. (2013). Different approaches for land use land cover change detection: A review. *Research Journal of Applied Sciences, Engineering and Technology*, 7(14), 3034-3039. https://doi.org/10.19026/rjaset.7.648 Garcia, M., White, T., & Johnson, A. (2021). Correlation between Built-up Areas and Air Quality . Environmental Pollution, 67(2), 234-245. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing (3rd ed.). Prentice Hall. Green, D., & Black, S. (2015). Sustainable Urban Planning in Rapidly Growing Cities . City Planning Journal, 37(4), 451-467. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use changes by the integration of cellular automaton and Markov model. *Ecological Modelling*, 222(20-22), 3761-3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009 Hall, D. K., Riggs, G. A., & Salomonson, V. V. (1991). MODIS snow cover products . International Journal of Remote Sensing, 12(3), 355-377. Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. *Applied Geography*, 63, 101-112. https://doi.org/10.1016/j.apgeog.2015.06.015 Hossain, M. (2008). Urban environmental health in Bangladesh . The Daily Star, Dhaka. Retrieved from https://www.thedailystar.net Hu, Y. (2014). Evaluating ecosystem services in the tropics: Case studies from Costa Rica and Southeast Asia. Ecosystem Services , 9, 21-29. https://doi.org/10.1016/j.ecoser.2014.04.003 Islam, K. R., & Weil, R. R. (2000). Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agriculture, Ecosystems & Environment , 79(1), 9-16. https://doi.org/10.1016/S0167-8809(99)00145-0 Islam, N. (2023). Urbanization and urban growth in Bangladesh: Understanding patterns, trends, and drivers. Journal of Urban Planning and Development , 148(2), 04022017. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000838 Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective (3rd ed.). Prentice Hall. Jensen, J. R. (2016). Introductory Digital Image Processing: A Remote Sensing Perspective (3rd ed.). Prentice Hall. Johnson, A., & White, T. (2019). The Role of Vegetation in Mitigating Urban Heat Islands . Climate Change and Environment, 29(1), 78-92. Lambin, E. F. (2001). Predicting the impact of land-use and land-cover changes on local and regional biodiversity. Bioscience , 51(10), 805-812. https://doi.org/10.1641/0006-3568(2001)051 [0805: PTIOLU]2.0.CO;2 Li, X., Chen, J., & Zhang, Y. (2020). Evaluation of Radiometric Correction Techniques for Satellite Imagery . Remote Sensing Letters, 11(12), 1174-1183. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote Sensing and Image Interpretation (7th ed.). Wiley. Ma, Z., & Stern, D. I. (2006). Environmental and development economics: Empirical analysis and policy options. The Journal of Economic Perspectives , 20(4), 137-150. https://doi.org/10.1257/jep.20.4.137 Maes, F., Collins, D. L., & Lachapelle, J. (1997). Multimodality image registration by maximization of mutual information . IEEE Transactions on Medical Imaging, 16(2), 187-198. Maintz, J. B. A., & Viergever, M. A. (1998). A survey of medical image registration . Medical Image Analysis, 1(1), 1-36. Miller, K. (2022). Urban Growth and Environmental Management . Sustainable Cities, 10(2), 87-99. Nelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, D. R., ... & Shaw, M. R. (2010). Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment , 7(1), 4-11. https://doi.org/10.1890/080023 Niyogi, D., Pyle, P., Lei, M., Arya, S. P., Kishtawal, C., Shepherd, J. M., ... & Subramanian, S. (2010). Urban modification of thunderstorms: An observational storm climatology and model case study for the Indianapolis urban region. Journal of Applied Meteorology and Climatology , 50(1), 112-123. https://doi.org/10.1175/2010JAMC1836.1 Rahman, M. T. (2023). LULC change analysis and prediction using machine learning models in the coastal region of Bangladesh. Remote Sensing , 14(9), 2041. https://doi.org/10.3390/rs14092041 Rai, R. K. (2015). Land use change and its driving forces in the Eastern Himalaya: A case study of Sikkim. *Journal of Mountain Science*, 12, 594-606. https://doi.org/10.1007/s11629-014-3136-y Rai, R. K., Sarker, M. H., & Islam, A. S. (2017). Monitoring LULC dynamics and predicting future trends using remote sensing and GIS techniques: A case study of Rajshahi district, Bangladesh. Environmental Monitoring and Assessment , 189(10), 479. https://doi.org/10.1007/s10661-017-6171-8 Richards, J. A., & Jia, X. (2006). Remote Sensing Digital Image Analysis: An Introduction (4th ed.). Springer. Rompaey, A. J. J., Govers, G., & Puttemans, C. (2002). Modelling land use changes and their impact on soil erosion and sediment supply to rivers. Earth Surface Processes and Landforms , 27(5), 481-494. https://doi.org/10.1002/esp.335 Shapla, T. A., Rahman, M. R., & Azad, A. K. (2015). Assessment of land use/land cover change and its impact on the ecosystem services of Dhaka city, Bangladesh. International Journal of Research in Geography , 1(2), 22-32. Smith, A. J. (2017). Google Earth Pro for Professional Applications . Springer. Smith, J., & Brown, P. (2018). Land Use Changes and Environmental Impacts in Urban Areas . Environmental Research Letters, 13(5), 110-120. Smith, J., Brown, T., & Zhang, H. (2018). Radiometric and Atmospheric Corrections for Satellite Imagery . International Journal of Remote Sensing, 39(6), 1724-1741. Stark, J. (2000). Histogram Equalization for Image Enhancement . Proceedings of the IEEE, 88(5), 816-824. Trisurat, Y., Alkemade, R., & Verburg, P. H. (2010). Projecting land-use change and its consequences for biodiversity in northern Thailand. Environmental Management , 45(3), 626-639. https://doi.org/10.1007/s00267-010-9439-7 Tso, B., & Mather, P. (2009). Classification Methods for Remotely Sensed Data (2nd ed.). CRC Press. Turner, A. (2018). Advanced GIS and Remote Sensing Techniques . Routledge. World Bank. (2007). Bangladesh: Strategy for Sustained Growth . Washington, DC: World Bank. https://doi.org/10.1596/978-0-8213-7089-6 Zheng, H., Xu, W., Shao, Q., Li, L., Zhang, Z., & Hu, S. (2015). Assessing the impact of climate variability and land use change on the water balance in the upper reaches of the Yangtze River, China. *Hydrology Research*, 46(6), 913-932. https://doi.org/10.2166/nh.2015.052 Zitova, B., & Flusser, J. (2003). Image registration methods: a survey . Image and Vision Computing, 21(11), 977-1000. 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-5315528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375561446,"identity":"1532b081-6a0e-446f-b832-c33baee009e0","order_by":0,"name":"Abdullah al Mahmud","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHiBmA7MYH4C4fKRoYTYAcdlI0cImwQBn4wG67WePbuYpY5Azl8h9Vvk1x06GjYH54aMbeLSYnclLu81zjsHYcka62W3ZbclAh7EZG+fg03Igx+w2bxtD4oYzx9huS25jBmrhYZPGq+X8G7CWepCWYslt9URouQGxJcHgeBsb48dth4nR8sbs5pxzEoYbjrcxSzNuO87DxkzIL+dzgLrKbOQNDgMt+bmt2p6fvfnhY3xaoAAcIwzMPGCSsHIEYPxBiupRMApGwSgYMQAA7ChCeX4vKGYAAAAASUVORK5CYII=","orcid":"","institution":"Water Technology BD Limited","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"al","lastName":"Mahmud","suffix":""},{"id":375561447,"identity":"3d1ee89c-6edd-423a-9ebb-a6b9342ab13d","order_by":1,"name":"Niger Sultana","email":"","orcid":"","institution":"Noakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Niger","middleName":"","lastName":"Sultana","suffix":""},{"id":375561448,"identity":"b57608b9-4dcc-4cbb-a939-e971ae5ed392","order_by":2,"name":"Muhammad Enamul Habib","email":"","orcid":"","institution":"Water Technology BD Limited","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Enamul","lastName":"Habib","suffix":""}],"badges":[],"createdAt":"2024-10-23 04:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5315528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5315528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68719359,"identity":"176e2e74-693b-4319-b9c2-891eda242aee","added_by":"auto","created_at":"2024-11-11 10:34:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1059429,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5315528/v1/0134005142408397a5f99e02.png"},{"id":68719357,"identity":"7e977823-d1e3-48b9-a1b6-0ab5646a6940","added_by":"auto","created_at":"2024-11-11 10:34:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104240,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed of research methodology of this study\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5315528/v1/e38406f3ecb0a46208f1f605.png"},{"id":68720962,"identity":"41fc262b-b555-427d-ac0b-9c085559cba9","added_by":"auto","created_at":"2024-11-11 10:50:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":943459,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Map spanning from 1999 to 2023\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5315528/v1/e1106113007194242c2c911b.png"},{"id":68720764,"identity":"66e9a532-7488-4e44-9a50-c94bcaf19850","added_by":"auto","created_at":"2024-11-11 10:42:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49575,"visible":true,"origin":"","legend":"\u003cp\u003eAverage loss and gain of different LULC in the study area between 1999-2023\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5315528/v1/c46496872d4acfa05c5e1e49.png"},{"id":79845140,"identity":"4d0f1bee-6939-48bb-8939-2ea7d70e3557","added_by":"auto","created_at":"2025-04-03 13:32:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3860039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5315528/v1/3e335c3a-79c4-41c7-b835-e291e9d28e0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eApplication of GIS for Spatio-Temporal Modeling of Land Use Change and Environmental Degradation in Dhaka, Narayanganj, and Gazipur: A 24-Year Study (1999–2023)\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand cover refers to the physical features present on the Earth's surface, while land use describes the functional aspect of these areas, collectively known as Land Use Land Cover (LULC) (Duhamel, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). LULC is a significant driver of global environmental changes, influencing the global climate, ecosystems, and human societies (Ganasri, Raju, \u0026amp; Dwarakish, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Changes in land use are critical factors in assessing global climate change and represent key issues in sustainable development and environmental transformation (Guan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Halmy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These environmental impacts are particularly pronounced in rapidly growing developing nations in Asia, where economies are predominantly agriculture-based (Rai, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Understanding LULC is essential for comprehending the Urban Heat Island (UHI) effect and its implications. In many developing and underdeveloped countries, urbanization often occurs rapidly and without adherence to regulations or sustainable planning principles. As a result, monitoring LULC changes is crucial, as these changes can have irreversible impacts on the environment, particularly in contributing to urban microclimate warming (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dewan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBangladesh is one of the fastest-growing economies in the world, with a GDP of \u003cspan\u003e$\u003c/span\u003e314.656\u0026nbsp;billion and a GDP growth rate of 8.13% for the fiscal year 2018\u0026ndash;2019 (BBS, 2016). Over the past two decades, the country has undergone significant development, particularly in urbanization and industrialization (Ferdous Jannatul, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From 2000 to 2011, Bangladesh's urban growth rate was 4.8%, placing it in the 4th position among Asian countries in terms of urban expansion (World Bank, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The urban population, which was only 6.27\u0026nbsp;million in 1974, surged to over 39\u0026nbsp;million by 2011 (Islam, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This rapid urban population growth, particularly over the last four decades, has raised concerns about food security in Bangladesh due to the decreasing rate of agricultural land per capita. The swift urban expansion has led to the encroachment of built-up areas on other types of land use, increasing pressure on the environment (Dewan \u0026amp; Yamaguchi, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). This land use transformation results in more impermeable surfaces and greater heat storage capacity, which are key contributors to the Urban Heat Island (UHI) effect (Ferdous Jannatul, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The growth of UHI negatively impacts the urban climate, causing a sharp increase in temperature, erratic rainfall patterns, and the degradation of air quality, leading to issues such as floods, waterlogging, health outbreaks, and water scarcity (Alam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dewan \u0026amp; Yamaguchi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e; Hossain, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, studies on Land Use Land Cover (LULC) changes and their impact assessments have a long history, particularly in relation to urbanization, climate change, agriculture, livelihood, and sustainability (Lambin, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ma \u0026amp; Stern, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Numerous studies have explored LULC changes and their irreversible impacts on the environment, such as forest degradation and fragmentation (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), loss of biodiversity (Trisurat, Alkemade, \u0026amp; Verburg, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), ecosystem services (Nelson et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), soil erosion (Islam \u0026amp; Weil, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rompaey, Govers, \u0026amp; Puttemans, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Biro et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), increasing vulnerability to extreme climate events (Dewan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), greenhouse gas emissions (Niyogi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and urban micro-climates (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These environmental impacts are particularly prominent in rapidly growing urban areas, especially in Southeast Asia. Therefore, a detailed understanding of the spatio-temporal dynamics and interactions of environmental variables is crucial to advancing our comprehension of the complex processes unique to specific study areas.\u003c/p\u003e \u003cp\u003eRecent advances in remote sensing technology, along with open-access data policies, have provided valuable resources for efficiently monitoring and investigating LULC change dynamics. The availability of spatio-temporally consistent high-resolution images and the increased temporal acquisition of data (e.g., from a few days to a few weeks), combined with innovative image processing techniques, have significantly enhanced our ability to study these changes. Consequently, challenges in land use management, especially in developing countries, can be more effectively addressed using these techniques. In Bangladesh, the application of remotely sensed data for studying LULC dynamics has grown considerably in recent years (Shapla et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahman, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cp\u003eThe study focuses on three key districts in Bangladesh\u0026mdash;Dhaka, Narayanganj, and Gazipur\u0026mdash;each of which plays a pivotal role in the country\u0026rsquo;s economic growth and industrialization. These districts form the core of Bangladesh\u0026rsquo;s central economic corridor, making them essential subjects for investigating the environmental impacts of rapid urbanization and industrialization.\u003c/p\u003e \u003cp\u003eDhaka, the capital city of Bangladesh, is one of the most densely populated cities in the world, with a population of 18.2\u0026nbsp;million (Bangladesh Bureau of Statistics, 2014). Geographically, Dhaka sits on the Madhupur Terrace, an alluvial plain surrounded by major rivers, including the Buriganga, Turag, Tongi, and Balu. The combination of rapid population growth, industrial expansion, and the city\u0026rsquo;s topographical location exacerbates its vulnerability to environmental degradation, including increased urban heat islands, flooding, and air pollution (Alam \u0026amp; Rabbani, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Dhaka's development trajectory is largely focused on expanding built-up areas at the expense of green spaces and water bodies, leading to substantial environmental challenges.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNarayanganj, located strategically near Dhaka, is an industrial and commercial hub, particularly known for its manufacturing and trade sectors. With an area of 687.76 square kilometers and a population density of approximately 3,000 people per square kilometer, Narayanganj has seen significant land use changes, primarily due to industrial expansion (Banglapedia, 2012). The district\u0026rsquo;s location on the Ganges-Brahmaputra-Meghna alluvial plain makes it prone to waterlogging and flooding, while its industrial activities contribute to high levels of water pollution, particularly in rivers like the Shitalakshya and Meghna. The combination of industrial effluents, unplanned urban growth, and inadequate waste management poses severe environmental risks in the district.\u003c/p\u003e \u003cp\u003eGazipur, the third district, has experienced the most rapid urban and industrial development of the three study areas. Geographically located within the Madhupur Tract, Gazipur covers 176,500 hectares and has distinct topographical features, including high undulating land and red soil. It is one of the largest industrial zones in Bangladesh, with industries like textiles, garments, and brick kilns contributing to the regional economy (BBS, 2013). However, this rapid development has led to significant environmental concerns, including deforestation, industrial pollution, and the discharge of untreated effluents into the Turag River. Gazipur\u0026rsquo;s unique combination of natural and industrial landscapes makes it particularly vulnerable to environmental degradation, with the district witnessing some of the highest rates of LULC changes in Bangladesh.\u003c/p\u003e \u003cp\u003eThese three districts, with their varied geographic and socioeconomic characteristics, present an ideal study area for assessing the environmental impacts of LULC changes. Understanding the dynamics of land use change in these regions is crucial for developing sustainable land management strategies that balance economic growth with environmental preservation.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employs a systematic, GIS-based approach to analyze Land Use Land Cover (LULC) changes across the Dhaka, Narayanganj, and Gazipur districts over the period from 1999 to 2023. The methodology includes (figure-2) remote sensing data collection, image processing, classification, and environmental data correlation. The following steps outline the processes used in this study:\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Remote Sensing Data Collection\u003c/h2\u003e\n \u003cp\u003eLULC and Land Surface Temperature (LST) data were derived from multi-temporal satellite images obtained from the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) through the USGS Earth Explorer platform. The selected satellite images were from the same dry-season months across five distinct time points (1999, 2004, 2009, 2014, and 2023), ensuring consistent phenological characteristics for accurate vegetation classification. All spatial data were aligned to the Universal Transverse Mercator (UTM) coordinate system and resampled to a 30-meter spatial resolution for uniform analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecification of satellite image acquisition\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatellite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcquisition date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath/Row\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Bands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRadiometric resolution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial resolution (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI/TIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI/TIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 bit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 \u003cstrong\u003eSatellite Image Processing\u003c/strong\u003e\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Image Preprocessing\u003c/h2\u003e\n \u003cp\u003ePreprocessing steps were applied to ensure the quality and comparability of satellite images across different time periods. This process included:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRadiometric Correction\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo correct for sensor degradation, atmospheric interference, and changes in solar angle, both absolute and relative radiometric correction methods were employed. This step improved the accuracy of pixel values, ensuring consistency across datasets.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeometric Correction\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGeometric distortions were addressed by aligning satellite images to known geographic coordinates using ground control points and polynomial transformation methods. This ensured the images could be accurately compared over time.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 \u003cstrong\u003eImage Registration and Mosaicking\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eFor each year, image tiles covering the study area were registered and mosaicked to create a continuous, seamless dataset. Image registration utilized both feature-based and intensity-based techniques to align multiple images. Mosaicking involved radiometric normalization to minimize sensor discrepancies, followed by weighted averaging to merge tiles.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 \u003cstrong\u003eImage Clipping\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe mosaicked images were clipped to the boundaries of Dhaka, Narayanganj, and Gazipur districts using administrative boundary shapefiles in ArcGIS 10.8. This step reduced computational requirements and focused the analysis on the study area.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.4 Image Enhancement\u003c/h2\u003e\n \u003cp\u003eTo improve feature visibility and classification accuracy, contrast stretching, histogram equalization, and spatial filtering techniques were applied. These processes enhanced image quality by highlighting relevant land cover features and minimizing noise.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Land Classification\u003c/h2\u003e\n \u003cp\u003eThe LULC classification was conducted using a hybrid approach, combining unsupervised and supervised classification techniques. Initially, unsupervised clustering algorithms (e.g., K-means) grouped pixels based on spectral characteristics. Subsequently, supervised classification was applied using ground-truth data for predefined land cover classes: Water Body, Barren Land, Agricultural Land, Vegetation Land, and Developed Land.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of land classification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDetailed\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAreas covered by wetland, water body like lake, pond, canal, including rivers, water reservoirs, and streams.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eland fallow, sand and earth in-fillings, settled land, diggings sites, open space and bare land and the remaining land cover types\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural lands, crop fields.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrees, natural vegetation, mixed forest, gardens, parks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloped Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand covered with concrete, such as low-density, medium-density, and high-density road networks, homes, businesses, schools, transit, open-roof concrete structures, other man-made structures, and landfills for solid waste.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSupervised Classification\u003c/strong\u003e: A maximum likelihood algorithm was used to classify the images, utilizing training samples derived from field data and high-resolution imagery. The classification process involved identifying spectral signatures for each land cover class.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy Assessment\u003c/strong\u003e: The accuracy of the classification was validated using confusion matrices and ground-truth data, achieving an overall classification accuracy above 85% for all time points.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Change Detection\u003c/h2\u003e\n \u003cp\u003eTo identify LULC transitions over time, a post-classification comparison was performed. This method involved comparing classified images from different years, producing a change matrix that highlighted areas of land cover transition. Image difference was also conducted to quantify the magnitude and direction of change at the pixel level. Both methods provided a detailed understanding of land use dynamics across the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Land Surface Temperature (LST) Analysis\u003c/h2\u003e\n \u003cp\u003eLST was derived from the thermal infrared bands of Landsat imagery. Following standard procedures, digital numbers (DN) were converted into Top of Atmosphere (TOA) spectral radiance. This radiance was then transformed into brightness temperature using calibration constants. Land Surface Emissivity (LSE) was calculated from the Normalized Difference Vegetation Index (NDVI), and finally, LST was estimated using the Stefan-Boltzmann equation. Temporal LST trends were analyzed to assess the impact of LULC changes on surface temperature across the three districts.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed equation of Land surface temperature extraction process.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOA Spectral Radiance (W/ (m\u0026sup2; x sr x \u0026micro;m))\u003c/p\u003e\n \u003cp\u003eL\u003csub\u003e\u0026lambda;\u003c/sub\u003e = ML x Q\u003csub\u003ecal\u003c/sub\u003e + AL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u003csub\u003e\u0026lambda;\u003c/sub\u003e = TOA Spectral Radiance (W/ (m\u003csup\u003e2\u003c/sup\u003e x sr x \u0026micro;m)),\u003c/p\u003e\n \u003cp\u003eML\u0026thinsp;=\u0026thinsp;Radiance Multiplicative Band (No.),\u003c/p\u003e\n \u003cp\u003eAL\u0026thinsp;=\u0026thinsp;Radiance Add Band (No.), and\u003c/p\u003e\n \u003cp\u003eQ\u003csub\u003ecal\u003c/sub\u003e = Quantized and Calibrated Standard Product Pixel Values (DN).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOA Brightness Temperature (\u0026deg;C)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{B}\\text{T}=\\frac{{\\text{k}}_{2}}{\\text{l}\\text{n}(\\frac{{\\text{k}}_{1}}{{\\text{L}}_{{\\lambda\\:}}}+1)}-272.15\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBT\u0026thinsp;=\u0026thinsp;Top of Atmosphere Brightness Temperature (\u0026deg;C),\u003c/p\u003e\n \u003cp\u003eL\u0026lambda;\u0026thinsp;=\u0026thinsp;TOA Spectral (W/ (m\u003csup\u003e2\u003c/sup\u003e x sr x \u0026micro;m)),\u003c/p\u003e\n \u003cp\u003eK1\u0026thinsp;=\u0026thinsp;K1 Constant Band (No.), and\u003c/p\u003e\n \u003cp\u003eK2\u0026thinsp;=\u0026thinsp;K2 Constant Band (No.).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormalized Differential Vegetation Index\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\text{D}\\text{V}\\text{I}=\\frac{\\text{N}\\text{I}\\text{R}-\\text{R}\\text{e}\\text{d}}{\\text{N}\\text{I}\\text{R}+\\text{R}\\text{e}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFor Landsat 5 TM\u003c/p\u003e\n \u003cp\u003eNear-Infrared band (0.76\u0026ndash;0.9 \u0026micro;m),\u003c/p\u003e\n \u003cp\u003eRed band (0.63\u0026ndash;0.69 \u0026micro;m) for.\u003c/p\u003e\n \u003cp\u003eFor Landsat 8 OLI\u003c/p\u003e\n \u003cp\u003eNear-Infrared band (0.85\u0026ndash;0.88 \u0026micro;m),\u003c/p\u003e\n \u003cp\u003eRed band (0.64\u0026ndash;0.67 \u0026micro;m).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of Vegetation\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}\\text{V}=\\left\\{\\frac{\\left(\\text{N}\\text{D}\\text{V}\\text{I}-{\\text{N}\\text{D}\\text{V}\\text{I}}_{\\text{m}\\text{i}\\text{n}}\\right)}{{(\\text{N}\\text{D}\\text{V}\\text{I}}_{\\text{m}\\text{a}\\text{x}}+{\\text{N}\\text{D}\\text{V}\\text{I}}_{\\text{m}\\text{i}\\text{n}})}\\right\\}^2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePV\u0026thinsp;=\u0026thinsp;Proportion of Vegetation,\u003c/p\u003e\n \u003cp\u003eNDVI\u0026thinsp;=\u0026thinsp;DN values from NDVI Image,\u003c/p\u003e\n \u003cp\u003eNDVI min\u0026thinsp;=\u0026thinsp;Minimum DN values from NDVI Image,\u003c/p\u003e\n \u003cp\u003eNDVI max\u0026thinsp;=\u0026thinsp;Maximum DN values from NDVI Image\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand Surface Emissivity\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{T}=\\left(\\frac{\\text{B}\\text{T}}{1}\\right)+\\text{W}\\:\\text{x}\\:\\left(\\frac{\\text{B}\\text{T}}{14380}\\right)\\text{x}\\:\\text{l}\\text{n}\\left(\\text{E}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBT\u0026thinsp;=\u0026thinsp;Top of Atmosphere Brightness Temperature (\u0026deg;C),\u003c/p\u003e\n \u003cp\u003eW\u0026thinsp;=\u0026thinsp;Wavelength of Emitted Radiance, and\u003c/p\u003e\n \u003cp\u003eE\u0026thinsp;=\u0026thinsp;Land Surface Emissivity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Environmental Data Collection and Correlation\u003c/h2\u003e\n \u003cp\u003eSecondary data on key environmental parameters\u0026mdash;pH, COD, BOD, Dissolved Oxygen (DO), and Electrical Conductivity (EC) for water bodies; PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e for air quality; and Temperature, Precipitation, and Humidity for climatic conditions\u0026mdash;were collected from Environmental Impact Assessments (EIAs) and feasibility reports. These parameters were normalized and statistically correlated with LULC changes, providing insights into how land use transitions affected environmental conditions in the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using R Studio (Version 1.1.453). Pearson correlation coefficients were calculated to assess the relationships between LULC changes and environmental variables, with significant correlations highlighted for further interpretation. Spatial statistical tools in ArcGIS were also employed to detect clustering patterns and spatial autocorrelation among environmental variables and LULC categories.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Spatial Distribution of Land Use and Land Cover (LULC) Changes\u003c/h2\u003e\n \u003cp\u003eThe spatial distribution of LULC classifications for Dhaka, Narayanganj, and Gazipur is presented in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e with the corresponding statistical data in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis reveals that barren land had the highest spatial coverage across all three districts in 1999, followed by vegetation and agricultural land. Over the study period (1999\u0026ndash;2023), significant shifts in land cover types were observed.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of land classification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLand area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e426.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e697.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e677.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e636.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eWetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAgriculture Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e556.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e572.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e655.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e664.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e630.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e392.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBuilt Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation Cover\u003c/strong\u003e: The vegetation cover decreased steadily in all districts. In Dhaka, vegetation declined from 426.43 km\u0026sup2; in 1999 to 220.51 km\u0026sup2; in 2023, a loss of approximately 48.3%. Similar declines were noted in Narayanganj (155.69 km\u0026sup2; to 128.81 km\u0026sup2;) and Gazipur (697.64 km\u0026sup2; to 628.97 km\u0026sup2;). This consistent reduction in green cover reflects the intensifying urbanization pressures, particularly the expansion of built-up areas.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWetlands and Barren Land\u003c/strong\u003e: In Dhaka, wetland areas decreased from 108.97 km\u0026sup2; to 96.53 km\u0026sup2;, while barren land shrunk from 1074.42 km\u0026sup2; to 946.98 km\u0026sup2;. Narayanganj and Gazipur also showed declining trends in wetlands and barren lands, reflecting the conversion of natural landscapes to urban developments.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDeveloped Land\u003c/strong\u003e: There was a notable increase in developed land across all districts. In Dhaka, developed land expanded from 75.65 km\u0026sup2; in 1999 to 143.09 km\u0026sup2; in 2023. Similar expansions were observed in Narayanganj (32.98 km\u0026sup2; to 84.88 km\u0026sup2;) and Gazipur (7.20 km\u0026sup2; to 89.92 km\u0026sup2;). The rapid increase in built-up areas indicates significant urban sprawl, particularly in Gazipur, where new developments rose by over 5000%.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of land use/land cover change in the study area\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLand area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1999\u0026ndash;2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2004\u0026ndash;2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2009\u0026ndash;2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2015\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1999\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eWetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-56.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-32.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAgriculture Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBuilt Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDHK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Land Surface Temperature (LST) Analysis\u003c/h2\u003e\n \u003cp\u003eThe LST analysis shows a clear correlation between the increase in built-up areas and rising temperatures across the study area. Higher temperatures (\u0026ge;\u0026thinsp;35\u0026deg;C) were consistently recorded across all phases (1999\u0026ndash;2023) in Dhaka and Narayanganj. Gazipur, which experienced the most rapid urbanization, also saw a sharp rise in LST in the later phases (2015\u0026ndash;2023). Statistical analysis shows a strong positive correlation between built-up areas and LST, with correlation coefficients of \u003cstrong\u003er\u0026thinsp;=\u0026thinsp;0.91\u003c/strong\u003e in all three districts.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDhaka\u003c/strong\u003e: The steady increase in built-up areas resulted in a corresponding rise in LST, creating more intense Urban Heat Islands (UHIs), especially in the city center.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGazipur and Narayanganj\u003c/strong\u003e: Gazipur\u0026rsquo;s late-stage development caused LST to increase rapidly during the last phase (2015\u0026ndash;2023), mirroring the trend observed in Dhaka two decades earlier. Narayanganj followed a similar trajectory, with a more gradual rise in LST.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Correlation of LULC Changes with Environmental Components\u003c/h2\u003e\n \u003cp\u003eThe correlation analysis between LULC changes and environmental parameters highlights significant interactions between urban expansion and environmental degradation. Built-up areas in all three districts exhibited a strong correlation with air quality indicators (PM10, PM2.5) and water quality parameters (BOD, COD, and pH).\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAir Quality\u003c/strong\u003e: PM10 levels showed strong positive correlations with built-up areas, particularly in Narayanganj and Gazipur, where rapid industrialization has led to significant air quality degradation. In Gazipur, correlation coefficients for PM10 and PM2.5 were \u003cstrong\u003er\u0026thinsp;=\u0026thinsp;0.99\u003c/strong\u003e, indicating severe particulate pollution due to urbanization and industrial activities.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWater Quality\u003c/strong\u003e: Narayanganj demonstrated strong correlations between built-up areas and water pollution, particularly with BOD and COD values, with coefficients of \u003cstrong\u003er\u0026thinsp;=\u0026thinsp;0.94\u003c/strong\u003e and \u003cstrong\u003er\u0026thinsp;=\u0026thinsp;0.88\u003c/strong\u003e respectively. Gazipur, with its industrial base, also displayed significant water pollution levels linked to LULC changes.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eTable 6: Correlation matrix of land use/land cover types along with their indices and environmental parameters\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/95224_ce634422aaf2e7a6/95224_custom_files/img1731320748.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003eNote: * \u0026le; 0.05, ** \u0026le; 0.01 and *** \u0026le; 0.001\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Discussion\u003c/h2\u003e\n \u003cp\u003eThe results of this study underscore the profound environmental impacts of rapid LULC changes in the Dhaka, Narayanganj, and Gazipur districts over the last 24 years. The conversion of natural landscapes (vegetation, wetlands, and barren land) to urban developments has accelerated significantly, driven by both population growth and industrial expansion.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Expansion and Its Environmental Consequences\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe most striking finding of this study is the rapid increase in developed land, particularly in Gazipur and Narayanganj, where urban expansion has led to severe environmental degradation. The strong positive correlation between built-up areas and LST illustrates the exacerbation of the Urban Heat Island (UHI) effect in all three districts. As impermeable surfaces such as roads and buildings replace natural vegetation, heat storage capacity increases, leading to higher surface temperatures.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Heat Islands (UHI)\u003c/strong\u003e: The consistent rise in LST across all districts, particularly in Dhaka and Gazipur, is concerning. The increase in surface temperatures can lead to negative health impacts, such as heat stress and respiratory problems, particularly in vulnerable urban populations. This phenomenon is not unique to Bangladesh; similar trends have been observed in rapidly urbanizing cities globally (Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dewan, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAir and Water Pollution\u003c/strong\u003e: The degradation of air quality, particularly the significant rise in PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e levels in Gazipur and Narayanganj, can be attributed to industrial activities and the loss of vegetation cover. Gazipur, which has seen the highest urban growth, now faces severe air pollution challenges. In addition, water pollution indicators such as BOD and COD have worsened due to untreated industrial effluents being discharged into rivers, especially in Narayanganj.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eImplications for Sustainable Development\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe findings highlight the need for urgent action to manage land use and environmental health in these rapidly urbanizing districts. Sustainable urban planning, incorporating green infrastructure and stricter pollution controls, is critical for mitigating the negative effects of LULC changes.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGreen Infrastructure\u003c/strong\u003e: The restoration of green spaces and the integration of green roofs, parks, and urban forests can mitigate the UHI effect and improve air quality. Urban planners must prioritize green infrastructure to reduce the environmental impact of future developments.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e: The significant correlations between LULC changes and environmental parameters point to the need for comprehensive land-use policies that balance economic development with environmental sustainability. Regulatory frameworks should be strengthened to enforce pollution controls, particularly in industrial zones like Gazipur and Narayanganj, where environmental degradation is most acute.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eComparative Perspectives\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCompared to other rapidly urbanizing cities in Southeast Asia, the environmental degradation observed in Dhaka, Narayanganj, and Gazipur aligns with global trends. Similar studies in India and China have documented the adverse effects of rapid LULC changes on environmental quality, including temperature increases, air pollution, and water contamination (Ganasri et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rahman, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the unique geographic and climatic conditions of Bangladesh exacerbate these impacts, particularly in flood-prone areas like Dhaka.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive GIS-based spatio-temporal analysis of Land Use Land Cover (LULC) changes and their environmental impacts in Dhaka, Narayanganj, and Gazipur over a 24-year period (1999\u0026ndash;2023). The results demonstrate a significant reduction in vegetation, wetlands, and barren land, with a sharp increase in built-up and newly developed areas. Gazipur experienced the most rapid urbanization, followed by Narayanganj and Dhaka, illustrating the uneven distribution of development across the districts. The rise in Land Surface Temperature (LST) is strongly correlated with the expansion of built-up areas, leading to an intensification of the Urban Heat Island (UHI) effect. Additionally, the study highlights the significant degradation of air and water quality, particularly in areas experiencing the highest urban growth.\u003c/p\u003e \u003cp\u003eThe findings underscore the environmental consequences of rapid and unplanned urbanization, which is particularly evident in Gazipur and Narayanganj, where industrial expansion has led to significant air and water pollution. The strong correlations between LULC changes and environmental parameters such as PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, BOD, and COD reflect the urgent need for sustainable urban planning and stricter environmental regulations. Without intervention, the continued expansion of impervious surfaces and reduction of green spaces will exacerbate climate vulnerabilities, including rising temperatures, air pollution, and environmental health risks.\u003c/p\u003e \u003cp\u003eBy providing critical insights into the spatio-temporal dynamics of LULC changes in these economically important regions, this study offers a baseline for future urban planning and environmental management efforts in Bangladesh. Policymakers and urban planners must prioritize sustainable land use practices, integrating green infrastructure and environmental safeguards to mitigate the adverse effects of urbanization.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendations for Future Research\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough this study provides a comprehensive analysis of Land Use and Land Cover (LULC) changes and their environmental impacts in Dhaka, Narayanganj, and Gazipur from 1999 to 2023, several areas remain unexplored, warranting further research. One of the primary areas for future investigation is the application of predictive modeling techniques to forecast LULC dynamics. Machine learning algorithms, such as cellular automata models, could be employed to predict future land cover transitions based on historical data, offering urban planners\u0026rsquo; valuable tools to anticipate the environmental effects of continued urban growth in these regions.\u003c/p\u003e \u003cp\u003eAdditionally, the significant correlations found between air pollution (PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e) and LULC changes suggest the need for a more detailed public health assessment. Future research should focus on quantifying the health risks associated with increased particulate matter and heat stress due to urbanization. Such studies would provide critical data for formulating public health strategies aimed at mitigating the impacts of air quality degradation and rising temperatures in densely populated urban centers.\u003c/p\u003e \u003cp\u003eMoreover, Dhaka\u0026rsquo;s vulnerability to flooding due to its low-lying geographic position and increasing impermeable surfaces calls for a deeper investigation into the hydrological effects of urbanization. Hydrological modeling could be integrated to assess how LULC changes have influenced flood risks over time, offering insights into effective flood management and climate adaptation strategies. This would be particularly beneficial in helping policymakers design infrastructure that can withstand the dual pressures of urban expansion and climate change.\u003c/p\u003e \u003cp\u003eExtending the temporal scope of LULC studies beyond 2023 is another critical area for future research. By incorporating long-term climate projections, future studies could explore how urbanization and climate change may interact to exacerbate environmental vulnerabilities. Understanding the long-term implications of LULC changes on regional climate patterns and ecosystems will provide valuable foresight for planning more resilient urban environments.\u003c/p\u003e \u003cp\u003eFinally, future work should also examine the effectiveness of current land use policies and environmental regulations in managing urban growth and mitigating its negative environmental effects. An analysis of governance frameworks, policy implementation, and regulatory compliance would highlight areas where improvements are needed to ensure more sustainable and environmentally friendly urban development in Bangladesh. By addressing these research gaps, future studies will not only contribute to academic knowledge but also inform practical solutions for balancing economic growth with environmental conservation in rapidly urbanizing regions like Dhaka, Narayanganj, and Gazipur.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAbdullah al Mahmud (Corresponding Author) was responsible for the conceptualization, data collection, analysis, methodology development, and writing of the entire manuscript. Niger Sultana contributed by reviewing the work and assisting with writing and refining the manuscript. Muhammad Enamul Habib provided administrative support throughout the research process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdullah, S., Raspati, G. S., \u0026amp; Ahmad, N. (2019). Forest degradation and fragmentation in Southeast Asia: Implications for biodiversity and carbon emissions. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, 440, 1-9. https://doi.org/10.1016/j.foreco.2019.02.003\u003c/li\u003e\n \u003cli\u003eAhmed, B. (2016). The history of Bhawal Estate and its impact on Gazipur\u0026apos;s urbanization. \u003cem\u003eGazipur Studies Journal\u003c/em\u003e, 7(2), 45-58.\u003c/li\u003e\n \u003cli\u003eAhmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. S., \u0026amp; Choi, K. (2013).\u0026nbsp;Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. *Remote Sensing*, 5(11), 5969-5998. https://doi.org/10.3390/rs5115969\u003c/li\u003e\n \u003cli\u003eAlam, M. S., Hu, Y., \u0026amp; Rasul, G. (2017). Ecosystem services and livelihood in Bangladesh: A nexus thinking approach for sustainable development. \u003cem\u003eEnvironmental Development\u003c/em\u003e, 21, 33-43. https://doi.org/10.1016/j.envdev.2016.12.002\u003c/li\u003e\n \u003cli\u003eAlam, M., \u0026amp; Rabbani, M. G. (2007). Vulnerabilities and responses to climate change for Dhaka. \u003cem\u003eEnvironment and Urbanization\u003c/em\u003e, 19(1), 81-97. https://doi.org/10.1177/0956247807076911\u003c/li\u003e\n \u003cli\u003eAlam, M., Golam, R., Rahman, M., \u0026amp; Hossain, M. (2017). Effects of urbanization on urban heat island in Dhaka city, Bangladesh. \u003cem\u003eJournal of Urban and Environmental Engineering\u003c/em\u003e, 11(1), 31-40. https://doi.org/10.4090/juee.2017.v11n1.031040\u003c/li\u003e\n \u003cli\u003eBangladesh Bureau of Statistics (BBS). (2013). \u003cem\u003eStatistical Yearbook of Bangladesh 2012\u003c/em\u003e. Dhaka, Bangladesh: Ministry of Planning.\u003c/li\u003e\n \u003cli\u003eBangladesh Bureau of Statistics (BBS). (2014). \u003cem\u003eDhaka Metropolitan Area Statistics 2014\u003c/em\u003e. Dhaka, Bangladesh: Ministry of Planning.\u003c/li\u003e\n \u003cli\u003eBangladesh Bureau of Statistics (BBS). (2016). \u003cem\u003eStatistical Yearbook of Bangladesh 2015\u003c/em\u003e. Dhaka, Bangladesh: Ministry of Planning.\u003c/li\u003e\n \u003cli\u003eBiro, K., Pradhan, B., Buchroithner, M., \u0026amp; Makeschin, F. (2013). Land use/land cover change analysis and its impact on soil erosion in the Phewa Lake watershed, Nepal. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 185(11), 7781-7797. https://doi.org/10.1007/s10661-013-3121-1\u003c/li\u003e\n \u003cli\u003eBrammer, H. (1996). \u003cem\u003eThe geography of the soils of Bangladesh\u003c/em\u003e. University Press Limited.\u003c/li\u003e\n \u003cli\u003eBrown, G. (2016). \u003cem\u003eGeographic Information Systems and Science\u003c/em\u003e. Wiley.\u003c/li\u003e\n \u003cli\u003eBrown, L., \u0026amp; Jones, R. (2020). \u003cem\u003eUrban Heat Island and Vegetation Loss in Metropolitan Areas\u003c/em\u003e. Journal of Environmental Studies, 45(3), 123-135.\u003c/li\u003e\n \u003cli\u003eBrown, L., \u0026amp; Lowe, D. (2007). \u003cem\u003eInvariant descriptors for visual recognition and image matching\u003c/em\u003e. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 781-794.\u003c/li\u003e\n \u003cli\u003eDewan, A. M. (2015). \u003cem\u003eFloods in a megacity: Geospatial techniques in assessing hazards, risk, and vulnerability\u003c/em\u003e. Springer. https://doi.org/10.1007/978-94-017-9234-4\u003c/li\u003e\n \u003cli\u003eDewan, A. M., \u0026amp; Yamaguchi, Y. (2009a). Land use and land cover change in greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. \u003cem\u003eApplied Geography\u003c/em\u003e, 29(3), 390-401. https://doi.org/10.1016/j.apgeog.2008.12.005\u003c/li\u003e\n \u003cli\u003eDewan, A. M., \u0026amp; Yamaguchi, Y. (2009b). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960\u0026ndash;2005. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 150(1-4), 237-249. https://doi.org/10.1007/s10661-008-0226-5\u003c/li\u003e\n \u003cli\u003eDhaka Population. (2023). Dhaka population data. \u003cem\u003eWorld Population Review\u003c/em\u003e. Retrieved from https://worldpopulationreview.com/world-cities/dhaka-population\u003c/li\u003e\n \u003cli\u003eDoe, J., \u0026amp; Lee, P. (2017). \u003cem\u003eThe Impact of Urbanization on Local Climate: A Case Study\u003c/em\u003e. Urban Environment, 22(1), 56-69.\u003c/li\u003e\n \u003cli\u003eDuhamel, C. (2012). *Land use and land cover: Concepts, methods, and applications*. Nova Science Publishers.\u003c/li\u003e\n \u003cli\u003eFerdous Jannatul. (2023). \u003cem\u003eUrbanization and its impact on Urban Heat Island (UHI) in Dhaka, Bangladesh\u003c/em\u003e. Unpublished master\u0026apos;s thesis, University of Dhaka.\u003c/li\u003e\n \u003cli\u003eFoody, G. M. (2002). \u003cem\u003eStatus of land cover classification accuracy assessment\u003c/em\u003e. Remote Sensing of Environment, 80(1), 185-201.\u003c/li\u003e\n \u003cli\u003eGanasri, B. P., Raju, R., \u0026amp; Dwarakish, G. S. (2013). Different approaches for land use land cover change detection: A review. *Research Journal of Applied Sciences, Engineering and Technology*, 7(14), 3034-3039. https://doi.org/10.19026/rjaset.7.648\u003c/li\u003e\n \u003cli\u003eGarcia, M., White, T., \u0026amp; Johnson, A. (2021). \u003cem\u003eCorrelation between Built-up Areas and Air Quality\u003c/em\u003e. Environmental Pollution, 67(2), 234-245.\u003c/li\u003e\n \u003cli\u003eGonzalez, R. C., \u0026amp; Woods, R. E. (2008). \u003cem\u003eDigital Image Processing\u003c/em\u003e (3rd ed.). Prentice Hall.\u003c/li\u003e\n \u003cli\u003eGreen, D., \u0026amp; Black, S. (2015). \u003cem\u003eSustainable Urban Planning in Rapidly Growing Cities\u003c/em\u003e. City Planning Journal, 37(4), 451-467.\u003c/li\u003e\n \u003cli\u003eGuan, D., Li, H., Inohae, T., Su, W., Nagaie, T., \u0026amp; Hokao, K. (2011). Modeling urban land use changes by the integration of cellular automaton and Markov model. *Ecological Modelling*, 222(20-22), 3761-3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009\u003c/li\u003e\n \u003cli\u003eHall, D. K., Riggs, G. A., \u0026amp; Salomonson, V. V. (1991). \u003cem\u003eMODIS snow cover products\u003c/em\u003e. International Journal of Remote Sensing, 12(3), 355-377.\u003c/li\u003e\n \u003cli\u003eHalmy, M. W. A., Gessler, P. E., Hicke, J. A., \u0026amp; Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. *Applied Geography*, 63, 101-112. https://doi.org/10.1016/j.apgeog.2015.06.015\u003c/li\u003e\n \u003cli\u003eHossain, M. (2008). \u003cem\u003eUrban environmental health in Bangladesh\u003c/em\u003e. The Daily Star, Dhaka. Retrieved from https://www.thedailystar.net\u003c/li\u003e\n \u003cli\u003eHu, Y. (2014). Evaluating ecosystem services in the tropics: Case studies from Costa Rica and Southeast Asia. \u003cem\u003eEcosystem Services\u003c/em\u003e, 9, 21-29. https://doi.org/10.1016/j.ecoser.2014.04.003\u003c/li\u003e\n \u003cli\u003eIslam, K. R., \u0026amp; Weil, R. R. (2000). Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, 79(1), 9-16. https://doi.org/10.1016/S0167-8809(99)00145-0\u003c/li\u003e\n \u003cli\u003eIslam, N. (2023). Urbanization and urban growth in Bangladesh: Understanding patterns, trends, and drivers. \u003cem\u003eJournal of Urban Planning and Development\u003c/em\u003e, 148(2), 04022017. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000838\u003c/li\u003e\n \u003cli\u003eJensen, J. R. (2005). \u003cem\u003eIntroductory Digital Image Processing: A Remote Sensing Perspective\u003c/em\u003e (3rd ed.). Prentice Hall.\u003c/li\u003e\n \u003cli\u003eJensen, J. R. (2016). \u003cem\u003eIntroductory Digital Image Processing: A Remote Sensing Perspective\u003c/em\u003e (3rd ed.). Prentice Hall.\u003c/li\u003e\n \u003cli\u003eJohnson, A., \u0026amp; White, T. (2019). \u003cem\u003eThe Role of Vegetation in Mitigating Urban Heat Islands\u003c/em\u003e. Climate Change and Environment, 29(1), 78-92.\u003c/li\u003e\n \u003cli\u003eLambin, E. F. (2001). Predicting the impact of land-use and land-cover changes on local and regional biodiversity. \u003cem\u003eBioscience\u003c/em\u003e, 51(10), 805-812. https://doi.org/10.1641/0006-3568(2001)051 [0805: PTIOLU]2.0.CO;2\u003c/li\u003e\n \u003cli\u003eLi, X., Chen, J., \u0026amp; Zhang, Y. (2020). \u003cem\u003eEvaluation of Radiometric Correction Techniques for Satellite Imagery\u003c/em\u003e. Remote Sensing Letters, 11(12), 1174-1183.\u003c/li\u003e\n \u003cli\u003eLillesand, T., Kiefer, R. W., \u0026amp; Chipman, J. (2014). \u003cem\u003eRemote Sensing and Image Interpretation\u003c/em\u003e (7th ed.). Wiley.\u003c/li\u003e\n \u003cli\u003eMa, Z., \u0026amp; Stern, D. I. (2006). Environmental and development economics: Empirical analysis and policy options. \u003cem\u003eThe Journal of Economic Perspectives\u003c/em\u003e, 20(4), 137-150. https://doi.org/10.1257/jep.20.4.137\u003c/li\u003e\n \u003cli\u003eMaes, F., Collins, D. L., \u0026amp; Lachapelle, J. (1997). \u003cem\u003eMultimodality image registration by maximization of mutual information\u003c/em\u003e. IEEE Transactions on Medical Imaging, 16(2), 187-198.\u003c/li\u003e\n \u003cli\u003eMaintz, J. B. A., \u0026amp; Viergever, M. A. (1998). \u003cem\u003eA survey of medical image registration\u003c/em\u003e. Medical Image Analysis, 1(1), 1-36.\u003c/li\u003e\n \u003cli\u003eMiller, K. (2022). \u003cem\u003eUrban Growth and Environmental Management\u003c/em\u003e. Sustainable Cities, 10(2), 87-99.\u003c/li\u003e\n \u003cli\u003eNelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, D. R., ... \u0026amp; Shaw, M. R. (2010). Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. \u003cem\u003eFrontiers in Ecology and the Environment\u003c/em\u003e, 7(1), 4-11. https://doi.org/10.1890/080023\u003c/li\u003e\n \u003cli\u003eNiyogi, D., Pyle, P., Lei, M., Arya, S. P., Kishtawal, C., Shepherd, J. M., ... \u0026amp; Subramanian, S. (2010). Urban modification of thunderstorms: An observational storm climatology and model case study for the Indianapolis urban region. \u003cem\u003eJournal of Applied Meteorology and Climatology\u003c/em\u003e, 50(1), 112-123. https://doi.org/10.1175/2010JAMC1836.1\u003c/li\u003e\n \u003cli\u003eRahman, M. T. (2023). LULC change analysis and prediction using machine learning models in the coastal region of Bangladesh. \u003cem\u003eRemote Sensing\u003c/em\u003e, 14(9), 2041. https://doi.org/10.3390/rs14092041\u003c/li\u003e\n \u003cli\u003eRai, R. K. (2015). Land use change and its driving forces in the Eastern Himalaya: A case study of Sikkim. *Journal of Mountain Science*, 12, 594-606. https://doi.org/10.1007/s11629-014-3136-y\u003c/li\u003e\n \u003cli\u003eRai, R. K., Sarker, M. H., \u0026amp; Islam, A. S. (2017). Monitoring LULC dynamics and predicting future trends using remote sensing and GIS techniques: A case study of Rajshahi district, Bangladesh. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 189(10), 479. https://doi.org/10.1007/s10661-017-6171-8\u003c/li\u003e\n \u003cli\u003eRichards, J. A., \u0026amp; Jia, X. (2006). \u003cem\u003eRemote Sensing Digital Image Analysis: An Introduction\u003c/em\u003e (4th ed.). Springer.\u003c/li\u003e\n \u003cli\u003eRompaey, A. J. J., Govers, G., \u0026amp; Puttemans, C. (2002). Modelling land use changes and their impact on soil erosion and sediment supply to rivers. \u003cem\u003eEarth Surface Processes and Landforms\u003c/em\u003e, 27(5), 481-494. https://doi.org/10.1002/esp.335\u003c/li\u003e\n \u003cli\u003eShapla, T. A., Rahman, M. R., \u0026amp; Azad, A. K. (2015). Assessment of land use/land cover change and its impact on the ecosystem services of Dhaka city, Bangladesh. \u003cem\u003eInternational Journal of Research in Geography\u003c/em\u003e, 1(2), 22-32.\u003c/li\u003e\n \u003cli\u003eSmith, A. J. (2017). \u003cem\u003eGoogle Earth Pro for Professional Applications\u003c/em\u003e. Springer.\u003c/li\u003e\n \u003cli\u003eSmith, J., \u0026amp; Brown, P. (2018). \u003cem\u003eLand Use Changes and Environmental Impacts in Urban Areas\u003c/em\u003e. Environmental Research Letters, 13(5), 110-120.\u003c/li\u003e\n \u003cli\u003eSmith, J., Brown, T., \u0026amp; Zhang, H. (2018). \u003cem\u003eRadiometric and Atmospheric Corrections for Satellite Imagery\u003c/em\u003e. International Journal of Remote Sensing, 39(6), 1724-1741.\u003c/li\u003e\n \u003cli\u003eStark, J. (2000). \u003cem\u003eHistogram Equalization for Image Enhancement\u003c/em\u003e. Proceedings of the IEEE, 88(5), 816-824.\u003c/li\u003e\n \u003cli\u003eTrisurat, Y., Alkemade, R., \u0026amp; Verburg, P. H. (2010). Projecting land-use change and its consequences for biodiversity in northern Thailand. \u003cem\u003eEnvironmental Management\u003c/em\u003e, 45(3), 626-639. https://doi.org/10.1007/s00267-010-9439-7\u003c/li\u003e\n \u003cli\u003eTso, B., \u0026amp; Mather, P. (2009). \u003cem\u003eClassification Methods for Remotely Sensed Data\u003c/em\u003e (2nd ed.). CRC Press.\u003c/li\u003e\n \u003cli\u003eTurner, A. (2018). \u003cem\u003eAdvanced GIS and Remote Sensing Techniques\u003c/em\u003e. Routledge.\u003c/li\u003e\n \u003cli\u003eWorld Bank. (2007). \u003cem\u003eBangladesh: Strategy for Sustained Growth\u003c/em\u003e. Washington, DC: World Bank. https://doi.org/10.1596/978-0-8213-7089-6\u003c/li\u003e\n \u003cli\u003eZheng, H., Xu, W., Shao, Q., Li, L., Zhang, Z., \u0026amp; Hu, S. (2015). Assessing the impact of climate variability and land use change on the water balance in the upper reaches of the Yangtze River, China. *Hydrology Research*, 46(6), 913-932. https://doi.org/10.2166/nh.2015.052\u003c/li\u003e\n \u003cli\u003eZitova, B., \u0026amp; Flusser, J. (2003). \u003cem\u003eImage registration methods: a survey\u003c/em\u003e. Image and Vision Computing, 21(11), 977-1000.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land Use, Land Cover, LST, Environmental Impact, Dhaka, Narayanganj, Gazipur","lastPublishedDoi":"10.21203/rs.3.rs-5315528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5315528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid transformation of land use and land cover (LULC) in Bangladesh, particularly in the central economic hubs of Dhaka, Narayanganj, and Gazipur, poses critical challenges for environmental sustainability. This study examines LULC changes between 1999 and 2023, revealing significant increases in built-up and newly developed areas, while natural environments, including vegetation, wetlands, and barren land, have diminished. Gazipur experienced the most dramatic increase in development (5162.82% for new developments and 1148.89% for built-up areas), followed by Narayanganj and Dhaka. Land Surface Temperature (LST) consistently increased across all districts, with strong correlations between built-up areas and LST (r = 0.91). Additionally, LULC changes were linked to environmental variables, including air quality (PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e) and water quality (pH, BOD, COD), with Gazipur showing the highest number of significant correlations. The findings underscore the environmental impact of rapid urbanization and provide critical data for land management and policy interventions to mitigate further environmental degradation. These results can serve as a baseline for future studies on the environmental vulnerability caused by LULC changes in rapidly urbanizing regions.\u003c/p\u003e","manuscriptTitle":"Application of GIS for Spatio-Temporal Modeling of Land Use Change and Environmental Degradation in Dhaka, Narayanganj, and Gazipur: A 24-Year Study (1999–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 10:34:06","doi":"10.21203/rs.3.rs-5315528/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":"4fb07f4b-21c2-4805-ba25-1e5426fbb493","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-03T13:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 10:34:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5315528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5315528","identity":"rs-5315528","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}