An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms

An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms | 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 Article An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms Nadhir Al-Ansari, Hayder Dibs, Ahmed Al-Janabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4148120/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 In recent years, the city of AL-Hilla in Babylon, Iraq has suffered from the illegal fragmentation of agricultural and orchard lands, leading to their conversion into residential areas. This transformation has had a negative impact on the economic viability of plantation and vegetation lands, affecting the climate and causing an increase in temperatures, winds, and dust storms. This study aims to examine the spatio-temporal dynamics of changes in land-use/land-cover (LU/LC) using different spatial resolutions of satellite images to detect urban sprawl. The present study utilizes a supervised imagery classifier, employing the Mahalanobis distance (MD) technique to produce three distinct LU/LC maps for 2002, 2011, and 2022. The accuracy of the outcomes is assessed using a confusion matrix, and a comparison was made to compute the changes in land categories. The research reveals that the expansion of the urban region in AL-Hilla has significantly increased from 33.40 km² in 2002 to 89.16 km² in 2022, with an Annual Growth Rate of (6.74%) between 2002 and 2011 and 6.14% between 2011 and 2022. The growth in urban area now constitutes 38.45% of the entire city area and has resulted in a decline in other land categories such as water bodies, soil, and vegetation. The study highlights the necessity for effective management and planning strategies to address the adverse impact of urban expansion on the environment and agriculture Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Urban area Geospatial dataset Change Detecting QuickBird-Imagery WorldView-Image Sentinel-Satellite Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Urbanization holds paramount importance in spatiotemporal analysis and exerts significant influence on various levels, including social, human life, economic, animal habitats, and environmental aspects. Urban sprawl, a complex and multifaceted spatiotemporal process, encompasses diverse forms of urban expansion [1-4]. In urban areas, sprawl arises from a combination of anthropogenic and natural factors [5]. Anthropogenic elements like population growth, economic development, and urbanization expansion serve as primary drivers of urban sprawl. However, natural factors such as topography and soil properties also contribute, exhibiting spatial heterogeneity [6-7]. The detrimental effects of urban sprawl on vegetation lands are well-documented [8]. The expansion of urban areas can induce various changes, including landscape transformation, desertification, conversion, temperature elevation, land degradation, and fragmentation of agricultural lands [9-11]. Currently, AL-Hilla, Iraq faces numerous challenges due to extensive conversion and fragmentation of vegetation and plantation lands, including orchards, into urban spaces. This has resulted in desertification, climate shifts, high temperatures, and persistent dust storms, particularly during the summer season [11]. Numerous studies have underscored the consequences of landscape conversion and transformation in urban region sprawl [12-14]. Urban expansion poses critical environmental issues with negative impacts on the sustainability of agricultural lands [13]. Moreover, urban growth leads to landscape conversion and transformations occurring in diverse and multi-directional manners [15-16]. Hence, effective management and planning strategies are imperative to mitigate the adverse effects of urban growth on the environment and agriculture in AL-Hilla city. According to previous research [17-19], urban sprawl is a complex process influenced by various factors, including land topography, city demographics, population, and economy. This process often involves the conversion of natural lands, such as rocks, soil, and vegetation, into manmade areas, including asphalt, concrete, and metals. Consequently, the surrounding lands and human resources may be positively or negatively impacted by urban sprawl, affecting human life quality. To address this issue, remote sensing and GIS technologies have become indispensable tools for detecting, monitoring, and managing urban growth [1, 20-30]. Specifically, satellite datasets and temporal remote sensing data offer comprehensive views and repetitive coverage of urban areas, enabling the establishment of databases for monitoring changes in urban growth. In this study, the Mahalanobis approach was employed for image classification of multispectral and temporal satellite images (QuickBird-2, WorldView-2, and Sentinel-2) to detect and quantify the rate of urban area expansion from 2002 to 2022 in AL-Hilla city, Babylon, Iraq. The findings of this study hold significance for decision-makers in city management and planning to estimate the social, economic, and environmental impacts of urban growth. It is noteworthy that the population of AL-Hilla has experienced a substantial increase since 2000, with a threefold growth recorded between 2003 and 2022 [31], resulting in alterations in land use and spatial organization. 2. Materials and Methods This research employed a multi-sensor approach using QuickBird, WorldView, and Sentinel Satellite-Images to evaluate the efficiency of detecting changes in LU/LC and monitoring expanded urbanization areas in AL-Hillah. The study consisted of various phases, including image pre-processing, study area selection, satellite image resampling, and ground control point (GCP) collection, training and testing samples selection, and the adoption of the MD as a supervised image classifier to create LU/LC thematic maps. The outputs accuracies were verified using the confusion matrix approach. The research also involved analyzing the changes in land categories using a statistical comparison of the classified images between 2002 and 2022, and computing the annual urban growth rate and its percentage rate. Figure 1 portrays the methodology of this study, as presented by Aljuboori et al. (2021). By utilizing multi-sensor datasets with different resolutions, this study offers insights into the potential of integrating satellite images to detect urbanization and land cover changes. Figure 1, indicates the methodology. 2.1 Study Area Description The research focuses on the central part of AL-Hill city, Babylon-Iraq, with coordinates of 32°32'07.93"N - 32°23'31.52"N latitude and 44°22'50.30"E- 44°29'42.41"E longitude. The study area covers an elevation of approximately 112 feet (34 meters) above the mean sea level and is situated about 100 km south of capital city of Iraq (Baghdad). AL-Hilla is geographically in central Iraq, and it is bordered with other governorates, including Baghdad, Karbala, Anbar, Najaf, Wassit, and AL-Qadissiya. The Euphrates-river runs through Al-Hilla, and it is second greatest and oldest rivers in Iraq. It has two branches, the first one is called the AL-Hindiyah river, and the second one is called AL-Hilla river. As per the 2018 Census, The study area has a population around of 970,000 inhabitants. (see Fig. 2 for a detailed illustration of the study area) [31]. Figure 2, Location in research area. Agriculture plays a crucial role in the economy of AL-Hilla, covering thousands of square kilometers and providing a wide variety of fruits, vegetation, and plantations to the local markets. The city also boasts numerous archaeological sites, including the ancient city of Babylon, which draw tourists and scholars to the area. Additionally, the presence of several private and government universities in the vicinity enhances the city's economic prospects. AL-Hilla experiences a dry climate, with temperatures during the summer months sometimes exceeding 50°C, while the average temperatures range from 14-24°C. Rainfall is scarce, occurring from November to April in the Winter-Season, with an Annual-Precipitation of 4.49 inches [32]. 3. Images Remove Noises In order to track changes in LU/LC as well as Urbanization expansion and growth in the research area, each of QuickBird-2, WorldView-2, and Sentinel-2 satellite images adopted. These images have highly Spatial, Spectral, and Spectral resolutions, and that made them suitable for this purpose. The images used were from 2002, 2011, and 2022 and were free of cloud cover. The Sentinel-2 imagery was obtained from the website of the European Space Agency (ESA). Table 1. Illustrates specifications of the applied images. Table 1. Datasets-Specifications. Various techniques employed to prepare the images for the subsequent processes aimed to detect and monitor the changes in LU/LC, urban development and growth in the study area. These techniques involved geometric and radiometric corrections, as well as image sub-setting to get the research area. After that, the images were subjected to geometric and radiometric corrections. Images-Ggeometric-Correction was accomplished by gathering 14 Ground Control Points (GCPs) in the field of the research area, which were then utilized to correct the image of QuickBird-2 satellite geometrically. Then, QuickBird-2 imagery was used as the reference (master) scene for the 2011 and 2022 images. The locations of the collected GCPs are shown in Fig. 3 and Table 2. Geometric rectification is a crucial step in producing a corrected map that identifies different classes change, urbanization regions expansion over time. The WordView-2 (2011) and Sentinel-2 (2022) satellite images were already corrected, and geo-referenced regarding the Universal Transverse Mercator map projection in Zone 38N, with the datum (WGS84-Ellipsoid). The Images served as slave scenes and were registered to the QuickBird-2 master scene using the image-to-image approach, which employed a first-degree polynomial equation for images transformation. The image rectification process yielded an excellent root-mean-square-error less than (0.5) m. Resampling performed using the Nearest-Neighbor approach, while Histogram-Matching was used to enhance the visual appearance and brightness of the output image. These techniques are commonly used to improve the quality of remote sensing data and have been reported in the literature [32, 33]. Table 2. the selected GCPs Figure 3 The Research area GCPs. 4. Image Processing and Classification In order to investigate the changes of sprawl in urban regions in research study from (2002 to 2022), the researchers chose specific bands from the employed satellite images. The selected bands were those that provided good visual information for urban areas, specifically bands 1, 3, and 4 form images of (QuickBird and Worldview), and bands 2, 4, and 7 for Sentinel-2. Color combinations were then created for each image to aid in analysis. For the conducting of classification, nine bands were used from the three data, excluding the thermal data. The MD method utilized for image classification, and the software used for LU/LC estimation was Envi. The researchers chose five categories for classification: Roads, Vegetation, Water Bodies, Soil region, and Urban region , which are further detailed in Table 3. region Table 3. the considered classes, and their definitions in this study. The classification accuracy of LU/LC is often hampered by spectral confusion, particularly when the classes share similar spectral responses. This is mainly due to the limitations of the classification methods used [34-36]. To overcome this limitation, the current study employed visual interpretation and on-screen digitizing. Two types of spectral confusion were identified, namely, (a) the Urban Region/Soil Region classes, (b) the Vegetation Region/Soils classes. These confusions were resolved by examining each pixel in the classified images and manually correcting any misclassifications. By adopting these methods, the study was able to produce accurate LU/LC maps of the study area. [34-36] were consulted for this study. 5. Results and Discussions Figures 4(a, b and c) shows the employed image for the years (2002, 2011, & 2022). Prior to analysis, the images underwent three processing steps: (1) subsetting to extract the study region, (2) remove the geometric and radiometric noises, and (3) Rectification of images using the image-to-image approach between the (QuickBird-2) and (WorldView-2 and Sentinel-2) [1]. The correction of geometric and radiometric errors ensured that the images were aligned and had consistent radiometry, which is crucial for accurate analysis of land use and land cover changes over time Figure 4 . Illustrates; (A); The QuickBird-2 satellite imagery of 2002, (B) The WorldView-2 satellite imagery of 2011. (C) The Sentinel-2 satellite imagery of 2022 of the study area. 5.1 Urban Sprawl Monitoring Changes The results of the satellite image classification for the years (2002, 2011, and 2022) using (QuickBird-2, WorldView-2, and Sentinel-2), are presented in Fig. 5 (a, b and c). The Mahalanobis classifier was employed for the classification process, and training and testing sites were collected through a random sampling technique from utilized images. The training sites utilized to classify the images for the periods of 2002-2011 and 2011-2022. For evaluate outputs, testing sites checked in situ and through visual interpretation of images acquired during fieldwork in July 2022. The overall accuracy of the results was found to be (85.94%, 89.82%, and 93.67% for (2002), (201)1, and (2022) images. The accuracy of the classified maps exceeded 85%, indicating the image processing methods used in this research are suitable for the analysis. Figures 5. in 5A, 5B, and 5C illustrate the classified thematic maps of AL-Hilla from the QuickBird-2 satellite image in 2002, WorldView-2 satellite image in 2011, and Sentinel-2 satellite image in 2022, respectively. The study findings provide the kappa coefficients for the three satellite systems examined. According to the results, the QuickBird-2 images captured in 2002, WorldView-2 images taken in 2011, and Sentinel-2 images acquired in 2022 demonstrate kappa coefficients of about 0.84, 0.86, and 0.90, respectively. Figs. 6 and 7 summarize the overall-accuracies, and kappa-coefficients of the classified images utilizing the MD classifier for the three images. Figures 6 , indicates the overall accuracies of the classified images of all the applied images of 2002, 2011 and 2022. Figures 7 , indicate the kappa coefficients of the classified images of (2002, 2011, & 2022). In this study, the Mahalanobis algorithm was employed for supervised image classification to estimate LU/LC change detection. It yielded superior results when compared to previous studies. For instance, Dewan and Yamaguchi [38] utilized Landsat MSS and TM along with a Maximum Likelihood approach for LU/LC detection spanning 1960-2005, achieving an accuracy of (85 -90)%. Another study by Jayanth et al., [39] applied support vector machines to classify images from LISS-IV and CARTOSAT-1 satellites, obtaining an accuracy range of (62.63 – 80.40) %. In this research, satellite images were classified into 5 distinct classes (Roads, Urban Region, Water Bodies Region, Vegetation Region, and Soil Region) to accurately represent the features and objects within the study area. It's worth noting that other studies of the same area employed 4 classes, potentially leading to the omission of information about certain features within the study area, as observed in the study by Zahraa and Hussein [40]. The study categorizes regions of interest into five major classes based on classified satellite images from 2002, 2011, and 2022. The obtained results and the changes in area (in km² and as a percentage) for each class from 2002 to 2022 are presented in Table 4, with special emphasis on the changes in urban and roads classes combined. Table 4. indicates the five-classes changes. The objective of this research was to ascertain changes in the study area using a statistical comparison technique. This entailed comparing various multi-temporal classified LU/LC maps generated from different multi-temporal satellite images captured between (2002) and (2011), and between (2011) and (2022). The study area witnessed significant spatial urban sprawl, with the urbanized area exhibiting a dramatic expansion in the central part of the research region in 2002. By 2022, this expansion had extended to cover a wider region. The statistical comparison of categories revealed noteworthy urban expansion between 2002 and 2022, with Roads and Urban Area categories displaying a substantial increase, collectively representing the actual urban area. The urbanization region in AL-Hilla experienced rapid growth, expanding by 49.31 km² from 16.59 km² in 2002 to 65.90 km² in 2022. The urbanization regions, encompassing both residential and roads, increased from 33.40 km² in 2002 to 89.16 km² in 2022, reflecting an Average Annual Increasing Rate of about 11.91%. The Average Annual Rate in 2022 was approximately 1.91 times higher than in 2002. The study indicated that the city underwent several phases of urbanization between 2002 and 2022, with particularly high urban sprawl observed in the first nine years (2002-2011), attributed to an influx of more than 600,000 people from surrounding countries. These individuals built private houses, leading to the construction of new residences and buildings in the region. The urbanized region grew dramatically from 33.40 km² in 2002 to 53.66 km² in 2011, amounting to 37% of AL-Hilla city's total area. The spatial urban expansion of the city continued to grow significantly in different directions between 2002 and 2011. Overall, the study underscores the significant urbanization that has occurred in AL-Hilla city over the last two decades, potentially impacting Urban Planning and Management in the region. Figure 8 , Growth of Urban Areas (Roads and Urban) for 2002, 2011, and 2022. In this study, a statistical approach was employed to track the transformations in AL-Hilla over a span of 20 years, from 2002 to 2022. This method entailed the comparison of Multi-Temporal Classified LU/LC Maps derived from Multi-Temporal Images. Spatial analysis of the region clearly indicated a substantial augmentation in the urban expanse, as illustrated in Figures 7-9. Urban sprawl was notably evident, with urban zones experiencing a significant surge, particularly concentrated in the central part of AL-Hilla in 2002. The urban areas continued to expand, exemplified in the 2022 thematic map. A statistical assessment of classes indicated a noteworthy increase in (Roads and Urban Area), jointly constituting the actual urban extent. This region underwent a rapid expansion, escalating from 16.59 km² in 2002 to 65.90 km² in 2022, signifying a growth of 49.31 km². The collective urban regions, encompassing residential and roads, swelled from 33.40 km² in 2002 to 89.16 km² in 2022, manifesting an Average-Annual-Increasing-Rate of 11.91% (refer to Table 5). Figures 5, 8, and 12 vividly portray the Urban-Expansion in various directions within AL-Hilla City. The latter decade of the study period, from 2011 to 2022, bore witness to a rapid urban enlargement, even amidst the influx of thousands of refugees between 2013 and 2018. Despite this demographic shift, the rate of urban expansion remained consistent, with only a marginal increase post-2011 attributed to the refugees settling inside or near AL-Hilla City. On a quantitative note, the combined urban and roads categories expanded by approximately 35.50 km², surging from 53.66 km² in 2011 to 89.16 km² in 2022 (see Fig. 12 and Table 4). The alterations and shifts in the Urban-Pattern and categories of this study were observed across all parts (Northwestern, Northeastern, Southwestern, and Southeastern) of AL-Hilla City, where new housing and residential areas emerged between 2011 and 2022 (see Figures 5b and c). Conversely, marked reductions were noted in the Water-Bodies, Vegetation-Area, and Soil-Area classes within the study area, as depicted in Figs. 5, 9, and Table 4, spanning the period from 2002 to 2022. The percentage decrease for these classes stood at (-3.80%, -15.63%, and -18.98%) respectively, equivalent to (-5.51 km², -22.66 km², and -27.52 km²) respectively. Fig. 9 visually represents the shifts in each class between 2002 and 2022. Overall, this study underscores the swift urbanization of AL-Hilla city over the past two decades, resulting in notable alterations in land cover and land use patterns within the study area. Figure 9. Changes in the classes between 2002-2022. 6. Urbanization Area Growth The percentage growth rate of the urbanization areas (urban and roads areas) for each year from 2002 to 2022 is presented in Table 5. The letter K is used as a key index to verify the growth of the urban region and is defined in equation (1) as described in reference [27]. In equation (1), (Ua) and (Ub) refer to the initial and final urban regions during the study period, while (T) indicates the entire duration of the study from (a to b). Table 5. AL-Hilla City Annual-Urban-Region-Growth Rates (2002–2022). The outcomes of this investigation disclose a twofold progression in the urbanization of AL-Hilla city over the span of 2002-2022. Initially, in the period from 2002 to 2011, there was an expansion of 17.78 km² in the urban area, registering an average annual growth rate of 1.97 km². Consequently, the urbanization rate surged from 23.70% to 45.44% of the total study area between 2011 and 2022. During this interval, the urban expansion primarily occurred horizontally within the confines of AL-Hilla city. When encompassing both urban and road regions, the urban area expanded from 33.4 km² to 53.66 km² from 2002 to 2011. The annual growth rates for the urban and road regions during the periods of 2002-2011 and 2011-2022 were 6.74% and 6.14% respectively, as outlined in Table 6. The subsequent stage, spanning from 2011 to 2022, focuses solely on urbanization area without taking roads into account. In this phase, the urban area saw an augmentation from 65.90 km² to 89.16 km², corresponding to an annual growth rate of 2.25 km². Table 6. AL-Hilla’s annual urban and roads region growth rate (2002–2022). From 2011 to 2022, the urbanization phase in AL-Hilla city witnessed a noteworthy rise in urban territory, expanding at an average annual rate of 1.11 km², accumulating to a total of 12.24 km². This growth was propelled by the development of critical infrastructure including educational institutions, residential and service buildings, water supply stations, and electricity facilities. These factors contributed substantially to the enlargement of the urban hub. This shift in the city's growth pattern was notable, as it moved from a previous trend of horizontal expansion towards a more vertical one. The annual growth rate for this period remained consistently at 8.34%, a figure that was sustained over the course of the 20-year study. When examining both urban and road areas from 2011 to 2022, the urban region saw an expansion from 53.66 km² to approximately 89.16 km², representing an annual growth rate of 8.16%. Previous research, as cited in references [17 & 38], utilizing satellite images from the Landsat satellite system of the MSS, TM, and ETM+ bands suggested that urbanization was predominantly concentrated along main roads and plantation lands. Our study demonstrated superior results compared to the findings of [17 & 38]. By employing various multispectral satellite images with differing spectral and spatial resolutions, we were able to detect and quantify urban sprawl and expansion in AL-Hilla city, as depicted in Figure 9 and tables (5 and 6). In contrast, alternative studies employed distinct methodologies, such as On-Screen digitizing on the Google Earth engine [41], remote sensing in combination with human observations on a geographic information system platform [42], Shannon’s entropy model [43], as well as Shannon’s entropy model in conjunction with principle component analysis [44-49], to identify and estimate urban areas. 7. Conclusion To investigate the changes in urban expansion within a specific area of AL-Hilla city over time, researchers utilized satellite imagery classified using the Mahalanobis distance classifier. The results were subsequently divided into five categories. The images employed in this study were obtained from different sensors, capturing data in the years 2002, 2011, and 2022, respectively. The researchers achieved an accuracy rate of approximately 85.94%, 89.82%, and 93.67% for the images from 2002, 2011, and 2022, respectively. The results indicated a notable increase in the urban area of AL-Hilla city, expanding from 33.40 km² in 2002 to approximately 89.16 km² in 2022, marking a surge of around 38.45% over the last two decades. The urban expansion occurred in two distinct stages: a High-Rate phase from 2002 to 2011, followed by a Steady-Rate phase from 2011 to 2022. The study identified that concentric urban sprawl was the predominant form of urban growth in AL-Hilla city from 2002 to 2022, predominantly occurring in residential regions, transportation routes, and new system routes. During this period, numerous schools, universities, and offices were established. Horizontal urbanization emerged as the dominant form of expansion throughout the entire period. However, from 2011 to 2022, the principal growth models in AL-Hilla shifted towards urban concentrations. The upswing in infrastructure projects significantly contributed to the expansion, with Vertical-Urban-Expansion taking precedence over the last decade. The findings of this study hold critical implications for urban planners and decision-makers in AL-Hilla city, emphasizing the need for more robust strategies for urban sustainability. The study also suggests that future research should explore alternative approaches, such as Shannon's entropy model, principle component analysis, and Markov algorithm, particularly when working with low-spatial datasets. Declarations Acknowledgments: The authors of the study would like to extend their gratitude to AL-Qasim Green University and Lulea University of Technology (Sweden) for the support that they received to complete this research. Their assistance and resources played a vital role in the successful completion of this study, and the authors are thankful for their contributions. Author Contributions: The research idea was conceived by HD and AMSA, while HD, and NAA were re-sponsible for developing the methodology. HD, AMSA and NAA handled the software aspect of the study, while HD, and NAA validated the results. HD, AMSA, and NAA conducted the investigation and curated the data. HD and NAA wrote the original draft of the manuscript, while NAA was in charge of the visualization. NAA oversaw the project administration. All authors have reviewed and consented to the final manuscript. Competing interests The authors have no conflicts of interest. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Supplementary information Supplementary Methods, Tables 1–6 and Figs. 1–9. References Wu, Y., Li, S., & Yu, S. Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China. Environ. Monit. Assess. 188, 54 (2016). Nitze, I., Guido, G., Benjamin M., Jones, Vladimir, E. R., & Julia, B. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun.,. 9, 1, 1-11 (2018). Cheng, M., Xiyun J., Lei, S., Josep, P., Lalit, K., Chenwei, N., Tianao, W., Kaihua, L., Wenbin, W., and Xiuliang, J. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing. Scientific Data . 9, ,1, 1-13 (2022). Li, S., Vu, D., Mukesh, K., Phu, N., and Tirtha, B. Mapping the wildland-urban interface in California using remote sensing data. Scientific reports 12, 1, 1-12 (2022). Lambin, E. F., Turner, B.L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., Coomes, O.T., Dirzo, R., Fischer, G., Folke, C., et al. The cause of land cover change moving beyond the myths. Glob. Environ. Chang . 11, 261–269, (2001) Dibs, H.,Mansor, S., Ahmad, N., Al-Ansari, N. Geometric Correction Analysis of Highly Distortion of Near Equatorial Satellite Images Using Remote Sensing and Digital Image Processing Techniques. Eng., 14, 1,1-8 (2022a). Dibs, H., Hasab, H.A., Jaber, H.S., Al-Ansari, N. Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images. Inn. Infrast. Solus. 7,1, 1-4 (2022b). Karanam, H.K., & Neela, V.B. Study of normalized difference built-up (NDBI) index in automatically mapping urban areas from Landsat TN imagery. Int. J. Eng. Sci. Math . 8, 239–248 (2017). Dewan, A.M., Kabir, M.H., Nahar, K., Rahman, M.Z. Urbanization and environmental degradation in Dhaka metropolitan area of Bangladesh. Int. J. Environ. Sustain. Dev . 11, 118–147 (2012). Dibs, H., Hasab, H.A., Mahmoud, A.S., & Al-Ansari, N. Fusion methods and multi-classifiers to improve land cover estimation using remote sensing analysis. Geo. Geol. Eng. 39, 8, 5825-42, (2021). Abbas, Z., & Jaber, H. S. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012166). IOP Publishing, (2020). Chen, S., Zeng, S., & Xie, C. Remote sensing and GIS for urban growth analysis in China. Photogramm. Eng. Remote Sens. J . 66, 593–598 (2000). Li, X., and Yeh, A.G.O. Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. Landsc. Urban Plane J . 64, 67–76 (2004). Liu, H., Weng, Q. Scaling-up effect on the relationship between landscape pattern and land surface temperature. Photogramm. Eng. Remote Sens. J. 75, 291–304 (2009). Andersson, E. Urban landscapes and sustainable cities. Eco. and soc. 11.1 (2006). Zhou, N., Zhao, S. Urbanization process and induced environmental geological hazards in China. Nat. Hazard . 67, 797–810 (2013). Zhou, D., Shi, P., Wu, X., Ma, J., and Yu, J. Effects of urbanization expansion on landscape pattern and region ecological risk in Chinese coastal city: A case study of Yantai city. Sci. World J . 1, (2014). Bhatta, B. Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India.Int. J. Remote Sens. 30, 4733–4746 (2009). Batisani, N., Yarnal, B. Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations. Appl. Geogr. 29, 235–249 (2009). Al-Bilbisi, H., & Tateishi, R. Using satellite remote sensing data to detect land use/cover changes and to monitor land degradation in central Jordan. J. Jpn. Soc. Photogramm. Remote Sens. 42, 4–18 (2003). Soffianian, A., Ahmadiadoushan, M., Yaghmael, L., & Falahatkar, S. Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran. World Appl. Sci. J. 9, 1370–1378 (2010). Al-Bilbisi, H. H. Land use/cover change detection in arid and semi-arid areas of Northeastern Jordan using Landsat images. Jordan J. Soc. Sci. 10, 265–275 (2017). Pirnazar, M., Ali-Askari, K., Eslamian, S., Singh, V., Dalezios, N., Ghane, M., and Qasemi, Z. Change detection of urban land use and erban expansion using GIS and RS, case study: Zanjan Province, Iran. Int. J. Constr. Civ. Eng . 4, 23–38 (2018). Weng, Q. Land use change analysis in Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. J. Environ. Manag . 64, 273–284 (2002). Al-Bilbisi, H. A two decades land use/cover change detection and land degradation monitoring in central Jordan using satellite images. Jordan J. Soc. Sci. 5, 133–149 (2012). Gar-On Y. A., Xia, L. Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm. Eng. remote Sen . 67 ,1, 83-90 (2001). Lee, J., Tian, L., Erickson, L., Kulikowski, D. Analyzing growth management policies with geographical information systems. Environ. Plann. B: Plann. Des . 25, 6, 865–879 (1998). Dibs, H., Al-Hedny, S. Detection wetland dehydration extent with multi-temporal remotely sensed data using remote sensing analysis and GIS techniques. Int. J. of Civil Eng. and Tech.10, 143-54 (2019). Dibs, H., Idrees, M.O., Saeidi, V., Mansor, S. Automatic Keypoints Extraction from UAV Image with Refine and Improved Scale Invariant Features Transform (RI-SIFT). Int. J. of Geo. 1,12, 3 (2016). Dibs, H. Comparison of derived Indices and unsupervised classification for AL-Razaza Lake dehydration extent using multi-temporal satellite data and remote sensing analysis. J Eng Appl Sci. 13, 24, 1-8 (2018). Money, R. I. The Hindiya Barrage, Mesopotamia. The Geo. J. 50, 3, 217-222 (1917). Schowengerdt, R.A. Remote Sensing: Models and Methods for Image Processing , (3rd ed., Academic Press), (London, UK, 2006). Jensen, J. R. Introductory Digital Image Processing: A Remote Sensing Perspective , (3rd ed., Prentice Hall: Upper Saddle River), ( NJ, USA, 2005). Singh, A. Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens . 10, 989–1003 (1989). Yang, X., Lo, C.P. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int. J. Remote Sens . 23, 1775–1798 (2002). Campbell, J. B., and R. H. Wynne. Introduction to Remote Sensing. Guilford. 123-157. New York, USA, 19872, (1987). Xiao, J.Y., Shen, Y.J., Ge, J.F., Tateishi, R., Tang, C.Y., Liang, Y.Q. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc. Urban Plan. J. 75, 69–80 (2006) Dewan, A.M., Yamaguchi, Y., 2009a. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environ. Monit. Assess. 150, 237–249. http://dx.doi.org/10.1007/s10661-008-0226-5. Jayanth, J., Ashok Kumar, T., Koliwad, S., Krishnashastry, S., 2016. Identification of land cover changes in the coastal area of Dakshina Kannada district, South India during the year 2004–2008. Egypt. J. Remote Sens. Space Sci. 19, 73–93. http:// dx.doi.org/10.1016/j.ejrs.2015.09.001. Abbas, Zahraa, and Hussein Sabah Jaber. "Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques." In IOP Conference Series: Materials Science and Engineering, vol. 745, no. 1, p. 012166. IOP Publishing, 2020. Lopez, J.M.R., Heider, K. and Scheffran, J., 2017. Frontiers of urbanization: Identifying and explaining urbanization hot spots in the south of Mexico City using human and remote sensing. Applied Geography, 79, pp.1-10. Mosammam, H.M., Nia, J.T., Khani, H., Teymouri, A. and Kazemi, M., 2017. Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. The Egyptian Journal of Remote Sensing and Space Science, 20(1), pp.103-116. Dhali, M.K., Chakraborty, M. and Sahana, M., 2019. Assessing spatio-temporal growth of urban sub-centre using Shannon’s entropy model and principle component analysis: A case from North 24 Parganas, lower Ganga River Basin, India. The Egyptian Journal of Remote Sensing and Space Science, 22(1), pp.25-35. Al Mashagbah, A., Al-Adamat, R. and Al-Amoush, H., 2012. GIS and remote sensing to investigate urban growth in Mafraq City/Jordan between 1987 and 2010. Dibs H, Jaber HS, Al-Ansari N. Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis. Emerging Science Journal.7(4):1215-31, (2023). Dibs H, Al-Ansari N, Laue J. Analysis of Remotely Sensed Imagery and Architecture Environment for Modelling 3D Detailed Buildings Using Geospatial Techniques. Engineering,15(05):328-41, (2023). Dibs H, Mansor S, Ahmad N, Al-Ansari N. Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis. Engineering. 15(2):75-89, (2023). Dibs H, Mansor S, Ahmad N, Al-Ansari N. Geometric correction analysis of highly distortion of near equatorial satellite images using remote sensing and digital image processing techniques. Engineering. 14(01):1-8, (2022) Hashim F, Dibs H, Jaber HS. Applying Support Vector Machine Algorithm on Multispectral Remotely Sensed Satellite Image for Geospatial Analysis. InJournal of Physics: Conference Series 2021 Jul 1 (Vol. 1963, No. 1, p. 012110). IOP Publishing. Tables Table 1. Datasets-Specifications. Satellite Date Spectral resolution Spatial resolution (m) Projection QuickBird-2 June, 2002 5 bands 1 x 1 UTM (Zone-38N) WorldView-2 June, 2011 8 bands 0.6 x 0.6 UTM (Zone-38N) Sentinel-2 June, 2022 13 bands 10 x 10 UTM (Zone-38N) Table 2. the selected GCPs No. Latitude Longitude 1 32°28'33.03"N 44°28'08.35"E 2 32°31'10.73"N 44°27'27.05"E 3 32°31'42.42"N 44°25'34.94"E 4 32°31'18.48"N 44°23'58.57"E 5 32°29'29.98"N 44°23'43.18"E 6 32°28'05.89"N 44°23'23.08"E 7 32°26'52.05"N 44°23'22.64"E 8 32°26'40.34"N 44°28'17.70"E 9 32°24'03.89"N 44°23'27.86"E 10 32°24'33.34"N 44°29'04.27"E 11 32°25'52.53"N 44°25'44.55"E 12 32°27'10.51"N 44°26'27.56"E 13 32°28'28.91"N 44°25'22.34"E 14 32°30'10.38"N 44°25'56.98"E Table 3. the considered classes, and their definitions in this study. Class number Class name Descriptions 1 Roads asphalt roads 2 Vegetation Plantation Type Areas 3 Urban Area All Types of Urban and Building areas 4 Water Bodies Water-Bodies 5 Soil Soils Table 4. indicates the five-classes changes. Class Name 2002 Area (km 2 ) / (%) 2011 Area (km 2 ) / (%) 2022 Area (km2) / (%) Area Differences in Km 2 Area Differences in (%) Urban and roads areas Roads 16.81 11.59 19.29 13.30 23.26 16.04 + 06.45 + 04.45 +38.45 % Urban Area 16.59 11.44 34.37 23.70 65.90 45.44 + 49.30 + 34.00 Water Bodies 07.93 05.47 04.29 02.96 02.42 01.67 - 05.51 - 03.80 ------- Vegetation 51.21 35.35 43.15 29.76 28.59 19.72 - 22.66 - 15.63 ------- Soil Area 52.42 36.15 43.90 30.28 24.90 17.17 - 27.52 - 18.98 ------- Table 5. AL-Hilla City Annual-Urban-Region-Growth Rates (2002–2022). Date Urban Areas (km 2 ) Annual-Growth (%) 2002 16.59 ------- 2011 34.37 11.91 2022 65.90 08.34 Table 6. AL-Hilla’s annual urban and roads region growth rate (2002–2022). Year Urban-Area (km 2 ) Annual-Growth (%) 1- 2002 33.40 ----- - 2- 2011 53.66 6.74 3- 2022 89.16 6.14 Supplementary Methods Supplementary Methods are not available with this version. 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-4148120","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288371868,"identity":"7e5bdca3-fc5f-42d3-8eb3-314927c5ef64","order_by":0,"name":"Nadhir Al-Ansari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACM4YEIMkH4UjI8RGthQ2qxZiNjRgtDAgtDIlthLSYsx/e9uDhHgZ5Nvazhz/83GOR3ibfY8D4owK3FsuetHKDhGcMhm08eWmSPc8kctvYeAyYec7g1mJwIMdMIuEAA2MbQ44ZA88BqBYgF7eW82/AWuzb+N8Yf/xzQCKdDaiF8ec/PFpuQGxJbJPIMZAG2pIA0sLA24BPy7MyoBaJ5DaJN2bSMgckDNvY0goO8xzD57DkbZI/DtjY9vPnGH98c6BOnp/58MaHP2pwa4ECCVTuAYIaRsEoGAWjYBTgBQAoQ0cG7dpQOgAAAABJRU5ErkJggg==","orcid":"","institution":"Lulea University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Nadhir","middleName":"","lastName":"Al-Ansari","suffix":""},{"id":288371869,"identity":"3e17b6a8-a41f-403f-901f-bddf703dfb31","order_by":1,"name":"Hayder Dibs","email":"","orcid":"","institution":"AL-Qasim Green University","correspondingAuthor":false,"prefix":"","firstName":"Hayder","middleName":"","lastName":"Dibs","suffix":""},{"id":288371870,"identity":"3fdc0b3c-26ba-468f-b220-f6ccf6e94015","order_by":2,"name":"Ahmed Al-Janabi","email":"","orcid":"","institution":"Cihan University-Erbil","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Al-Janabi","suffix":""}],"badges":[],"createdAt":"2024-03-22 08:15:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4148120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4148120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54321843,"identity":"40bf1e27-799a-47b0-b523-311299d92c1d","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211753,"visible":true,"origin":"","legend":"\u003cp\u003eIndicates the methodology.\u003c/p\u003e","description":"","filename":"Doc11.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/6aa8f4f2e1501f06ba7ecc8e.png"},{"id":54321842,"identity":"a8ea855e-e3e3-4d31-91e9-e485cca0b76c","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59771,"visible":true,"origin":"","legend":"\u003cp\u003eLocation in research area.\u003c/p\u003e","description":"","filename":"Doc12.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/04cda7c093f30fc3353cee85.png"},{"id":54321841,"identity":"059512cd-519f-4b52-a160-3bf6fc79c2dc","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":787902,"visible":true,"origin":"","legend":"\u003cp\u003eThe Research area GCPs.\u003c/p\u003e","description":"","filename":"Doc13.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/7545d7bd352f4f281598d1f6.png"},{"id":54322249,"identity":"f114ce6d-c07b-4810-8cac-66e965af161e","added_by":"auto","created_at":"2024-04-08 19:48:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2001840,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates; (A); The QuickBird-2 satellite imagery of 2002, (B) The WorldView-2 satellite imagery of 2011. (C) The Sentinel-2 satellite imagery of 2022 of the study area.\u003c/p\u003e","description":"","filename":"Doc14.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/ad3b403eda7154f6b6c03238.png"},{"id":54321848,"identity":"642b3dc5-f0b0-49b9-9bd5-733a82996c6f","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2359053,"visible":true,"origin":"","legend":"\u003cp\u003e5A, 5B, and 5C illustrate the classified thematic maps of AL-Hilla from the QuickBird-2 satellite image in 2002, WorldView-2 satellite image in 2011, and Sentinel-2 satellite image in 2022, respectively.\u003c/p\u003e","description":"","filename":"Doc15.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/a9431e79ebd76f86c03b97a8.png"},{"id":54321847,"identity":"083a8fc7-de02-4240-8010-3d30be36cac9","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":12221,"visible":true,"origin":"","legend":"\u003cp\u003eIndicates the overall accuracies of the classified images of all the applied images of 2002, 2011 and 2022.\u003c/p\u003e","description":"","filename":"Doc16.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/43a6290b677b86316d739ff9.png"},{"id":54321846,"identity":"2cec9bc7-96bd-4cd5-9f8b-f2c08b21eaef","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15148,"visible":true,"origin":"","legend":"\u003cp\u003eIndicate the kappa coefficients of the classified images of (2002, 2011, \u0026amp; 2022).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/3b3266baf6ca2e55cb9e7bc4.png"},{"id":54321845,"identity":"e90472fc-a157-4789-a77b-4c3fbc85278e","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":63711,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth of Urban Areas (Roads and Urban) for 2002, 2011, and 2022.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/e0c589dcde5077391f5a1ede.png"},{"id":54321849,"identity":"8283870b-0d0f-4430-b626-ce70d7058044","added_by":"auto","created_at":"2024-04-08 19:40:07","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44841,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the classes between 2002-2022.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/0839c6b1a1773861a732fa74.png"},{"id":66956208,"identity":"bdf070c1-dab0-4562-9cc0-ed8926423e27","added_by":"auto","created_at":"2024-10-18 11:31:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6665386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4148120/v1/58b4cf65-410f-4a68-9a7d-c478093ef9c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanization holds paramount importance in spatiotemporal analysis and exerts significant influence on various levels, including social, human life, economic, animal habitats, and environmental aspects. Urban sprawl, a complex and multifaceted spatiotemporal process, encompasses diverse forms of urban expansion [1-4]. In urban areas, sprawl arises from a combination of anthropogenic and natural factors [5]. Anthropogenic elements like population growth, economic development, and urbanization expansion serve as primary drivers of urban sprawl. However, natural factors such as topography and soil properties also contribute, exhibiting spatial heterogeneity [6-7]. The detrimental effects of urban sprawl on vegetation lands are well-documented [8]. The expansion of urban areas can induce various changes, including landscape transformation, desertification, conversion, temperature elevation, land degradation, and fragmentation of agricultural lands [9-11].\u003c/p\u003e\n\u003cp\u003eCurrently, AL-Hilla, Iraq faces numerous challenges due to extensive conversion and fragmentation of vegetation and plantation lands, including orchards, into urban spaces. This has resulted in desertification, climate shifts, high temperatures, and persistent dust storms, particularly during the summer season [11]. Numerous studies have underscored the consequences of landscape conversion and transformation in urban region sprawl [12-14]. Urban expansion poses critical environmental issues with negative impacts on the sustainability of agricultural lands [13]. Moreover, urban growth leads to landscape conversion and transformations occurring in diverse and multi-directional manners [15-16]. Hence, effective management and planning strategies are imperative to mitigate the adverse effects of urban growth on the environment and agriculture in AL-Hilla city.\u003c/p\u003e\n\u003cp\u003eAccording to previous research [17-19], urban sprawl is a complex process influenced by various factors, including land topography, city demographics, population, and economy. This process often involves the conversion of natural lands, such as rocks, soil, and vegetation, into manmade areas, including asphalt, concrete, and metals. Consequently, the surrounding lands and human resources may be positively or negatively impacted by urban sprawl, affecting human life quality. To address this issue, remote sensing and GIS technologies have become indispensable tools for detecting, monitoring, and managing urban growth [1, 20-30]. Specifically, satellite datasets and temporal remote sensing data offer comprehensive views and repetitive coverage of urban areas, enabling the establishment of databases for monitoring changes in urban growth. In this study, the Mahalanobis approach was employed for image classification of multispectral and temporal satellite images (QuickBird-2, WorldView-2, and Sentinel-2) to detect and quantify the rate of urban area expansion from 2002 to 2022 in AL-Hilla city, Babylon, Iraq. The findings of this study hold significance for decision-makers in city management and planning to estimate the social, economic, and environmental impacts of urban growth. It is noteworthy that the population of AL-Hilla has experienced a substantial increase since 2000, with a threefold growth recorded between 2003 and 2022 [31], resulting in alterations in land use and spatial organization.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis research employed a multi-sensor approach using QuickBird, WorldView, and Sentinel Satellite-Images to evaluate the efficiency of detecting changes in LU/LC and monitoring expanded urbanization areas in AL-Hillah. The study consisted of various phases, including image pre-processing, study area selection, satellite image resampling, and ground control point (GCP) collection, training and testing samples selection, and the adoption of the MD as a supervised image classifier to create LU/LC thematic maps. The outputs accuracies were verified using the confusion matrix approach. The research also involved analyzing the changes in land categories using a statistical comparison of the classified images between 2002 and 2022, and computing the annual urban growth rate and its percentage rate. Figure 1 portrays the methodology of this study, as presented by Aljuboori et al. (2021). By utilizing multi-sensor datasets with different resolutions, this study offers insights into the potential of integrating satellite images to detect urbanization and land cover changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1, indicates the methodology.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.1 Study Area Description\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe research focuses on the central part of AL-Hill city, Babylon-Iraq, with coordinates of 32\u0026deg;32\u0026apos;07.93\u0026quot;N - 32\u0026deg;23\u0026apos;31.52\u0026quot;N latitude and 44\u0026deg;22\u0026apos;50.30\u0026quot;E- 44\u0026deg;29\u0026apos;42.41\u0026quot;E longitude. The study area covers an elevation of approximately 112 feet (34 meters) above the mean sea level and is situated about 100 km south of capital city of Iraq (Baghdad). AL-Hilla is geographically in central Iraq, and it is bordered with other governorates, including Baghdad, Karbala, Anbar, Najaf, Wassit, and AL-Qadissiya. The Euphrates-river runs through Al-Hilla, and it is second greatest and oldest rivers in Iraq. It has two branches, the first one is called the AL-Hindiyah river, and the second one is called AL-Hilla river. As per the 2018 Census, The study area has a population around of 970,000 inhabitants. \u0026nbsp;(see Fig. 2 for a detailed illustration of the study area) [31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2, Location in research area.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgriculture plays a crucial role in the economy of AL-Hilla, covering thousands of square kilometers and providing a wide variety of fruits, vegetation, and plantations to the local markets. The city also boasts numerous archaeological sites, including the ancient city of Babylon, which draw tourists and scholars to the area. Additionally, the presence of several private and government universities in the vicinity enhances the city\u0026apos;s economic prospects. AL-Hilla experiences a dry climate, with temperatures during the summer months sometimes exceeding 50\u0026deg;C, while the average temperatures range from 14-24\u0026deg;C. Rainfall is scarce, occurring from November to April in the Winter-Season, with an Annual-Precipitation of 4.49 inches [32].\u003c/p\u003e"},{"header":"3. Images Remove Noises","content":"\u003cp\u003eIn order to track changes in LU/LC as well as Urbanization expansion and growth in the research area, each of QuickBird-2, WorldView-2, and Sentinel-2 satellite images adopted. These images\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ehave highly Spatial, Spectral, and Spectral resolutions, and that made them suitable for this purpose. The images used were from 2002, 2011, and 2022 and were free of cloud cover. The Sentinel-2 imagery was obtained from the website of the European Space Agency (ESA). Table 1. Illustrates specifications of the applied images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Datasets-Specifications.\u003c/p\u003e\n\u003cp\u003eVarious techniques employed to prepare the images for the subsequent processes aimed to detect and monitor the changes in LU/LC, urban development and growth in the study area. These techniques involved geometric and radiometric corrections, as well as image sub-setting to get the research area. After that, the images were subjected to geometric and radiometric corrections. Images-Ggeometric-Correction was accomplished by gathering 14 Ground Control Points (GCPs) in the field of the research area, which were then utilized to correct the image of QuickBird-2 satellite geometrically. Then, QuickBird-2 imagery was used as the reference (master) scene for the 2011 and 2022 images. The locations of the collected GCPs are shown in Fig. 3 and Table 2. Geometric rectification is a crucial step in producing a corrected map that identifies different classes change, urbanization regions expansion over time. The WordView-2 (2011) and Sentinel-2 (2022) satellite images were already corrected, and geo-referenced regarding the Universal Transverse Mercator map projection in Zone 38N, with the datum (WGS84-Ellipsoid). The Images served as slave scenes and were registered to the QuickBird-2 master scene using the image-to-image approach, which employed a first-degree polynomial equation for images transformation. The image rectification process yielded an excellent root-mean-square-error less than (0.5) m. Resampling performed using the Nearest-Neighbor approach, while Histogram-Matching was used to enhance the visual appearance and brightness of the output image. These techniques are commonly used to improve the quality of remote sensing data and have been reported in the literature [32, 33].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e the selected GCPs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 The Research area GCPs.\u003c/p\u003e"},{"header":"4. Image Processing and Classification ","content":"\u003cp\u003eIn order to investigate the changes of sprawl in urban regions in research study from (2002 to 2022), the researchers chose specific bands from the employed satellite images. The selected bands were those that provided good visual information for urban areas, specifically bands 1, 3, and 4 form images of (QuickBird and Worldview), and bands 2, 4, and 7 for Sentinel-2. Color combinations were then created for each image to aid in analysis. For the conducting of classification, nine bands were used from the three data, excluding the thermal data. The MD method utilized for image classification, and the software used for LU/LC estimation was Envi. The researchers chose five categories for classification: Roads, Vegetation, Water Bodies, Soil region, and Urban region , which are further detailed in Table 3. region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003ethe considered classes, and their definitions in this study.\u003c/p\u003e\n\u003cp\u003eThe classification accuracy of LU/LC is often hampered by spectral confusion, particularly when the classes share similar spectral responses. This is mainly due to the limitations of the classification methods used [34-36]. To overcome this limitation, the current study employed visual interpretation and on-screen digitizing. Two types of spectral confusion were identified, namely, (a) the Urban Region/Soil Region classes, (b) the Vegetation Region/Soils classes. These confusions were resolved by examining each pixel in the classified images and manually correcting any misclassifications. By adopting these methods, the study was able to produce accurate LU/LC maps of the study area. [34-36] were consulted for this study.\u003c/p\u003e"},{"header":"5. Results and Discussions","content":"\u003cp\u003eFigures 4(a, b and c) shows the employed image for the years (2002, 2011, \u0026amp; 2022). Prior to analysis, the images underwent three processing steps: (1) subsetting to extract the study region, (2) remove the geometric and radiometric noises, and (3) Rectification of images using the image-to-image approach between the (QuickBird-2) and (WorldView-2 and Sentinel-2) [1]. The correction of geometric and radiometric errors ensured that the images were aligned and had consistent radiometry, which is crucial for accurate analysis of land use and land cover changes over time\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;4\u003c/strong\u003e.\u0026nbsp;Illustrates; (A); The QuickBird-2 satellite imagery of 2002, (B) The WorldView-2 satellite imagery of 2011. (C)\u0026nbsp;The Sentinel-2\u0026nbsp;satellite imagery of 2022 of the study area.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e5.1 Urban Sprawl Monitoring Changes\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe results of the satellite image classification for the years (2002, 2011, and 2022) using (QuickBird-2, WorldView-2, and Sentinel-2), are presented in Fig. 5 (a, b and c). The Mahalanobis classifier was employed for the classification process, and training and testing sites were collected through a random sampling technique from utilized images. The training sites utilized to classify the images for the periods of 2002-2011 and 2011-2022. For evaluate outputs, testing sites checked in situ and through visual interpretation of images acquired during fieldwork in July 2022. The overall accuracy of the results was found to be (85.94%, 89.82%, and 93.67% for (2002), (201)1, and (2022) images. The accuracy of the classified maps exceeded 85%, indicating the image processing methods used in this research are suitable for the analysis.\u003c/p\u003e\n\u003cp\u003eFigures 5. in 5A, 5B, and 5C illustrate the classified thematic maps of AL-Hilla from the QuickBird-2 satellite image in 2002, WorldView-2 satellite image in 2011, and Sentinel-2 satellite image in 2022, respectively.\u003c/p\u003e\n\u003cp\u003eThe study findings provide the kappa coefficients for the three satellite systems examined. According to the results, the QuickBird-2 images captured in 2002, WorldView-2 images taken in 2011, and Sentinel-2 images acquired in 2022 demonstrate kappa coefficients of about 0.84, 0.86, and 0.90, respectively. Figs. 6 and 7 summarize the overall-accuracies, and kappa-coefficients of the classified images utilizing the MD classifier for the three images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigures 6\u003c/strong\u003e, indicates the overall accuracies of the classified images of all the applied images of 2002, 2011 and 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigures 7\u003c/strong\u003e, indicate the kappa coefficients of the classified images of (2002, 2011, \u0026amp; 2022).\u003c/p\u003e\n\u003cp\u003eIn this study, the Mahalanobis algorithm was employed for supervised image classification to estimate LU/LC change detection. It yielded superior results when compared to previous studies. For instance, Dewan and Yamaguchi [38] utilized Landsat MSS and TM along with a Maximum Likelihood approach for LU/LC detection spanning 1960-2005, achieving an accuracy of (85 -90)%. Another study by Jayanth et al., [39] applied support vector machines to classify images from LISS-IV and CARTOSAT-1 satellites, obtaining an accuracy range of (62.63 \u0026ndash; 80.40) %. In this research, satellite images were classified into 5 distinct classes (Roads, Urban Region, Water Bodies Region, Vegetation Region, and Soil Region) to accurately represent the features and objects within the study area. It\u0026apos;s worth noting that other studies of the same area employed 4 classes, potentially leading to the omission of information about certain features within the study area, as observed in the study by Zahraa and Hussein [40]. The study categorizes regions of interest into five major classes based on classified satellite images from 2002, 2011, and 2022. The obtained results and the changes in area (in km\u0026sup2; and as a percentage) for each class from 2002 to 2022 are presented in Table 4, with special emphasis on the changes in urban and roads classes combined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eindicates the five-classes changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe objective of this research was to ascertain changes in the study area using a statistical comparison technique. This entailed comparing various multi-temporal classified LU/LC maps generated from different multi-temporal satellite images captured between (2002) and (2011), and between (2011) and (2022). The study area witnessed significant spatial urban sprawl, with the urbanized area exhibiting a dramatic expansion in the central part of the research region in 2002. By 2022, this expansion had extended to cover a wider region. The statistical comparison of categories revealed noteworthy urban expansion between 2002 and 2022, with Roads and Urban Area categories displaying a substantial increase, collectively representing the actual urban area. The urbanization region in AL-Hilla experienced rapid growth, expanding by 49.31 km\u0026sup2; from 16.59 km\u0026sup2; in 2002 to 65.90 km\u0026sup2; in 2022. The urbanization regions, encompassing both residential and roads, increased from 33.40 km\u0026sup2; in 2002 to 89.16 km\u0026sup2; in 2022, reflecting an Average Annual Increasing Rate of about 11.91%. The Average Annual Rate in 2022 was approximately 1.91 times higher than in 2002. The study indicated that the city underwent several phases of urbanization between 2002 and 2022, with particularly high urban sprawl observed in the first nine years (2002-2011), attributed to an influx of more than 600,000 people from surrounding countries. These individuals built private houses, leading to the construction of new residences and buildings in the region. The urbanized region grew dramatically from 33.40 km\u0026sup2; in 2002 to 53.66 km\u0026sup2; in 2011, amounting to 37% of AL-Hilla city\u0026apos;s total area. The spatial urban expansion of the city continued to grow significantly in different directions between 2002 and 2011. Overall, the study underscores the significant urbanization that has occurred in AL-Hilla city over the last two decades, potentially impacting Urban Planning and Management in the region.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan dir=\"RTL\"\u003e8\u003c/span\u003e, Growth of Urban Areas (Roads and Urban) for 2002, 2011, and 2022.\u003c/p\u003e\n\u003cp\u003eIn this study, a statistical approach was employed to track the transformations in AL-Hilla over a span of 20 years, from 2002 to 2022. This method entailed the comparison of Multi-Temporal Classified LU/LC Maps derived from Multi-Temporal Images. Spatial analysis of the region clearly indicated a substantial augmentation in the urban expanse, as illustrated in Figures 7-9. Urban sprawl was notably evident, with urban zones experiencing a significant surge, particularly concentrated in the central part of AL-Hilla in 2002. The urban areas continued to expand, exemplified in the 2022 thematic map. A statistical assessment of classes indicated a noteworthy increase in (Roads and Urban Area), jointly constituting the actual urban extent. This region underwent a rapid expansion, escalating from 16.59 km\u0026sup2; in 2002 to 65.90 km\u0026sup2; in 2022, signifying a growth of 49.31 km\u0026sup2;. The collective urban regions, encompassing residential and roads, swelled from 33.40 km\u0026sup2; in 2002 to 89.16 km\u0026sup2; in 2022, manifesting an Average-Annual-Increasing-Rate of 11.91% (refer to Table 5).\u003c/p\u003e\n\u003cp\u003eFigures 5, 8, and 12 vividly portray the Urban-Expansion in various directions within AL-Hilla City. The latter decade of the study period, from 2011 to 2022, bore witness to a rapid urban enlargement, even amidst the influx of thousands of refugees between 2013 and 2018. Despite this demographic shift, the rate of urban expansion remained consistent, with only a marginal increase post-2011 attributed to the refugees settling inside or near AL-Hilla City. On a quantitative note, the combined urban and roads categories expanded by approximately 35.50 km\u0026sup2;, surging from 53.66 km\u0026sup2; in 2011 to 89.16 km\u0026sup2; in 2022 (see Fig. 12 and Table 4). The alterations and shifts in the Urban-Pattern and categories of this study were observed across all parts (Northwestern, Northeastern, Southwestern, and Southeastern) of AL-Hilla City, where new housing and residential areas emerged between 2011 and 2022 (see Figures 5b and c).\u003c/p\u003e\n\u003cp\u003eConversely, marked reductions were noted in the Water-Bodies, Vegetation-Area, and Soil-Area classes within the study area, as depicted in Figs. 5, 9, and Table 4, spanning the period from 2002 to 2022. The percentage decrease for these classes stood at (-3.80%, -15.63%, and -18.98%) respectively, equivalent to (-5.51 km\u0026sup2;, -22.66 km\u0026sup2;, and -27.52 km\u0026sup2;) respectively. Fig. 9 visually represents the shifts in each class between 2002 and 2022. Overall, this study underscores the swift urbanization of AL-Hilla city over the past two decades, resulting in notable alterations in land cover and land use patterns within the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 9.\u003c/strong\u003e Changes in the classes between 2002-2022.\u003c/p\u003e"},{"header":"6. Urbanization Area Growth ","content":"\u003cp\u003eThe percentage growth rate of the urbanization areas (urban and roads areas) for each year from 2002 to 2022 is presented in Table 5. The letter K is used as a key index to verify the growth of the urban region and is defined in equation (1) as described in reference [27].\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1712577791.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn equation (1), (Ua) and (Ub) refer to the initial and final urban regions during the study period, while (T) indicates the entire duration of the study from (a to b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eAL-Hilla City Annual-Urban-Region-Growth Rates (2002\u0026ndash;2022).\u003c/p\u003e\n\u003cp\u003eThe outcomes of this investigation disclose a twofold progression in the urbanization of AL-Hilla city over the span of 2002-2022. Initially, in the period from 2002 to 2011, there was an expansion of 17.78 km\u0026sup2; in the urban area, registering an average annual growth rate of 1.97 km\u0026sup2;. Consequently, the urbanization rate surged from 23.70% to 45.44% of the total study area between 2011 and 2022. During this interval, the urban expansion primarily occurred horizontally within the confines of AL-Hilla city. When encompassing both urban and road regions, the urban area expanded from 33.4 km\u0026sup2; to 53.66 km\u0026sup2; from 2002 to 2011. The annual growth rates for the urban and road regions during the periods of 2002-2011 and 2011-2022 were 6.74% and 6.14% respectively, as outlined in Table 6. The subsequent stage, spanning from 2011 to 2022, focuses solely on urbanization area without taking roads into account. In this phase, the urban area saw an augmentation from 65.90 km\u0026sup2; to 89.16 km\u0026sup2;, corresponding to an annual growth rate of 2.25 km\u0026sup2;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eAL-Hilla\u0026rsquo;s annual urban and roads region growth rate (2002\u0026ndash;2022).\u003c/p\u003e\n\u003cp\u003eFrom 2011 to 2022, the urbanization phase in AL-Hilla city witnessed a noteworthy rise in urban territory, expanding at an average annual rate of 1.11 km\u0026sup2;, accumulating to a total of 12.24 km\u0026sup2;. This growth was propelled by the development of critical infrastructure including educational institutions, residential and service buildings, water supply stations, and electricity facilities. These factors contributed substantially to the enlargement of the urban hub. This shift in the city\u0026apos;s growth pattern was notable, as it moved from a previous trend of horizontal expansion towards a more vertical one. The annual growth rate for this period remained consistently at 8.34%, a figure that was sustained over the course of the 20-year study. When examining both urban and road areas from 2011 to 2022, the urban region saw an expansion from 53.66 km\u0026sup2; to approximately 89.16 km\u0026sup2;, representing an annual growth rate of 8.16%.\u003c/p\u003e\n\u003cp\u003ePrevious research, as cited in references [17 \u0026amp; 38], utilizing satellite images from the Landsat satellite system of the MSS, TM, and ETM+ bands suggested that urbanization was predominantly concentrated along main roads and plantation lands. Our study demonstrated superior results compared to the findings of [17 \u0026amp; 38]. By employing various multispectral satellite images with differing spectral and spatial resolutions, we were able to detect and quantify urban sprawl and expansion in AL-Hilla city, as depicted in Figure 9 and tables (5 and 6). In contrast, alternative studies employed distinct methodologies, such as On-Screen digitizing on the Google Earth engine [41], remote sensing in combination with human observations on a geographic information system platform [42], Shannon\u0026rsquo;s entropy model [43], as well as Shannon\u0026rsquo;s entropy model in conjunction with principle component analysis [44-49], to identify and estimate urban areas.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eTo investigate the changes in urban expansion within a specific area of AL-Hilla city over time, researchers utilized satellite imagery classified using the Mahalanobis distance classifier. The results were subsequently divided into five categories. The images employed in this study were obtained from different sensors, capturing data in the years 2002, 2011, and 2022, respectively. The researchers achieved an accuracy rate of approximately 85.94%, 89.82%, and 93.67% for the images from 2002, 2011, and 2022, respectively. The results indicated a notable increase in the urban area of AL-Hilla city, expanding from 33.40 km\u0026sup2; in 2002 to approximately 89.16 km\u0026sup2; in 2022, marking a surge of around 38.45% over the last two decades. The urban expansion occurred in two distinct stages: a High-Rate phase from 2002 to 2011, followed by a Steady-Rate phase from 2011 to 2022. The study identified that concentric urban sprawl was the predominant form of urban growth in AL-Hilla city from 2002 to 2022, predominantly occurring in residential regions, transportation routes, and new system routes. During this period, numerous schools, universities, and offices were established. Horizontal urbanization emerged as the dominant form of expansion throughout the entire period. However, from 2011 to 2022, the principal growth models in AL-Hilla shifted towards urban concentrations. The upswing in infrastructure projects significantly contributed to the expansion, with Vertical-Urban-Expansion taking precedence over the last decade. The findings of this study hold critical implications for urban planners and decision-makers in AL-Hilla city, emphasizing the need for more robust strategies for urban sustainability. The study also suggests that future research should explore alternative approaches, such as Shannon\u0026apos;s entropy model, principle component analysis, and Markov algorithm, particularly when working with low-spatial datasets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of the study would like to extend their gratitude to AL-Qasim Green University and Lulea University of Technology (Sweden) for the support that they received to complete this research. Their assistance and resources played a vital role in the successful completion of this study, and the authors are thankful for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e The research idea was conceived by HD and AMSA, while HD, and NAA were re-sponsible for developing the methodology. HD, AMSA and NAA handled the software aspect of the study, while HD, and NAA validated the results. HD, AMSA, and NAA conducted the investigation and curated the data. HD and NAA wrote the original draft of the manuscript, while NAA was in charge of the visualization. NAA oversaw the project administration. All authors have reviewed and consented to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Methods, Tables 1\u0026ndash;6 and Figs. 1\u0026ndash;9.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWu, Y., Li, S., \u0026amp; Yu, S. Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China. \u003cem\u003eEnviron. Monit. \u003c/em\u003eAssess. 188, 54 (2016).\u003c/li\u003e\n \u003cli\u003eNitze, I., Guido, G., Benjamin M., Jones, Vladimir, E. R., \u0026amp; Julia, B. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun.,. 9, 1, 1-11 (2018).\u003c/li\u003e\n \u003cli\u003eCheng, M., Xiyun J., Lei, S., Josep, P., Lalit, K., Chenwei, N., Tianao, W., Kaihua, L., Wenbin, W., and Xiuliang, J. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing. \u003cem\u003eScientific Data\u003c/em\u003e. 9, ,1, 1-13 (2022).\u003c/li\u003e\n \u003cli\u003eLi, S., Vu, D., Mukesh, K., Phu, N., and Tirtha, B. Mapping the wildland-urban interface in California using remote sensing data. \u003cem\u003eScientific reports\u003c/em\u003e 12, 1, 1-12 (2022).\u003c/li\u003e\n \u003cli\u003eLambin, E. F., Turner, B.L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., Coomes, O.T., Dirzo, R., Fischer, G., Folke, C., et al. The cause of land cover change moving beyond the myths. \u003cem\u003eGlob. Environ. Chang\u003c/em\u003e. 11, 261\u0026ndash;269, (2001)\u003c/li\u003e\n \u003cli\u003eDibs, H.,Mansor, S., Ahmad, N., Al-Ansari, N. Geometric Correction Analysis of Highly Distortion of Near Equatorial Satellite Images Using Remote Sensing and Digital Image Processing Techniques. \u003cem\u003eEng.,\u003c/em\u003e 14, 1,1-8 (2022a).\u003c/li\u003e\n \u003cli\u003eDibs, H., Hasab, H.A., Jaber, H.S., Al-Ansari, N. Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images. Inn. Infrast. Solus. 7,1, 1-4 (2022b).\u003c/li\u003e\n \u003cli\u003eKaranam, H.K., \u0026amp; Neela, V.B. Study of normalized difference built-up (NDBI) index in automatically mapping urban areas from Landsat TN imagery. \u003cem\u003eInt. J. Eng. Sci. Math\u003c/em\u003e. 8, 239\u0026ndash;248 (2017).\u003c/li\u003e \u003cli\u003eDewan, A.M., Kabir, M.H., Nahar, K., Rahman, M.Z. Urbanization and environmental degradation in Dhaka metropolitan area of Bangladesh. \u003cem\u003eInt. J. Environ. Sustain. Dev\u003c/em\u003e. 11, 118\u0026ndash;147 (2012). \u003c/li\u003e\n \u003cli\u003eDibs, H., Hasab, H.A., Mahmoud, A.S., \u0026amp; Al-Ansari, N. Fusion methods and multi-classifiers to improve land cover estimation using remote sensing analysis. Geo. Geol. Eng. 39, 8, 5825-42, (2021).\u003c/li\u003e\n \u003cli\u003eAbbas, Z., \u0026amp; Jaber, H. S. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012166). IOP Publishing, (2020).\u003c/li\u003e\n \u003cli\u003eChen, S., Zeng, S., \u0026amp; Xie, C. Remote sensing and GIS for urban growth analysis in China. Photogramm.\u003cem\u003e Eng. Remote Sens. J\u003c/em\u003e. 66, 593\u0026ndash;598 (2000). \u003c/li\u003e\n \u003cli\u003eLi, X., and Yeh, A.G.O. Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. \u003cem\u003eLandsc. Urban Plane J\u003c/em\u003e. 64, 67\u0026ndash;76 (2004). \u003c/li\u003e\n \u003cli\u003eLiu, H., Weng, Q. Scaling-up effect on the relationship between landscape pattern and land surface temperature. Photogramm. \u003cem\u003eEng. Remote Sens. J.\u003c/em\u003e 75, 291\u0026ndash;304 (2009). \u003c/li\u003e\n \u003cli\u003eAndersson, E. Urban landscapes and sustainable cities. Eco. and soc. 11.1 (2006).\u003c/li\u003e\n \u003cli\u003eZhou, N., Zhao, S. Urbanization process and induced environmental geological hazards in China. \u003cem\u003eNat. Hazard\u003c/em\u003e. 67, 797\u0026ndash;810 (2013). \u003c/li\u003e\n \u003cli\u003eZhou, D., Shi, P., Wu, X., Ma, J., and Yu, J. Effects of urbanization expansion on landscape pattern and region ecological risk in Chinese coastal city: A case study of Yantai city. \u003cem\u003eSci. World J\u003c/em\u003e. 1, (2014).\u003c/li\u003e\n\u003cli\u003eBhatta, B. Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India.Int. J. Remote Sens. 30, 4733\u0026ndash;4746 (2009). \u003c/li\u003e\n \u003cli\u003eBatisani, N., Yarnal, B. Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations. Appl. Geogr. 29, 235\u0026ndash;249 (2009). \u003c/li\u003e\n \u003cli\u003eAl-Bilbisi, H., \u0026amp; Tateishi, R. Using satellite remote sensing data to detect land use/cover changes and to monitor land degradation in central Jordan. \u003cem\u003eJ. Jpn. Soc. Photogramm. Remote Sens.\u003c/em\u003e 42, 4\u0026ndash;18 (2003). \u003c/li\u003e\n \u003cli\u003eSoffianian, A., Ahmadiadoushan, M., Yaghmael, L., \u0026amp; Falahatkar, S. Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran. \u003cem\u003eWorld Appl. Sci. J. \u003c/em\u003e9, 1370\u0026ndash;1378 (2010).\u003c/li\u003e\n \u003cli\u003eAl-Bilbisi, H. H. Land use/cover change detection in arid and semi-arid areas of Northeastern Jordan using Landsat images. \u003cem\u003eJordan J. Soc. Sci.\u003c/em\u003e 10, 265\u0026ndash;275 (2017).\u003c/li\u003e\n \u003cli\u003ePirnazar, M., Ali-Askari, K., Eslamian, S., Singh, V., Dalezios, N., Ghane, M., and Qasemi, Z. Change detection of urban land use and erban expansion using GIS and RS, case study: Zanjan Province, Iran. \u003cem\u003eInt. J. Constr. Civ. Eng\u003c/em\u003e. 4, 23\u0026ndash;38 (2018). \u003c/li\u003e\n \u003cli\u003eWeng, Q. Land use change analysis in Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. \u003cem\u003eJ. Environ. Manag\u003c/em\u003e. 64, 273\u0026ndash;284 (2002). \u003c/li\u003e\n \u003cli\u003eAl-Bilbisi, H. A two decades land use/cover change detection and land degradation monitoring in central Jordan using satellite images. \u003cem\u003eJordan J. Soc. Sci.\u003c/em\u003e 5, 133\u0026ndash;149 (2012).\u003c/li\u003e\n \u003cli\u003eGar-On Y. A., Xia, L. Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm. \u003cem\u003eEng. remote Sen\u003c/em\u003e. 67 ,1, 83-90 (2001).\u003c/li\u003e\n \u003cli\u003eLee, J., Tian, L., Erickson, L., Kulikowski, D. Analyzing growth management policies with geographical information systems. \u003cem\u003eEnviron. Plann. B: Plann. Des\u003c/em\u003e. 25, 6, 865\u0026ndash;879 (1998).\u003c/li\u003e\n \u003cli\u003eDibs, H., Al-Hedny, S. Detection wetland dehydration extent with multi-temporal remotely sensed data using remote sensing analysis and GIS techniques. Int. J. of Civil Eng. and Tech.10, 143-54 (2019).\u003c/li\u003e\n \u003cli\u003eDibs, H., Idrees, M.O., Saeidi, V., Mansor, S. Automatic Keypoints Extraction from UAV Image with Refine and Improved Scale Invariant Features Transform (RI-SIFT). Int. J. of Geo. 1,12, 3 (2016).\u003c/li\u003e\n \u003cli\u003eDibs, H. Comparison of derived Indices and unsupervised classification for AL-Razaza Lake dehydration extent using multi-temporal satellite data and remote sensing analysis. \u003cem\u003eJ Eng Appl Sci.\u003c/em\u003e13, 24, 1-8 (2018).\u003c/li\u003e\n \u003cli\u003eMoney, R. I. The Hindiya Barrage, Mesopotamia. The Geo. J. 50, 3, 217-222 (1917).\u003c/li\u003e \u003cli\u003eSchowengerdt, R.A. Remote Sensing: \u003cem\u003eModels and Methods for Image Processing\u003c/em\u003e, (3rd ed., Academic Press), (London, UK, 2006).\u003c/li\u003e\n \u003cli\u003eJensen, J. R. Introductory Digital Image Processing: \u003cem\u003eA Remote Sensing Perspective\u003c/em\u003e, (3rd ed., Prentice Hall: Upper Saddle River), ( NJ, USA, 2005).\u003c/li\u003e\n \u003cli\u003eSingh, A. Digital change detection techniques using remotely-sensed data. \u003cem\u003eInt. J. Remote Sens\u003c/em\u003e. 10, 989\u0026ndash;1003 (1989). \u003c/li\u003e\n \u003cli\u003eYang, X., Lo, C.P. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. \u003cem\u003eInt. J. Remote Sens\u003c/em\u003e. 23, 1775\u0026ndash;1798 (2002).\u003c/li\u003e\n \u003cli\u003eCampbell, J. B., and R. H. Wynne. Introduction to Remote Sensing. Guilford. 123-157. New York, USA, 19872, (1987).\u003c/li\u003e\n \u003cli\u003eXiao, J.Y., Shen, Y.J., Ge, J.F., Tateishi, R., Tang, C.Y., Liang, Y.Q. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc. Urban Plan. J. 75, 69\u0026ndash;80 (2006) \u003c/li\u003e\n \u003cli\u003eDewan, A.M., Yamaguchi, Y., 2009a. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960\u0026ndash;2005. Environ. Monit. Assess. 150, 237\u0026ndash;249. http://dx.doi.org/10.1007/s10661-008-0226-5.\u003c/li\u003e\n \u003cli\u003eJayanth, J., Ashok Kumar, T., Koliwad, S., Krishnashastry, S., 2016. Identification of land cover changes in the coastal area of Dakshina Kannada district, South India during the year 2004\u0026ndash;2008. Egypt. J. Remote Sens. Space Sci. 19, 73\u0026ndash;93. http:// dx.doi.org/10.1016/j.ejrs.2015.09.001.\u003c/li\u003e\n \u003cli\u003eAbbas, Zahraa, and Hussein Sabah Jaber. \u0026quot;Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques.\u0026quot; In IOP Conference Series: Materials Science and Engineering, vol. 745, no. 1, p. 012166. IOP Publishing, 2020.\u003c/li\u003e\n \u003cli\u003eLopez, J.M.R., Heider, K. and Scheffran, J., 2017. Frontiers of urbanization: Identifying and explaining urbanization hot spots in the south of Mexico City using human and remote sensing. Applied Geography, 79, pp.1-10.\u003c/li\u003e\n \u003cli\u003eMosammam, H.M., Nia, J.T., Khani, H., Teymouri, A. and Kazemi, M., 2017. Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. The Egyptian Journal of Remote Sensing and Space Science, 20(1), pp.103-116.\u003c/li\u003e\n \u003cli\u003eDhali, M.K., Chakraborty, M. and Sahana, M., 2019. Assessing spatio-temporal growth of urban sub-centre using Shannon\u0026rsquo;s entropy model and principle component analysis: A case from North 24 Parganas, lower Ganga River Basin, India. The Egyptian Journal of Remote Sensing and Space Science, 22(1), pp.25-35.\u003c/li\u003e\n \u003cli\u003eAl Mashagbah, A., Al-Adamat, R. and Al-Amoush, H., 2012. GIS and remote sensing to investigate urban growth in Mafraq City/Jordan between 1987 and 2010.\u003c/li\u003e\n \u003cli\u003eDibs H, Jaber HS, Al-Ansari N. Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis. Emerging Science Journal.7(4):1215-31, (2023).\u003c/li\u003e\n \u003cli\u003eDibs H, Al-Ansari N, Laue J. Analysis of Remotely Sensed Imagery and Architecture Environment for Modelling 3D Detailed Buildings Using Geospatial Techniques. Engineering,15(05):328-41, (2023).\u003c/li\u003e\n \u003cli\u003eDibs H, Mansor S, Ahmad N, Al-Ansari N. Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis. Engineering. 15(2):75-89, (2023).\u003c/li\u003e\n \u003cli\u003eDibs H, Mansor S, Ahmad N, Al-Ansari N. Geometric correction analysis of highly distortion of near equatorial satellite images using remote sensing and digital image processing techniques. Engineering. 14(01):1-8, (2022)\u003c/li\u003e\n \u003cli\u003eHashim F, Dibs H, Jaber HS. Applying Support Vector Machine Algorithm on Multispectral Remotely Sensed Satellite Image for Geospatial Analysis. InJournal of Physics: Conference Series 2021 Jul 1 (Vol. 1963, No. 1, p. 012110). IOP Publishing.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Datasets-Specifications.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.776859504132233%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatellite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpectral resolution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eresolution (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.669421487603305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProjection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.776859504132233%\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eQuickBird-2\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;June, 2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e5 bands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e1 x 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.669421487603305%\" valign=\"top\"\u003e\n \u003cp\u003eUTM (Zone-38N)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.776859504132233%\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eWorldView-2\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003eJune, 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e8 bands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e0.6 x 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.669421487603305%\" valign=\"top\"\u003e\n \u003cp\u003eUTM (Zone-38N)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.776859504132233%\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eSentinel-2\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003eJune, 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e13 bands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.85123966942149%\" valign=\"top\"\u003e\n \u003cp\u003e10 x 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.669421487603305%\" valign=\"top\"\u003e\n \u003cp\u003eUTM (Zone-38N)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e the selected GCPs\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;28\u0026apos;33.03\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;28\u0026apos;08.35\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;31\u0026apos;10.73\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;27\u0026apos;27.05\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;31\u0026apos;42.42\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;25\u0026apos;34.94\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;31\u0026apos;18.48\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;23\u0026apos;58.57\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;29\u0026apos;29.98\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;23\u0026apos;43.18\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;28\u0026apos;05.89\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;23\u0026apos;23.08\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;26\u0026apos;52.05\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;23\u0026apos;22.64\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;26\u0026apos;40.34\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;28\u0026apos;17.70\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;24\u0026apos;03.89\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;23\u0026apos;27.86\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;24\u0026apos;33.34\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;29\u0026apos;04.27\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;25\u0026apos;52.53\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;25\u0026apos;44.55\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;27\u0026apos;10.51\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;26\u0026apos;27.56\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;28\u0026apos;28.91\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;25\u0026apos;22.34\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.869281045751634%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026deg;30\u0026apos;10.38\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" valign=\"top\"\u003e\n \u003cp\u003e44\u0026deg;25\u0026apos;56.98\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3.\u0026nbsp;\u003c/strong\u003ethe considered classes,\u0026nbsp;and their\u0026nbsp;definitions\u0026nbsp;in\u0026nbsp;this\u0026nbsp;study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003eRoads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003easphalt roads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePlantation Type Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003eUrban Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAll Types of Urban and Building areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003eWater Bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWater-Bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.675675675675677%\" valign=\"top\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSoils\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eindicates the five-classes changes.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.288611544461778%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.196567862714508%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2002 Area\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(km\u003csup\u003e2\u003c/sup\u003e) \u0026nbsp; / (%) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.508580343213728%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2011 Area\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(km\u003csup\u003e2\u003c/sup\u003e) \u0026nbsp;/ \u0026nbsp; \u0026nbsp; \u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.352574102964118%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022 Area\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(km2) \u0026nbsp;/ \u0026nbsp; \u0026nbsp; \u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324492979719189%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Differences in Km\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324492979719189%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Differences in (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.004680187207487%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban and roads areas\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165109034267913%\" valign=\"top\"\u003e\n \u003cp\u003e19.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e13.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e16.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e+ 06.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e+ 04.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.978193146417446%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e+38.45 %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.386491557223266%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.818011257035648%\" valign=\"top\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.25515947467167%\" valign=\"top\"\u003e\n \u003cp\u003e11.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.630393996247655%\" valign=\"top\"\u003e\n \u003cp\u003e34.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.00562851782364%\" valign=\"top\"\u003e\n \u003cp\u003e23.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.00562851782364%\" valign=\"top\"\u003e\n \u003cp\u003e65.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.25515947467167%\" valign=\"top\"\u003e\n \u003cp\u003e45.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.821763602251407%\" valign=\"top\"\u003e\n \u003cp\u003e+ 49.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.821763602251407%\" valign=\"top\"\u003e\n \u003cp\u003e+ 34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Bodies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003e07.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e05.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165109034267913%\" valign=\"top\"\u003e\n \u003cp\u003e04.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e02.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e02.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e01.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 05.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 03.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.978193146417446%\" valign=\"top\"\u003e\n \u003cp\u003e-------\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003e51.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e35.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165109034267913%\" valign=\"top\"\u003e\n \u003cp\u003e43.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e29.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e28.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e19.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 22.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.978193146417446%\" valign=\"top\"\u003e\n \u003cp\u003e-------\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003e52.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e36.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165109034267913%\" valign=\"top\"\u003e\n \u003cp\u003e43.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e30.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\" valign=\"top\"\u003e\n \u003cp\u003e24.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.853582554517134%\" valign=\"top\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 27.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.305295950155763%\" valign=\"top\"\u003e\n \u003cp\u003e- 18.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.978193146417446%\" valign=\"top\"\u003e\n \u003cp\u003e-------\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eAL-Hilla City Annual-Urban-Region-Growth Rates (2002\u0026ndash;2022).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.56716417910448%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Areas (km\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.298507462686565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual-Growth (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.56716417910448%\" valign=\"top\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.298507462686565%\" valign=\"top\"\u003e\n \u003cp\u003e-------\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.56716417910448%\" valign=\"top\"\u003e\n \u003cp\u003e34.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.298507462686565%\" valign=\"top\"\u003e\n \u003cp\u003e11.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.56716417910448%\" valign=\"top\"\u003e\n \u003cp\u003e65.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.298507462686565%\" valign=\"top\"\u003e\n \u003cp\u003e08.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eAL-Hilla\u0026rsquo;s annual urban and roads region growth rate (2002\u0026ndash;2022).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.21875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.28125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban-Area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual-Growth (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.21875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1- \u0026nbsp; \u0026nbsp; \u0026nbsp;2002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.28125%\" valign=\"top\"\u003e\n \u003cp\u003e33.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.5%\" valign=\"top\"\u003e\n \u003cp\u003e-----\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.21875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2- \u0026nbsp; 2011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.28125%\" valign=\"top\"\u003e\n \u003cp\u003e53.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.5%\" valign=\"top\"\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.21875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3- \u0026nbsp; \u0026nbsp; \u0026nbsp;2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.28125%\" valign=\"top\"\u003e\n \u003cp\u003e89.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.5%\" valign=\"top\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Supplementary Methods","content":"\u003cp\u003eSupplementary Methods are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban area, Geospatial dataset, Change Detecting, QuickBird-Imagery, WorldView-Image, Sentinel-Satellite","lastPublishedDoi":"10.21203/rs.3.rs-4148120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4148120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, the city of AL-Hilla in Babylon, Iraq has suffered from the illegal fragmentation of agricultural and orchard lands, leading to their conversion into residential areas. This transformation has had a negative impact on the economic viability of plantation and vegetation lands, affecting the climate and causing an increase in temperatures, winds, and dust storms. This study aims to examine the spatio-temporal dynamics of changes in land-use/land-cover (LU/LC) using different spatial resolutions of satellite images to detect urban sprawl. The present study utilizes a supervised imagery classifier, employing the Mahalanobis distance (MD) technique to produce three distinct LU/LC maps for 2002, 2011, and 2022. The accuracy of the outcomes is assessed using a confusion matrix, and a comparison was made to compute the changes in land categories. The research reveals that the expansion of the urban region in AL-Hilla has significantly increased from 33.40 km² in 2002 to 89.16 km² in 2022, with an Annual Growth Rate of (6.74%) between 2002 and 2011 and 6.14% between 2011 and 2022. The growth in urban area now constitutes 38.45% of the entire city area and has resulted in a decline in other land categories such as water bodies, soil, and vegetation. The study highlights the necessity for effective management and planning strategies to address the adverse impact of urban expansion on the environment and agriculture","manuscriptTitle":"An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 19:40:02","doi":"10.21203/rs.3.rs-4148120/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":"acabc1ff-90c5-4654-b16f-2f22b7afd133","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30359767,"name":"Earth and environmental sciences/Environmental sciences"},{"id":30359768,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2024-10-18T11:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 19:40:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4148120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4148120","identity":"rs-4148120","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}