Identifying the Land Use Land Cover (LULC) Classifications and Change Using Google Earth Engine (GEE): A Case Study of Narayanganj District, Bangladesh

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M. Nazmul Haque, A S M Shanawaz Uddin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4366221/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted 8 You are reading this latest preprint version Abstract Natural landscape has been changing day by day in the cities of developing countries like Bangladesh due to rapid urbanization, industrial development and population growth. The current research focuses on LULC classification and changes of a fastest growing district Narayanganj, Bangladesh over the past twenty years. A soft computing supervised machine learning algorithm have been developed using cloud-based computing platform Google Earth Engine to perform the LULC classification and change detection analysis. Landsat-7 TOA imageries are used with Random Forest classifier to classify the LULC for the five different years (2000, 2005, 2010, 2015 and 2020). LULC change detection analysis between 2000 and 2020 has shown that 88.06% increase in urban area, 70.77% decrease in bare area and 36.72% decrease in water area. Change detection classification map Google Earth Engine Landsat 7 LULC Figures Figure 1 Figure 2 Figure 3 1. Introduction The surface of the earth is used by people mainly for living, cultivation, and transportation. Rivers, forests, and hilly areas are the natural users of the earth's surface. The use of the earth's surface has been altering day by day due to the necessity of people. This alteration to the land surface on earth is referred to as “Land Use and Land Cover (LULC) Change” (Gasirabo et al., 2023 ). Over the last few decades, the LULC has been changed due to population growth and economic development (Gasirabo et al., 2023 ). This population growth and economic improvement tend to expand the number as well as the area of cities. These are considered to be human and natural factors for land use changes. A number of detrimental effects result from LULC change, including deforestation, diminished agriculture and streams, conversion of grassland to urban areas, and Urban Heat Island (UHI). Hence, the factors of LULC change like population growth, rapid urbanization, and industrialization are becoming the major challenges against the sustainable development of an area. Based on the research of Garcia-Ruiz et al. (García-Ruiz et al., 1996 ) and Musa et al. (Ibrahim Musa et al., 2018 ), sustainable development has been affected due to the LULC change. To reduce the interconnected land use challenges in the upcoming future for sustainable development, observation on LULC variations data is very crucial. Hence the LULC monitoring is considered to be an important part of achieving sustainable development against these challenges (Hakim et al., 2020 ). Developing countries in the South Asian regions have faced these challenges frequently (Kotharkar et al., 2018 ). Bangladesh like others developing countries has also faced these challenges now a days. The increased population growth in different cities of Bangladesh is accelerating the reduction of vegetation, water bodies and the increase in urban areas (Rashid et al., 2022 ). The capital city of Bangladesh, Dhaka was experienced a rapid change in LULC between 1975–2003 i.e., an increase in urban areas about 10553 hectares and a substantial reduction in water bodies, vegetation, crop lands and, wetlands (Dewan & Yamaguchi, 2009 ). Due to urbanization, the Chittagong City Corporation faced an increase of built-up area by 13.72% and a reduction in water bodies, fallow land and hilly vegetation areas (Hussain et al., 2016 ). In Rajshahi City Corporation region Kafy et al. (Kafy et al., 2020 ) showed that there was an increase of urban area almost about 16% since 1999 and a decrease of vegetation (19%) and water bodies (4%), which leads to an increase of average Land Surface Temperature (LST) by 9.83°C. On the same way, Narayanganj is considered to be a potential area in Bangladesh for its rapid industrialization (Rashid et al., 2022 ). Rashid et al. (Rashid et al., 2022 ) evaluated urban heat island effects based on numeric parameters i.e., LULC, land surface temperature (LST), normalized difference vegetation index (NDVI) for Narayanganj city area with increasing LST of 1.8°C during 2011 and 2019. Islam & Haque (Islam & Haque, 2022 ) identify the LULC change on Narayanganj Upazila over the year 2001 and 2021. However, there is no existence of district scale (bigger scale) quantitative analysis of LULC change and most of the previous studies had been conducted based on downscale of Narayanganj district to access the UHI effect so far. Focusing on this background, to achieve a sustainable development, this research aims to quantify the LULC change over last 20 years (2000 ~ 2020) on district scale. LULC change pattern analysis by direct field visits are very time-consuming, laborious and error-prone (Kafy et al., 2020 ). Therefore, various geographic models and publicly available remote sensing data are thought to be highly useful tools for managing and keeping track of the state and evolution of LULC (Gasirabo et al., 2023 ). Now-a-days researchers are using various Geographic Information System (GIS) technologies and machine learning algorithms for the thematic mapping of LULC classification and LULC change (Dr. C. Pande, 2022 ). To classify a LULC image, multiple functions like supervised and unsupervised classification, machine learning programming, fuzzy grading, and Google Earth Engine (GEE) platform are used as the GIS and Remote Sensing (RS) technology (Dr. C. Pande, 2022 ). GEE is promising platform for big geospatial data processing with comprehensive analysis (Dr. C. Pande, 2022 ). Moreover, machine learning approach in the GEE platform is quicker to access, process, and analyze large imagery data within a very few seconds comparing with RS and GIS software.(Dr. C. Pande, 2022 ). GEE is a cloud-based computing platform of Google and was officially released in 2010 with substantial computational facilities (Zhao et al., 2021 ). In this platform, various supervised and unsupervised algorithms are available for LULC classification task. K-means clustering, ISODATA (Iterative Self-Organizing Data Analysis) clustering, artificial neural network, Fuzzy C-means clustering are available unsupervised classification approaches. On the other hand, K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) classifier, maximum likelihood classifier (MLC), minimum distance classifier are available supervised classification approaches (Jangid et al., 2023 ). Among these, Random Forest (RF) classifier is an ensemble classifier (Pal, 2005 ) which provide better accuracy than any other single classifier (Giacinto & Roli, 1997 ). Pal (Pal, 2005 ) suggested that in terms of classification accuracy and training time, the RF classifier is just as effective as SVMs but the RF classifier required a smaller number of user-defined parameters. Hence, the RF supervised classification approach is more widely used by researchers (Amani et al., 2020 ). Also in this study, the RF classifier is used to classify the LULC due to its popularity. 2. Materials and Methodology 2.1. Description of the study area Narayanganj is one of the oldest and most prominent river ports of Bangladesh. The study area as shown in Fig. 1 , is located between 23°33′ and 23°57′ north latitudes and between 90°26′ and 90°45′ east longitudes (B. Statistics & Statistics, n.d.). It was considered to be a “Ganj” because of its prominent place of trade and commerce in the long past (B. B. O. Statistics, 2013 ). It was also referred to as “The Dundee of East” due to its extensive jute markets and jute processing businesses (Rashid et al., 2022 ). It is flanked by Shitalakshya river and became an economic heart of Bangladesh because of its strategic location and friendly business environment (Noman et al., 2016 ). The district has five upazila namely Araihazar, Bandar, Narayanganj Sadar, Rupganj and Sonargaon. Total amount of area covered by the district is about 68437 hectares within which the riverine area is 11146 hectares (B. B. O. Statistics, 2013 ). It has a population of about 3 million (B. B. O. Statistics, 2011 ) with an average density of five thousand people per square kilometer (B. B. O. Statistics, 2013 ). 2.2. Description of data Enhanced Thematic Mapper Plus (ETM+) sensor-based satellite Landsat 7 top of atmosphere (TOA) reflectance image collections for 2000, 2005, 2010, 2015, 2020 were used in this study Table 1 Source and Description of Used Data Year Sensor Type Acquisition Time Resolution Source 2000 Landsat 7 ETM+ 2000-01-01 to 2000-12-31 30 USGS 2005 2005-01-01 to 2005-12-31 2010 2010-01-01 to 2010-12-31 2015 2015-01-01 to 2015-12-31 2020 2020-01-01 to 2020-12-31 to classify the LULC of the study area. Source of the satellite datasets used in this research is listed in Table 1 . Global Administrative Unit Layer (GAUL) 2015 was used to identify the region of interest (ROI). For more accurate classification, three indices namely normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and enhanced vegetation index (EVI) were added to the reduced image (Feng et al., 2022 ). The formula of these indices are as follows: In this study, total five types of LULC namely urban, bare, water, vegetation, and cropland were considered to classify the reduced image. Classification map considering Ground Control Points (GCPs), validation, and area calculation were performed in GEE platform. Information of GCPs is listed in Table 2 . The classified image and calculated area were imported in QGIS and Python respectively for further processing. Table 2 Information of Ground Control Points (GCPs) Land Use Type Year 2000 2005 2010 2015 2020 GCPs Urban 38 20 14 12 15 Bare 40 25 20 31 33 Water 48 27 31 31 31 Vegetation 38 20 30 29 30 Crop land 45 22 26 26 28 Total GCPs 209 114 121 129 137 2.3. Classification methods 2.3.1. Data Processing In this article, the GEE Data Catalog has been used for accessing the imagery datasets. It is free of cost and includes more than forty years of historical imagery and scientific datasets, updated and expanded daily. To process the datasets, GEE Code Editor has been used. It is an interactive environment providing JavaScript code editor for developing the Earth Engine application. The Landsat 7 data was loaded in the environment using an Earth Engine Snippet. It is a collection of images covering the entire earth since May 28, 1990. The collection was filtered by a date range and geometry i.e., region of interest (ROI) and resulted in a relatively small collection of images. The collection was then reduced to a single image by calculating the median of all values at each pixel. Nine bands were selected and three indices were added to the image. Finally, the reduced image was clipped by a geometry (ROI) for LULC classification. The classification procedure and several others steps are shown in Fig. 2 . 2.3.2. LULC Classification, Accuracy Assessment, and Area Calculation To classify the image, RF classifier is utilized which is an ensemble classifier and with the help of a randomly selected subset of samples and variables, it produces multiple decision trees (Belgiu & Drăguţ, 2016 ). The final classification is determined by the voting process of all trees during the classification process (Feng et al., 2022 ). For the classification of LULC, all land use type GCPs for a particular year were merged together and were labelled a property with value for identification. Among these, 60% were used as training GCPs to collect designated features from reduced image for training the RF classifier and the remaining 40% were used as validation GCPs. The classifier generally takes six input parameters such as number of decision trees, number of variables (or features) per split, minimum leaf population, fraction of input to bag per tree, maximum number of leaf nodes in each tree and randomization seed (Amini et al., 2022 ). In this study, the number of decision trees were five hundred (Belgiu & Drăguţ, 2016 ) and other input parameters were as default value of GEE RF classifier. The classifier grew the forest up to the defined number of decision tress i.e., 500. Each tree was generated using randomly selected GCPs (here, 50% of input GCPs) with replacement and it contains node. Each node was split using randomly selected features (here, number of features is square root of input features) until the pure leaf nodes were obtained (Pal, 2005 ). These leaf nodes are the final class (or property) of the decision tree. A new unlabeled data was passed through the all-decision trees to let them vote for a class. The class which gained maximum votes, is the final class of this data. To strengthen the confidence of classified image, a quantitative measure of accuracy is required (Foody, 2002 ). In this study, 40% randomly selected GCPs were used for a particular year to assess the accuracy of the classifier. Many scientists like Pande et. al. [25, 26], Seyam et. al. (Seyam et al., 2023 ), used three famous accuracy measures such as overall accuracy (OA), user accuracy (UA), and producer accuracy (PA) for the classification correctness. Accuracy level for LULC classification have been recommended above 90 % in the previous study for the excelleny and reliability (Gasirabo et al., 2023 ). The accuracy has been calculated using GEE with the help of confusion matrix. Table 3 Accuracy of LULC types and maps in different years LULC Map year User’s Accuracy (%) Producer’s Accuracy (%) Overall Accuracy (%) Urban Bare Water Vegetation Crop land Urban Bare Water Vegetation Crop land 2000 100 100 92.3 100 100 100 100 100 100 91.3 97.8 2005 100 100 100 100 100 100 100 100 100 100 100 2010 100 100 100 92.3 91.6 75 100 100 100 91.6 95.8 2015 100 100 100 100 85.7 100 100 100 92.8 100 97.8 2020 100 94.4 100 91.6 100 80 100 100 100 87.5 96.4 2.3.3. Mapping and change pattern analysis Mapping of the classified images using proper coordinate system and scaling is very important to visualize the result nicely. In this study the classified images were exported as GeoTIFF file from the GEE platform and imported in the QGIS for further analysis. Change detection analysis is important to identify, describe and quantify differences between images of different times or conditions. Many tools such as ArcGIS, QGIS, GEE can be used independently for the change detection analysis. In this study, GEE was used to detect change patterns because of its simplicity and free-of-cost. 3. Results and Discussion 3.1. LULC Dynamics Figure 3 shows Narayanganj district’s spatial land cover dynamics for the years 2000, 2005, 2010, 2015, and 2020 with high accuracy (Table 3 ) and individual color ramp representing distinct land cover. The figure shows significant increase in urban land cover from 2000 to 2015 at around 23°38′N and 90°30′E. According to Fig. 3, there is a clearly observable urbanization process around 23°38′N and 90°30′E. From 2000 to 2015, urban land cover percentage increases from 3.7 to 13.9 of the overall land as reported in Table 4 . Moreover, multiplication of vegetation cover is dominated among other land uses throughout the study period and conspicuous change is observed in the central part of Narayanganj district in Fig. 3. Table 4 shows that vegetation cover comprises 24.3% of the overall land in 2000 whereas it reaches 42.4% in 2020. From Fig. 3 it is clear that, the large amount of crop area is converted into vegetation area but the amount of overall cropland area is almost unchanged in 2000 and 2020 (Table 4 ). In 2000, the amount of crop area was about 30000 hectares (Table 4 ) and among which about 11000 hectares (Table 5 ) are converted into vegetation areas in 2020. This remarkable change indicates the land-filling process of crop area. However, reported attenuation in water body and bare land during 2000–2020 as shown in Fig. 3 and most of which converted into cropland and vegetation area. Hence, Narayanganj district’s crop land change over the study period is not momentous. Table 4 Areas and percentages of different LULC types for different years LULC type Area (ha) % of total land (75852.66 ha) 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 Urban 2770.76 4939.624 5309.78 10568.64 5212.14 3.7 6.5 7 13.9 6.9 Bare 15322.1 4295.684 6723.61 6587.64 4478.47 20.2 5.7 8.9 8.7 5.9 Water 10064 4723.272 4803.39 6053.83 6368.69 13.3 6.2 6.3 8 8.4 Vegetation 18431.3 29812.79 45521.14 26052.14 32194.94 24.3 39.3 60 34.3 42.4 Crop land 29264.5 32081.29 13494.74 26590.41 27598.42 38.6 42.3 17.8 35.1 36.4 3.2. LULC Change Detection from 2000 to 2020 In this study, classification map in 2000 was considered as reference map. From the analysis the unchanged area was observed as 32229 hectares. The urban and vegetation area were increased by the amount of 2441 and 13763 hectares respectively whereas the bare land, water-body and crop land area were decreased by the amount of 10843, 3696 and 1665 hectares respectively. Bare, water, vegetation and crop land areas in 2000 were converted into urban area in 2020 by the amount of 1250, 363, 774 and 1842 hectares respectively. On the other hand, bare area in 2000 was converted into urban, water, vegetation and crop land area by the amount of 1250, 87, 8393 and 4640 hectares respectively. Other transition values are listed in the Table 5 . Table 5 LULC transition matrix from year 2000 to 2020 2000 Total Urban Bare Water Vegetation Crop 2020 Urban 984 a) 1250 363 774 1842 5213 Bare 498 952 a) 617 734 1678 4479 Water 125 87 5436 a) 67 653 6368 Vegetation 621 8393 838 11054 a) 11288 32194 Crop 544 4640 2810 5802 13803 a) 27599 Total 2772 15322 10064 18431 29264 a) (Unchanged area) 4. Conclusion Over twenty years, the LULC classification map was observed for the year 2000, 2005, 2010, 2015 and 2020. There was an increasing trend of urban area and vegetation of about 88.06% and 74.64% respectively, whereas decreasing trend of bare land, crop land and waterbody of 70.77%, 36.72% and 5.69% respectively in between 2000 to 2020. This comprehensive study shows that abrupt change in LULC which will further create pressure on environment, climate and ecosystem services. The findings would be beneficial for the policy makers, project planners as well as city developers to manage the LULC in an effective and sustainable way. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by S. M. Nazmul Haque and A S M Shanawaz Uddin. The first draft of the manuscript was written by S. M. Nazmul Haque and other author commented on previous versions of the manuscript. Both authors read and approved the final manuscript. Data Availability The datasets used for analysis in the current study are available in the GEE Data Catalog repository, [https://developers.google.com/earth-engine/datasets/]. References Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Alizadeh Moghaddam, S., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 , 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052 Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban land use and land cover change analysis using random forest classification of landsat time series. 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Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 13 Oct, 2024 Reviews received at journal 04 Jun, 2024 Reviewers agreed at journal 10 May, 2024 Reviewers agreed at journal 10 May, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 05 May, 2024 Submission checks completed at journal 05 May, 2024 First submitted to journal 03 May, 2024 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-4366221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308160956,"identity":"b5be44d4-7cab-4662-852a-3649a9e3c1a3","order_by":0,"name":"S. M. Nazmul Haque","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYNCCAiBmb4ByDhClxQCIeUBKE0jSIpFApBZz9uNPN/MYMMjzSz5+JvnzB4Mc340E/FosexLSbgO1GM6cnWYmzZPAYCxJSIvBgYRjIC0JBrcTzKSBDkvcQFDL+YdtYC32N49/k/yRwFBPWMuNZDaILRI8ZhJAhyUYENbyjO3mHAMJwxlncoqtedIkDGeeeUDIYenPbrypsJHnbz++8eYPGxt5vuMEbIECCQzGKBgFo2AUjAJKAAC5p0D5BWxGEQAAAABJRU5ErkJggg==","orcid":"","institution":"Ahsanullah University of Science and Technology (AUST)","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"M. Nazmul","lastName":"Haque","suffix":""},{"id":308160957,"identity":"5691f026-cded-4477-8664-31f2025396ce","order_by":1,"name":"A S M Shanawaz Uddin","email":"","orcid":"","institution":"Erasmus Mundus Joint Masters Student in CoastHazar","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"S M Shanawaz","lastName":"Uddin","suffix":""}],"badges":[],"createdAt":"2024-05-04 00:42:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4366221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4366221/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05340-8","type":"published","date":"2025-01-10T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57874773,"identity":"cb36cc06-50a5-4a9e-9e06-5c76682ac838","added_by":"auto","created_at":"2024-06-06 18:48:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202667,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Narayanganj District, Bangladesh (Study Area)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4366221/v1/acfc7b7a6dc98b4ed6f95483.png"},{"id":57873486,"identity":"a4d59f23-cfd2-4ed3-a0b2-7c6b128fa095","added_by":"auto","created_at":"2024-06-06 18:40:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94693,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of LULC mapping, validation and area calculation\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4366221/v1/60575e4f3e549a27bc2dd0c7.jpeg"},{"id":57873489,"identity":"baaae3fc-e32a-4ded-83a3-3d0eb5098c2b","added_by":"auto","created_at":"2024-06-06 18:40:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":716674,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classification map in different years\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4366221/v1/fbba494fbb1ae0e7da4ada0c.png"},{"id":73693795,"identity":"4ab42d3d-7e36-40fb-9942-a12d4dd61f45","added_by":"auto","created_at":"2025-01-13 16:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1877087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4366221/v1/c325ad70-ed90-41a6-b802-b11ac87c60b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying the Land Use Land Cover (LULC) Classifications and Change Using Google Earth Engine (GEE): A Case Study of Narayanganj District, Bangladesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe surface of the earth is used by people mainly for living, cultivation, and transportation. Rivers, forests, and hilly areas are the natural users of the earth's surface. The use of the earth's surface has been altering day by day due to the necessity of people. This alteration to the land surface on earth is referred to as \u0026ldquo;Land Use and Land Cover (LULC) Change\u0026rdquo; (Gasirabo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Over the last few decades, the LULC has been changed due to population growth and economic development (Gasirabo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This population growth and economic improvement tend to expand the number as well as the area of cities. These are considered to be human and natural factors for land use changes. A number of detrimental effects result from LULC change, including deforestation, diminished agriculture and streams, conversion of grassland to urban areas, and Urban Heat Island (UHI). Hence, the factors of LULC change like population growth, rapid urbanization, and industrialization are becoming the major challenges against the sustainable development of an area. Based on the research of Garcia-Ruiz et al. (Garc\u0026iacute;a-Ruiz et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Musa et al. (Ibrahim Musa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), sustainable development has been affected due to the LULC change. To reduce the interconnected land use challenges in the upcoming future for sustainable development, observation on LULC variations data is very crucial. Hence the LULC monitoring is considered to be an important part of achieving sustainable development against these challenges (Hakim et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDeveloping countries in the South Asian regions have faced these challenges frequently (Kotharkar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Bangladesh like others developing countries has also faced these challenges now a days. The increased population growth in different cities of Bangladesh is accelerating the reduction of vegetation, water bodies and the increase in urban areas (Rashid et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The capital city of Bangladesh, Dhaka was experienced a rapid change in LULC between 1975\u0026ndash;2003 i.e., an increase in urban areas about 10553 hectares and a substantial reduction in water bodies, vegetation, crop lands and, wetlands (Dewan \u0026amp; Yamaguchi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Due to urbanization, the Chittagong City Corporation faced an increase of built-up area by 13.72% and a reduction in water bodies, fallow land and hilly vegetation areas (Hussain et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In Rajshahi City Corporation region Kafy et al. (Kafy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that there was an increase of urban area almost about 16% since 1999 and a decrease of vegetation (19%) and water bodies (4%), which leads to an increase of average Land Surface Temperature (LST) by 9.83\u0026deg;C. On the same way, Narayanganj is considered to be a potential area in Bangladesh for its rapid industrialization (Rashid et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rashid et al. (Rashid et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated urban heat island effects based on numeric parameters i.e., LULC, land surface temperature (LST), normalized difference vegetation index (NDVI) for Narayanganj city area with increasing LST of 1.8\u0026deg;C during 2011 and 2019. Islam \u0026amp; Haque (Islam \u0026amp; Haque, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identify the LULC change on Narayanganj Upazila over the year 2001 and 2021. However, there is no existence of district scale (bigger scale) quantitative analysis of LULC change and most of the previous studies had been conducted based on downscale of Narayanganj district to access the UHI effect so far. Focusing on this background, to achieve a sustainable development, this research aims to quantify the LULC change over last 20 years (2000\u0026thinsp;~\u0026thinsp;2020) on district scale.\u003c/p\u003e \u003cp\u003eLULC change pattern analysis by direct field visits are very time-consuming, laborious and error-prone (Kafy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, various geographic models and publicly available remote sensing data are thought to be highly useful tools for managing and keeping track of the state and evolution of LULC (Gasirabo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Now-a-days researchers are using various Geographic Information System (GIS) technologies and machine learning algorithms for the thematic mapping of LULC classification and LULC change (Dr. C. Pande, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To classify a LULC image, multiple functions like supervised and unsupervised classification, machine learning programming, fuzzy grading, and Google Earth Engine (GEE) platform are used as the GIS and Remote Sensing (RS) technology (Dr. C. Pande, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). GEE is promising platform for big geospatial data processing with comprehensive analysis (Dr. C. Pande, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, machine learning approach in the GEE platform is quicker to access, process, and analyze large imagery data within a very few seconds comparing with RS and GIS software.(Dr. C. Pande, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGEE is a cloud-based computing platform of Google and was officially released in 2010 with substantial computational facilities (Zhao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this platform, various supervised and unsupervised algorithms are available for LULC classification task. K-means clustering, ISODATA (Iterative Self-Organizing Data Analysis) clustering, artificial neural network, Fuzzy C-means clustering \u003c/p\u003e \u003cp\u003eare available unsupervised classification approaches. On the other hand, K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) classifier, maximum likelihood classifier (MLC), minimum distance classifier are available supervised classification approaches (Jangid et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among these, Random Forest (RF) classifier is an ensemble classifier (Pal, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) which provide better accuracy than any other single classifier (Giacinto \u0026amp; Roli, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Pal (Pal, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) suggested that in terms of classification accuracy and training time, the RF classifier is just as effective as SVMs but the RF classifier required a smaller number of user-defined parameters. Hence, the RF supervised classification approach is more widely used by researchers (Amani et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also in this study, the RF classifier is used to classify the LULC due to its popularity.\u003c/p\u003e"},{"header":"2. Materials and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e \u003cp\u003eNarayanganj is one of the oldest and most prominent river ports of Bangladesh. The study area as shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e, is located between 23\u0026deg;33\u0026prime; and 23\u0026deg;57\u0026prime; north latitudes and between 90\u0026deg;26\u0026prime; and 90\u0026deg;45\u0026prime; east longitudes (B. Statistics \u0026amp; Statistics, n.d.). It was considered to be a \u0026ldquo;Ganj\u0026rdquo; because of its prominent place of trade and commerce in the long past (B. B. O. Statistics, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It was also referred to as \u0026ldquo;The Dundee of East\u0026rdquo; due to its extensive jute markets and jute processing businesses (Rashid et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is flanked by Shitalakshya river and became an economic heart of Bangladesh because of its strategic location and friendly business environment (Noman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The district has five upazila namely Araihazar, Bandar, Narayanganj Sadar, Rupganj and Sonargaon. Total amount of area covered by the district is about 68437 hectares within which the riverine area is 11146 hectares (B. B. O. Statistics, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It has a population of about 3\u0026nbsp;million (B. B. O. Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with an average density of five thousand people per square kilometer (B. B. O. Statistics, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Description of data\u003c/h2\u003e \u003cp\u003eEnhanced Thematic Mapper Plus (ETM+) sensor-based satellite Landsat 7 top of atmosphere (TOA) reflectance image collections for 2000, 2005, 2010, 2015, 2020 were used in this study\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSource and Description of Used Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcquisition Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLandsat 7 ETM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000-01-01 to 2000-12-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2005-01-01 to 2005-12-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010-01-01 to 2010-12-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015-01-01 to 2015-12-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020-01-01 to 2020-12-31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eto classify the LULC of the study area. Source of the satellite datasets used in this research is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Global Administrative Unit Layer (GAUL) 2015 was used to identify the region of interest (ROI). For more accurate classification, three indices namely normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and enhanced vegetation index (EVI) were added to the reduced image (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The formula of these indices are as follows:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" height=\"130\" width=\"716\"\u003e\u003c/p\u003e\u003cp\u003eIn this study, total five types of LULC namely urban, bare, water, vegetation, and cropland were considered to classify the reduced image. Classification map considering Ground Control Points (GCPs), validation, and area calculation were performed in GEE platform. Information of GCPs is listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The classified image and calculated area were imported in QGIS and Python respectively for further processing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of Ground Control Points (GCPs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLand Use Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGCPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal GCPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Classification methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Data Processing\u003c/h2\u003e \u003cp\u003eIn this article, the GEE Data Catalog has been used for accessing the imagery datasets. It is free of cost and includes more than forty years of historical imagery and scientific datasets, updated and expanded daily. To process the datasets, GEE Code Editor has been used. It is an interactive environment providing JavaScript code editor for developing the Earth Engine application. The Landsat 7 data was loaded in the environment using an Earth Engine Snippet. It is a collection of images covering the entire earth since May 28, 1990. The collection was filtered by a date range and geometry i.e., region of interest (ROI) and resulted in a relatively small collection of images. The collection was then reduced to a single image by calculating the median of all values at each pixel. Nine bands were selected and three indices were added to the image. Finally, the reduced image was clipped by a geometry (ROI) for LULC classification. The classification procedure and several others steps are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. LULC Classification, Accuracy Assessment, and Area Calculation\u003c/h2\u003e \u003cp\u003eTo classify the image, RF classifier is utilized which is an ensemble classifier and with the help of a randomly selected subset of samples and variables, it produces multiple decision trees (Belgiu \u0026amp; Drăguţ, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The final classification is determined by the voting process of all trees during the classification process (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For the classification of LULC, all land use type GCPs for a particular year were merged together and were labelled a property with value for identification. Among these, 60% were used as training GCPs to collect designated features from reduced image for training the RF classifier and the remaining 40% were used as validation GCPs.\u003c/p\u003e \u003cp\u003eThe classifier generally takes six input parameters such as number of decision trees, number of variables (or features) per split, minimum leaf population, fraction of input to bag per tree, maximum number of leaf nodes in each tree and randomization seed (Amini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, the number of decision trees were five hundred (Belgiu \u0026amp; Drăguţ, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and other input parameters were as default value of GEE RF classifier. The classifier grew the forest up to the defined number of decision tress i.e., 500. Each tree was generated using randomly selected GCPs (here, 50% of input GCPs) with replacement and it contains node. Each node was split using randomly selected features (here, number of features is square root of input features) until the pure leaf nodes were obtained (Pal, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These leaf nodes are the final class (or property) of the decision tree. A new unlabeled data was passed through the all-decision trees to let them vote for a class. The class which gained maximum votes, is the final class of this data.\u003c/p\u003e \u003cp\u003eTo strengthen the confidence of classified image, a quantitative measure of accuracy is required (Foody, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this study, 40% randomly selected GCPs were used for a particular year to assess the accuracy of the classifier. Many scientists like Pande et. al. [25, 26], Seyam et. al. (Seyam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), used three famous accuracy measures such as overall accuracy (OA), user accuracy (UA), and producer accuracy (PA) for the classification correctness. Accuracy level for LULC classification have been recommended above 90 % in the previous study for the excelleny and reliability (Gasirabo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The accuracy has been calculated using GEE with the help of confusion matrix.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy of LULC types and maps in different years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC Map year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUser\u0026rsquo;s Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eProducer\u0026rsquo;s Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e97.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Mapping and change pattern analysis\u003c/h2\u003e \u003cp\u003eMapping of the classified images using proper coordinate system and scaling is very important to visualize the result nicely. In this study the classified images were exported as GeoTIFF file from the GEE platform and imported in the QGIS for further analysis.\u003c/p\u003e \u003cp\u003eChange detection analysis is important to identify, describe and quantify differences between images of different times or conditions. Many tools such as ArcGIS, QGIS, GEE can be used independently for the change detection analysis. In this study, GEE was used to detect change patterns because of its simplicity and free-of-cost.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. LULC Dynamics\u003c/h2\u003e \u003cp\u003eFigure 3 shows Narayanganj district\u0026rsquo;s spatial land cover dynamics for the years 2000, 2005, 2010, 2015, and 2020 with high accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and individual color ramp representing distinct land cover. The figure shows significant increase in urban land cover from 2000 to 2015 at around 23\u0026deg;38\u0026prime;N and 90\u0026deg;30\u0026prime;E. According to Fig.\u0026nbsp;3, there is a clearly observable urbanization process around 23\u0026deg;38\u0026prime;N and 90\u0026deg;30\u0026prime;E. From 2000 to 2015, urban land cover percentage increases from 3.7 to 13.9 of the overall land as reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Moreover, multiplication of vegetation cover is dominated among other land uses throughout the study period and conspicuous change is observed in the central part of Narayanganj district in Fig.\u0026nbsp;3. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that vegetation cover comprises 24.3% of the overall land in 2000 whereas it reaches 42.4% in 2020. From Fig.\u0026nbsp;3 it is clear that, the large amount of crop area is converted into vegetation area but the amount of overall cropland area is almost unchanged in 2000 and 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In 2000, the amount of crop area was about 30000 hectares (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and among which about 11000 hectares (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) are converted into vegetation areas in 2020. This remarkable change indicates the land-filling process of crop area. However, reported attenuation in water body and bare land during 2000\u0026ndash;2020 as shown in Fig.\u0026nbsp;3 and most of which converted into cropland and vegetation area. Hence, Narayanganj district\u0026rsquo;s crop land change over the study period is not momentous.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAreas and percentages of different LULC types for different years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003e% of total land (75852.66 ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2770.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4939.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5309.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10568.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5212.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15322.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4295.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6723.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6587.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4478.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4723.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4803.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6053.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6368.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18431.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29812.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45521.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26052.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32194.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29264.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32081.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13494.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26590.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27598.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. LULC Change Detection from 2000 to 2020\u003c/h2\u003e \u003cp\u003eIn this study, classification map in 2000 was considered as reference map. From the analysis the unchanged area was observed as 32229 hectares. The urban and vegetation area were increased by the amount of 2441 and 13763 hectares respectively whereas the bare land, water-body and crop land area were decreased by the amount of 10843, 3696 and 1665 hectares respectively. Bare, water, vegetation and crop land areas in 2000 were converted into urban area in 2020 by the amount of 1250, 363, 774 and 1842 hectares respectively. On the other hand, bare area in 2000 was converted into urban, water, vegetation and crop land area by the amount of 1250, 87, 8393 and 4640 hectares respectively. Other transition values are listed in the Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC transition matrix from year 2000 to 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCrop\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e984\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1250\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e774\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1842\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5213\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e952\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e617\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e734\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1678\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4479\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5436\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6368\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8393\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11054\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11288\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4640\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2810\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5802\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13803\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27599\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea)\u003c/sup\u003e (Unchanged area)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOver twenty years, the LULC classification map was observed for the year 2000, 2005, 2010, 2015 and 2020. There was an increasing trend of urban area and vegetation of about 88.06% and 74.64% respectively, whereas decreasing trend of bare land, crop land and waterbody of 70.77%, 36.72% and 5.69% respectively in between 2000 to 2020. This comprehensive study shows that abrupt change in LULC which will further create pressure on environment, climate and ecosystem services. The findings would be beneficial for the policy makers, project planners as well as city developers to manage the LULC in an effective and sustainable way.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;have\u0026nbsp;no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors\u0026nbsp;contributed\u0026nbsp;to the study conception and design. Material preparation, data collection and analysis were performed by S. M. Nazmul Haque and A S M Shanawaz Uddin. The first draft of the manuscript was written by S. M. Nazmul Haque and other author commented on previous versions of the manuscript. Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets used for analysis in the current study are available in the GEE Data Catalog repository, [https://developers.google.com/earth-engine/datasets/].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Alizadeh Moghaddam, S., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., \u0026amp; Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. \u003cem\u003eIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 5326\u0026ndash;5350. https://doi.org/10.1109/JSTARS.2020.3021052\u003c/li\u003e\n\u003cli\u003eAmini, S., Saber, M., Rabiei-Dastjerdi, H., \u0026amp; Homayouni, S. (2022). 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Progress and Trends in the Application of Google Earth and Google Earth Engine. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 3778. https://doi.org/10.3390/rs13183778\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Change detection, classification map, Google Earth Engine, Landsat 7, LULC","lastPublishedDoi":"10.21203/rs.3.rs-4366221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4366221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNatural landscape has been changing day by day in the cities of developing countries like Bangladesh due to rapid urbanization, industrial development and population growth. The current research focuses on LULC classification and changes of a fastest growing district Narayanganj, Bangladesh over the past twenty years. A soft computing supervised machine learning algorithm have been developed using cloud-based computing platform Google Earth Engine to perform the LULC classification and change detection analysis. Landsat-7 TOA imageries are used with Random Forest classifier to classify the LULC for the five different years (2000, 2005, 2010, 2015 and 2020). LULC change detection analysis between 2000 and 2020 has shown that 88.06% increase in urban area, 70.77% decrease in bare area and 36.72% decrease in water area.\u003c/p\u003e","manuscriptTitle":"Identifying the Land Use Land Cover (LULC) Classifications and Change Using Google Earth Engine (GEE): A Case Study of Narayanganj District, Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 18:40:18","doi":"10.21203/rs.3.rs-4366221/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-13T16:03:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-04T18:31:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177243948101026867704154423795368079383","date":"2024-05-10T10:41:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269691165751542721800089286603814640381","date":"2024-05-10T06:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T06:19:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-05T22:12:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-05T22:12:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-05-04T00:40:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"07fbf5f3-0879-410f-9e39-fe09240e7831","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T15:59:07+00:00","versionOfRecord":{"articleIdentity":"rs-4366221","link":"https://doi.org/10.1007/s00704-024-05340-8","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-01-10 15:56:59","publishedOnDateReadable":"January 10th, 2025"},"versionCreatedAt":"2024-06-06 18:40:18","video":"","vorDoi":"10.1007/s00704-024-05340-8","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05340-8","workflowStages":[]},"version":"v1","identity":"rs-4366221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4366221","identity":"rs-4366221","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}