Reviewing the present landscape scenario in Suri subdivision, Birbhum District, West Bengal, using the land use and land cover map

Reviewing the present landscape scenario in Suri subdivision, Birbhum District, West Bengal, using the land use and land cover map | 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 Short Report Reviewing the present landscape scenario in Suri subdivision, Birbhum District, West Bengal, using the land use and land cover map Pritam Ghosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8723388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land use and land cover classification mapping, using Geographic Information Systems (GIS) and remote sensing, is a systematic process of identifying and mapping different types of surface features and human activities on the Earth's surface with the help of satellite images and spatial analysis tools. This study focuses on examining the existing land cover conditions of the Suri sub-division through the application of satellite data and GIS techniques, to understand current land characteristics and determine future land use requirements. The study particularly focused on interpreting the spatial features in the study area through satellite data. The obtained maps have been studied to explore the spatial characteristics in the studied area. The main objective of the image processing was to accurately extract the built-up coverage of the Study Area for the year 2025. The supervised image classification with the maximum likelihood algorithm method was very useful for this type of classification. Five land use & land cover classes were identified in the false colour composite of the satellite images. The result shows that LULC mapping is perfect and precisely represents the current landscape situation of this study area. Geographic Information Systems Algorithm Landscape Scenario Suri Remote Sensing and Geographic Information System Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background of the study Land, a limited resource, that utilizing in various purposes(Best, 2024). Geographically, land was the upper part of the earth, where man and other animals performed various activities for living on the planet(Rahman, 2023). So it not only considers surface resources but also plays a crucial role in resources that are located under the surface of the earth, and man used this(Upadhyay, 2025). Human activities and natural forces cause morphological changes on the surface of the Earth(Qiu et al., 2024). All these changes are major driving forces for biogeochemical cycles, climate change, and regional to global food production(Wang et al., 2025). Land cover and its use represent human behavior and their needs(Henderson & Loreau, 2023). Physical and biological elements(Forest, grassland, water bodies, bare soil, or rocks) are a type of land cover that was created by natural phenomena(Ramos, Sappa, 2024). Various forms, such as vegetation, grassland, scrub, water bodies, bare soil, etc., were naturally located and are called land cover(Wang & Mountrakis, 2023). Land use is a specific type of use that represents the characteristics of the land and its capabilities(Mustaquim, 2024). Therefore, land cover is a natural element created by natural processes, and land use is the use of those natural parts by humans according to their needs(Hussein, 2023). Apparently, land use means the modification of the land cover and its management (Bozkurt et al.,2023). Land use and land cover classification mapping helps to understand the present condition of socio-economic elements in a particular space and presents the contemporary situation of the natural landscape in this space(Deka, Chowdhury, Saha, 2024). The land use and land cover map can be used to assess natural and socio-economic factors(Ologunde et al., 2025). The classification helps planners, geographers, and other field researchers and administrators to understand the existing pattern of land utilization, assess the suitability of land for different activities, and make sustainable development(Mehari Genovese, 2023). These maps can help in making some sustainable decisions to ensure the development of specific regions(Jadhav et al.,2024). And also environmental conservation, resource management, and minimizing land use conflicts( Kalogiannidis et al., 2023). Land use and land cover classification using GIS and remote sensing is a systematic process of identifying and mapping different types of surface features and human activities on the Earth’s surface with the help of satellite images and spatial analysis tools. Remote sensing provides large amounts of data on the objects that are situated on the Earth’s surface, and GIS visualizes to us (Zahoor, 2023). Through techniques like image interpretation, digital image classification, and change detection, this method helps in understanding spatial distribution and temporal changes in land use and land cover(Bai et al., 2023). This LULC map is widely used for developmental work, such as in areas such as development, urban planning, agricultural management, decision-making, etc.(Kalfas et al., 2023). Land use and Land cover classification maps created by using GIS and Remote sensing are divided into two major types, respectively, with their techniques(Sameer & Hamid, 2023). Such as supervised classification and unsupervised classification. Supervised classification is a remote sensing techniques in which the analyst select known sample areas for each land use/land cover class, and the classification algorithm then assigns every pixel in the image to one of these predefined classes based on their spectral characteristic. In this case major six number of algorithms are used, such as Maximum Likelihood Classification (MLC), Minimum Distance(MD), Parallelepiped Classification(PC), Support Vector Machine(SVM), Random Forest(RF), and Artificial Network(AN)(Avcı et al., 2023). Unsupervised classification automatically clusters the pixels without any sample or training data. In this case, K-Means clustering is a major classification algorithm, and another one is ISODATA(Iterative Self-organizing Data Analysis Techniques)(Lv & Liu, 2023). This study focuses on examining the existing land cover conditions of the Suri sub-division through the application of remote sensing data and GIS techniques, to understand current land characteristics and determine future land use requirements. Selection of the Study Area Suri Sadar Subdivision is an administrative subdivision of Birbhum District in the state of West Bengal, India. It is also the headquarters of the town of Suri. The latitudinal extension of this area is 23 0 92 / N and the longitudinal extension is 87 0 53 / E (Fig. 1 ). It is one of three subdivisions of the Birbhum District. The subdivision contains 7 community development blocks (Suri-I, Suri-II, Sainthia, Mohammad Bazar, Rajnagar, Khoyrasol). Objective of the study: To study the present land cover of the Suri sub-division using satellite data. To create land cover maps with the help of GIS tools. To understand how different types of land cover are distributed across the area. To find areas that need changes in land use and better planning for sustainable development. Data Source Remote sensing data are very useful in this study because they give a wide overall view, can be collected repeatedly over time, and provide up-to-date information. The Landsat TM and OLI satellite imageries of the Suri subdivision for the year 2025 were acquired from the USGS Earth Explorer( https://earthexplorer.usgs.gov/ ). The spectral details of the satellite images are shown in Table 2 . In image acquisition, the impacts of the sun’s inclination, seasons, and cloud cover. Software Q-GIS 3.34.4 was used for image processing, spatial analysis, and creation of the GIS maps. The obtained maps have been studied to explore the spatial features in the study area. Table 2 SL. NO. BAND NAME WAVE LENGTH(µm) PATH NO. ROW NO. RESOLUTION DATE SERISE BAND 1 Coastal Aerosol(Blue) 0.43–0.45 139 43 30 X 30 METER 3TH NOVEMBER 2025 LANDSAT 9 BAND 2 Blue 0.45–0.51 BAND 3 Green 0.53–0.59 BAND 4 Red 0.64–0.67 BAND 5 NIR 0.85–0.88 BAND 6 SWIR 1 1.57–1.65 BAND 7 SWIR 2 2.11–2.29 Methodology The type of land use leads to an understanding of the economic condition of the landscape. The study particularly focused on interpreting the land use and land cover based on satellite data. Image processing stage The main objective of the image processing was to accurately extract the built-up coverage of the study area for the year 2025. All images were projected to WGS 1984 UTM zone 45 N. The images underwent several basic processing steps, including georeferencing, creating band combinations, mosaicking the images, and clipping them to the study area. Lulc classification Land cover refers to the natural features on the earth that are used by humans(Best, 2024). Generally, Earth's surface has been covered by various land use features such as vegetation, agricultural land, water bodies, and others, which are used by different groups of people; therefore, land cover changes over time. In the present decade, natural land is transforming into built-up or agricultural land. The decrease in land area caused by negative impacts reduces the overall land cover(Best, 2024). The LULC map is created to understand landscape conditions and their changes over time. In this case, the prepared LULC map aims to understand the current state of the landscape in this study area for the year 2025. Supervised image classification using the support vector machine learning (SVM) algorithm was applied to measure land use, as it has proven to be an efficient method among other classifiers(Jozdani et al., 2019). The images were classified into five categories: water bodies, vegetation, agricultural land, built-up land, and fallow land. Definition of lulc classes: Water body: A water body is any natural area on the Earth's surface that is covered with water, either permanently or temporarily. It includes features such as rivers, lakes, ponds, oceans, wetlands, etc. Vegetation: Vegetation is the collective term for all plant life in a particular area or region, including trees, shrubs, grasses, and other plants that grow naturally or are cultivated there. Agricultural land: Agricultural land is land that is used for farming activities, such as growing crops, raising livestock, and related agricultural practices, to produce food, fiber, and other agricultural products. Built-up land: Built-up land is land that is covered by human-made structures and infrastructure, such as houses, buildings, roads, factories, schools, and commercial areas, used for residential, industrial, or urban activities. Fallow land: Fallow land is agricultural land that is left uncultivated for a period of time to restore soil fertility and moisture before it is used again for growing crops. Accuracy assessment: Accuracy assessment is the best method for reducing the error of the land use and land cover map. Researchers explained that 85% minimum accuracy value is acceptable. Here, the user accuracy value is 75.23 to 82.55% to 2025, and the producer accuracy is from 85.62% to 90.39%. Table 3 Category 2025 Total number of samples 100 User accuracy 75.23% Procedure accuracy 85.62% Kappa coefficient 0.78 NDVI: The important indices in RS & GIS are the Normalised Difference Vegetation Index. This tool is used in RS & GIS to determine the health of vegetation. The NDVI is calculated using the formula- (NIR – RED)/(NIR + RED). NDVI value range is -1 to + 1. -1 means the vegetation is sparse or other land use indicates, i. e., water body, built-up land, etc., and the + 1 value represents healthy and dense vegetation. Usually, crops are also considered in this as a type of vegetation. NDWI The other important indices in the Normalised Difference Water Index. This index displays the water bodies on the Earth's surface. Generally, this is a monitoring tool used to detect water on the surface. NDWI is calculated using the formula- (GREEN – NIR)/(GREEN + NIR) NDWI value range is -1 to + 1. Here, -1 represents the other land cover types, i.e., vegetation and also built-up land. The water body represents a value of + 1. Generally, NDWI monitors and detects changes in water bodies on the Earth's surface over time. Result & Discussion Assessment Of Normalised Difference Vegetation Index(NDVI): NDVI analysis played a crucial role in assessing the health of vegetation(Szabo, et al., 2016). NDVI represents the present landscape scenario of the vegetation in an area. Here NDVI analysis shows the present condition of the vegetation health in the Suri subdivision. In these cases, NDVI classes were divided into 5 divisions. At first, in the fallow land, the NDVI value is -1 to 0. The fallow land occupied in this area is 6.24 sq k.m. Generally, water bodies and built-up land are also included in this division. Spread vegetation, representative value is 0 to 0.1. indicate those types of vegetation where trees are situated in wide spaces with gaps. Usually, plants grow with large gaps of bare soil or rock between them. In our study area, sparse vegetation occupied 99.87 sq k.m. The low-density vegetation has an NDVI value of 0.1 to 0.2. The area occupied by low-density vegetation is 1321.99 sq k.m. Moderate dense vegetation amount is 352.90 sq k.m and simultaneously high dense vegetation amount is very low, area is 1.74 sq k.m. Assessment of Normalised Difference Water Index(NDWI): NDWI is widely used in remote sensing and geographic information systems platforms. Usually, NDWI represents the water bodies of the Earth's surface(Szabo, et al., 2016). It helps to determine river, lake, and wetlands, and also flood and drought analysis previously. The NDWI value range is -1 to + 1. -1 represents non-water bodies, including vegetation, and built-up land. And + 1 represents water bodies. Here, NDWI analysis was done to acquire some information about the water resources in our study area. The water bodies occupied only 11.09 sq k.m area(Fig. 4 ). Assessment of Land Use & Land Cover(LULC): Land use and land cover mapping is a very useful tool to understand the landscape scenario in a particular area. It manifested the economic condition and related socio-cultural rituals that happened. Generally, this also represents the natural resources such as vegetation, groundwater, minerals, etc., and so on. This research work was done to know the current situation of land cover and its uses in the Suri subdivision. Here, the land use and land cover classification based on supervised classification techniques was very useful, and the maximum likelihood algorithm is very comfortable for this type of classification. On average, 50 to 70 training samples were selected for each LULC class. These training samples have been selected based on the user's knowledge and Google Earth time series images. Five land use & land cover classes were identified in the false colour composite of the satellite images. LULC in five classes are water body, vegetation, agricultural land, built-up land or settlement, and fallow land. The result of the present research revealed the land use and land cover types of the year 2025. The amount of water body area in this region is 181.07 sq k.m or 10.10%, vegetation is 569.73 sq k.m or 31.79%, agricultural land is 589.81 sq k.m or 32.91%, built-up land or settlement area is 446.92 sq k.m or 24.94% and fallow land is 4.26 sq k.m or 0.23%(Table 4 ). Table 4 LAND USE AND LAND COVER AREA OF SURI SUBDIVISION 2025 TYPES OF LAND USE AREA IN SQ KILOMETER Percentage % WATER BODY 181.0746 10.10564532 VEGETATION 569.7351 31.79651285 AGRICULTURA LAND 589.8195 32.917409 BUILT UP LAND 446.9229 24.94245085 FALLOW LAND 4.2642 0.237981985 TOTAL 1791.8163 100 Discussion Using Landsat images(2025), data were analyzed, and result shows that LULC mapping is perfect and precisely represents the current landscape situation of the Suri subdivision of West Bengal. Generally, NDVI and NDWI indicated the vegetation pattern and water bodies. Here we see that the spread vegetation amount is much higher than moderate and high-density vegetation. So this indicates that here the built-up land is widely spaced. The LULC map can show that the agricultural land here is very high compared to the vegetation. Simultaneously, NDWI represents the present water bodies' condition. Water bodies are comparatively low from other land use; this is a natural element of the land. But the settlement condition indicates that the water bodies have decreased over the past decade. Apparently, says that the settlement was increased over time, and natural land covers are being destroyed for these reasons. Conclusions This study represents the advancement of spatial technology. This technology provided powerful tools for getting some information about the landscape of a specific area(S Steiniger, GJ Hay, 2009). Remote sensing and Geographic Information Systems were able to report the dynamism of land cover elements and land use. For this study, the use of satellite images provided valuable data on the study area. Two types of indices were used to know the specific information, such as NDVI, which represents the vegetation, and NDWI, which means the water bodies scenario(Szabo et al., 2016). These types of index analysis were very helpful for representing the study area’s scenario. The Land Use and Land Cover (LULC) map shows the overall condition. Generally, agricultural land was much higher than other land cover components. The settlement amount represented land highly used in built-up areas, and some natural land cover components were changed into built-up land. Because population data was important evidence for this. In this study area, fallow land is situated in the south-western portion, and settlements are situated widely spaced over the study area. Vegetation is generally abundant in the northeast portion of the study area. Water bodies are spread over the study area. Generally, the Maurakshi River is located in the northern portion, and the Ajoy River is located in the southern portion of the study area. So, the riverside, the water bodies were very dense. Fallow land along the riverside, generally, this is the sandy land. Agricultural land is widely situated from the northwest to the southwest. Finally, this study highlighted the present landscape condition of the study area, and this study helps researchers, urban planners, and administrators to make some developmental decisions. Declarations Acknowledgement The author would also like to acknowledge the USGS for providing the satellite images and other data. The author would like to thank the University of Kalynai’s (DODL) department of Geography for their constant support and for providing the wonderful research platform. And also thank you, my guide Dr. Sanjay Kumar Sadhukhan, for completing the work. References AbdelRahman, M. A. (2023). 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purposes(Best, 2024). Geographically, land was the upper part of the earth, where man and other animals performed various activities for living on the planet(Rahman, 2023). So it not only considers surface resources but also plays a crucial role in resources that are located under the surface of the earth, and man used this(Upadhyay, 2025). Human activities and natural forces cause morphological changes on the surface of the Earth(Qiu et al., 2024). All these changes are major driving forces for biogeochemical cycles, climate change, and regional to global food production(Wang et al., 2025). Land cover and its use represent human behavior and their needs(Henderson \u0026amp; Loreau, 2023).\u003c/p\u003e \u003cp\u003ePhysical and biological elements(Forest, grassland, water bodies, bare soil, or rocks) are a type of land cover that was created by natural phenomena(Ramos, Sappa, 2024). Various forms, such as vegetation, grassland, scrub, water bodies, bare soil, etc., were naturally located and are called land cover(Wang \u0026amp; Mountrakis, 2023). Land use is a specific type of use that represents the characteristics of the land and its capabilities(Mustaquim, 2024). Therefore, land cover is a natural element created by natural processes, and land use is the use of those natural parts by humans according to their needs(Hussein, 2023). Apparently, land use means the modification of the land cover and its management (Bozkurt et al.,2023).\u003c/p\u003e \u003cp\u003eLand use and land cover classification mapping helps to understand the present condition of socio-economic elements in a particular space and presents the contemporary situation of the natural landscape in this space(Deka, Chowdhury, Saha, 2024). The land use and land cover map can be used to assess natural and socio-economic factors(Ologunde et al., 2025). The classification helps planners, geographers, and other field researchers and administrators to understand the existing pattern of land utilization, assess the suitability of land for different activities, and make sustainable development(Mehari Genovese, 2023). These maps can help in making some sustainable decisions to ensure the development of specific regions(Jadhav et al.,2024). And also environmental conservation, resource management, and minimizing land use conflicts( Kalogiannidis et al., 2023).\u003c/p\u003e \u003cp\u003eLand use and land cover classification using GIS and remote sensing is a systematic process of identifying and mapping different types of surface features and human activities on the Earth’s surface with the help of satellite images and spatial analysis tools. Remote sensing provides large amounts of data on the objects that are situated on the Earth’s surface, and GIS visualizes to us (Zahoor, 2023). Through techniques like image interpretation, digital image classification, and change detection, this method helps in understanding spatial distribution and temporal changes in land use and land cover(Bai et al., 2023). This LULC map is widely used for developmental work, such as in areas such as development, urban planning, agricultural management, decision-making, etc.(Kalfas et al., 2023).\u003c/p\u003e \u003cp\u003eLand use and Land cover classification maps created by using GIS and Remote sensing are divided into two major types, respectively, with their techniques(Sameer \u0026amp; Hamid, 2023). Such as supervised classification and unsupervised classification. Supervised classification is a remote sensing techniques in which the analyst select known sample areas for each land use/land cover class, and the classification algorithm then assigns every pixel in the image to one of these predefined classes based on their spectral characteristic. In this case major six number of algorithms are used, such as Maximum Likelihood Classification (MLC), Minimum Distance(MD), Parallelepiped Classification(PC), Support Vector Machine(SVM), Random Forest(RF), and Artificial Network(AN)(Avcı et al., 2023). Unsupervised classification automatically clusters the pixels without any sample or training data. In this case, K-Means clustering is a major classification algorithm, and another one is ISODATA(Iterative Self-organizing Data Analysis Techniques)(Lv \u0026amp; Liu, 2023).\u003c/p\u003e \u003cp\u003eThis study focuses on examining the existing land cover conditions of the Suri sub-division through the application of remote sensing data and GIS techniques, to understand current land characteristics and determine future land use requirements.\u003c/p\u003e\n\u003ch3\u003eSelection of the Study Area\u003c/h3\u003e\n\u003cp\u003eSuri Sadar Subdivision is an administrative subdivision of Birbhum District in the state of West Bengal, India. It is also the headquarters of the town of Suri. The latitudinal extension of this area is 23\u003csup\u003e0\u003c/sup\u003e92\u003csup\u003e/\u003c/sup\u003e N and the longitudinal extension is 87\u003csup\u003e0\u003c/sup\u003e53\u003csup\u003e/\u003c/sup\u003e E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is one of three subdivisions of the Birbhum District. The subdivision contains 7 community development blocks (Suri-I, Suri-II, Sainthia, Mohammad Bazar, Rajnagar, Khoyrasol).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eObjective of the study:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo study the present land cover of the Suri sub-division using satellite data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo create land cover maps with the help of GIS tools.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo understand how different types of land cover are distributed across the area.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo find areas that need changes in land use and better planning for sustainable development.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eData Source\u003c/p\u003e \u003cp\u003eRemote sensing data are very useful in this study because they give a wide overall view, can be collected repeatedly over time, and provide up-to-date information. The Landsat TM and OLI satellite imageries of the Suri subdivision for the year 2025 were acquired from the USGS Earth Explorer(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The spectral details of the satellite images are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In image acquisition, the impacts of the sun’s inclination, seasons, and cloud cover. Software Q-GIS 3.34.4 was used for image processing, spatial analysis, and creation of the GIS maps. The obtained maps have been studied to explore the spatial features in the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL. NO.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBAND NAME\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWAVE LENGTH(µm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePATH NO.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eROW NO.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRESOLUTION\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDATE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSERISE\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal Aerosol(Blue)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43–0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e30 X 30 METER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e3TH NOVEMBER 2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLANDSAT 9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45–0.51\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53–0.59\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64–0.67\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85–0.88\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57–1.65\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAND 7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWIR 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.11–2.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eThe type of land use leads to an understanding of the economic condition of the landscape. The study particularly focused on interpreting the land use and land cover based on satellite data.\u003c/p\u003e\u003cp\u003eImage processing stage\u003c/p\u003e\u003cp\u003eThe main objective of the image processing was to accurately extract the built-up coverage of the study area for the year 2025. All images were projected to WGS 1984 UTM zone 45 N. The images underwent several basic processing steps, including georeferencing, creating band combinations, mosaicking the images, and clipping them to the study area.\u003c/p\u003e\u003cp\u003eLulc classification\u003c/p\u003e\u003cp\u003eLand cover refers to the natural features on the earth that are used by humans(Best, 2024). Generally, Earth's surface has been covered by various land use features such as vegetation, agricultural land, water bodies, and others, which are used by different groups of people; therefore, land cover changes over time. In the present decade, natural land is transforming into built-up or agricultural land. The decrease in land area caused by negative impacts reduces the overall land cover(Best, 2024). The LULC map is created to understand landscape conditions and their changes over time. In this case, the prepared LULC map aims to understand the current state of the landscape in this study area for the year 2025. Supervised image classification using the support vector machine learning (SVM) algorithm was applied to measure land use, as it has proven to be an efficient method among other classifiers(Jozdani et al., 2019). The images were classified into five categories: water bodies, vegetation, agricultural land, built-up land, and fallow land.\u003c/p\u003e\u003cp\u003eDefinition of lulc classes:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eWater body: A water body is any natural area on the Earth's surface that is covered with water, either permanently or temporarily. It includes features such as rivers, lakes, ponds, oceans, wetlands, etc.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVegetation: Vegetation is the collective term for all plant life in a particular area or region, including trees, shrubs, grasses, and other plants that grow naturally or are cultivated there.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAgricultural land: Agricultural land is land that is used for farming activities, such as growing crops, raising livestock, and related agricultural practices, to produce food, fiber, and other agricultural products.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBuilt-up land: Built-up land is land that is covered by human-made structures and infrastructure, such as houses, buildings, roads, factories, schools, and commercial areas, used for residential, industrial, or urban activities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFallow land: Fallow land is agricultural land that is left uncultivated for a period of time to restore soil fertility and moisture before it is used again for growing crops.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eAccuracy assessment:\u003c/p\u003e\u003cp\u003eAccuracy assessment is the best method for reducing the error of the land use and land cover map. Researchers explained that 85% minimum accuracy value is acceptable. Here, the user accuracy value is 75.23 to 82.55% to 2025, and the producer accuracy is from 85.62% to 90.39%.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of samples\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser accuracy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.23%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedure accuracy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.62%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eNDVI:\u003c/h2\u003e\u003cp\u003eThe important indices in RS \u0026amp; GIS are the Normalised Difference Vegetation Index. This tool is used in RS \u0026amp; GIS to determine the health of vegetation. The NDVI is calculated using the formula- (NIR – RED)/(NIR + RED).\u003c/p\u003e\u003cp\u003eNDVI value range is -1 to + 1. -1 means the vegetation is sparse or other land use indicates, i. e., water body, built-up land, etc., and the + 1 value represents healthy and dense vegetation. Usually, crops are also considered in this as a type of vegetation.\u003c/p\u003e\n\u003ch3\u003eNDWI\u003c/h3\u003e\n\u003cp\u003eThe other important indices in the Normalised Difference Water Index. This index displays the water bodies on the Earth's surface. Generally, this is a monitoring tool used to detect water on the surface.\u003c/p\u003e \u003cp\u003eNDWI is calculated using the formula- (GREEN – NIR)/(GREEN + NIR)\u003c/p\u003e \u003cp\u003eNDWI value range is -1 to + 1. Here, -1 represents the other land cover types, i.e., vegetation and also built-up land. The water body represents a value of + 1. Generally, NDWI monitors and detects changes in water bodies on the Earth's surface over time.\u003c/p\u003e "},{"header":"Result \u0026 Discussion","content":"\u003cul\u003e \u003cli\u003e \u003cp\u003eAssessment Of Normalised Difference Vegetation Index(NDVI):\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eNDVI analysis played a crucial role in assessing the health of vegetation(Szabo, et al., 2016). NDVI represents the present landscape scenario of the vegetation in an area. Here NDVI analysis shows the present condition of the vegetation health in the Suri subdivision. In these cases, NDVI classes were divided into 5 divisions. At first, in the fallow land, the NDVI value is -1 to 0. The fallow land occupied in this area is 6.24 sq k.m. Generally, water bodies and built-up land are also included in this division. Spread vegetation, representative value is 0 to 0.1. indicate those types of vegetation where trees are situated in wide spaces with gaps. Usually, plants grow with large gaps of bare soil or rock between them. In our study area, sparse vegetation occupied 99.87 sq k.m. The low-density vegetation has an NDVI value of 0.1 to 0.2. The area occupied by low-density vegetation is 1321.99 sq k.m. Moderate dense vegetation amount is 352.90 sq k.m and simultaneously high dense vegetation amount is very low, area is 1.74 sq k.m.\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eAssessment of Normalised Difference Water Index(NDWI):\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eNDWI is widely used in remote sensing and geographic information systems platforms. Usually, NDWI represents the water bodies of the Earth's surface(Szabo, et al., 2016). It helps to determine river, lake, and wetlands, and also flood and drought analysis previously. The NDWI value range is -1 to + 1. -1 represents non-water bodies, including vegetation, and built-up land. And + 1 represents water bodies. Here, NDWI analysis was done to acquire some information about the water resources in our study area. The water bodies occupied only 11.09 sq k.m area(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eAssessment of Land Use \u0026amp; Land Cover(LULC):\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eLand use and land cover mapping is a very useful tool to understand the landscape scenario in a particular area. It manifested the economic condition and related socio-cultural rituals that happened. Generally, this also represents the natural resources such as vegetation, groundwater, minerals, etc., and so on. This research work was done to know the current situation of land cover and its uses in the Suri subdivision.\u003c/p\u003e\u003cp\u003eHere, the land use and land cover classification based on supervised classification techniques was very useful, and the maximum likelihood algorithm is very comfortable for this type of classification. On average, 50 to 70 training samples were selected for each LULC class. These training samples have been selected based on the user's knowledge and Google Earth time series images. Five land use \u0026amp; land cover classes were identified in the false colour composite of the satellite images. LULC in five classes are water body, vegetation, agricultural land, built-up land or settlement, and fallow land. The result of the present research revealed the land use and land cover types of the year 2025. The amount of water body area in this region is 181.07 sq k.m or 10.10%, vegetation is 569.73 sq k.m or 31.79%, agricultural land is 589.81 sq k.m or 32.91%, built-up land or settlement area is 446.92 sq k.m or 24.94% and fallow land is 4.26 sq k.m or 0.23%(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLAND USE AND LAND COVER AREA OF SURI SUBDIVISION 2025\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYPES OF LAND USE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAREA IN SQ KILOMETER\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage %\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWATER BODY\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181.0746\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.10564532\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\u003e569.7351\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.79651285\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGRICULTURA LAND\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e589.8195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.917409\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUILT UP LAND\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446.9229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.94245085\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFALLOW LAND\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2642\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237981985\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1791.8163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing Landsat images(2025), data were analyzed, and result shows that LULC mapping is perfect and precisely represents the current landscape situation of the Suri subdivision of West Bengal. Generally, NDVI and NDWI indicated the vegetation pattern and water bodies. Here we see that the spread vegetation amount is much higher than moderate and high-density vegetation. So this indicates that here the built-up land is widely spaced. The LULC map can show that the agricultural land here is very high compared to the vegetation. Simultaneously, NDWI represents the present water bodies' condition. Water bodies are comparatively low from other land use; this is a natural element of the land. But the settlement condition indicates that the water bodies have decreased over the past decade. Apparently, says that the settlement was increased over time, and natural land covers are being destroyed for these reasons.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study represents the advancement of spatial technology. This technology provided powerful tools for getting some information about the landscape of a specific area(S Steiniger, GJ Hay, 2009). Remote sensing and Geographic Information Systems were able to report the dynamism of land cover elements and land use. For this study, the use of satellite images provided valuable data on the study area. Two types of indices were used to know the specific information, such as NDVI, which represents the vegetation, and NDWI, which means the water bodies scenario(Szabo et al., 2016). These types of index analysis were very helpful for representing the study area\u0026rsquo;s scenario. The Land Use and Land Cover (LULC) map shows the overall condition. Generally, agricultural land was much higher than other land cover components. The settlement amount represented land highly used in built-up areas, and some natural land cover components were changed into built-up land. Because population data was important evidence for this. In this study area, fallow land is situated in the south-western portion, and settlements are situated widely spaced over the study area. Vegetation is generally abundant in the northeast portion of the study area. Water bodies are spread over the study area. Generally, the Maurakshi River is located in the northern portion, and the Ajoy River is located in the southern portion of the study area. So, the riverside, the water bodies were very dense. Fallow land along the riverside, generally, this is the sandy land. Agricultural land is widely situated from the northwest to the southwest. Finally, this study highlighted the present landscape condition of the study area, and this study helps researchers, urban planners, and administrators to make some developmental decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe author would also like to acknowledge the USGS for providing the satellite images and other data. The author would like to thank the University of Kalynai\u0026rsquo;s (DODL) department of Geography for their constant support and for providing the wonderful research platform.\u003c/p\u003e \u003cp\u003eAnd also thank you, my guide Dr. Sanjay Kumar Sadhukhan, for completing the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdelRahman, M. A. (2023). An overview of land degradation, desertification, and sustainable land management using GIS and remote sensing applications. \u003cem\u003eRendiconti Lincei. Scienze Fisiche e Naturali\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(3), 767\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvcı, C., Budak, M., Yağmur, N., \u0026amp; Bal\u0026ccedil;ık, F. (2023). 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In \u003cem\u003eEmerging Technologies for Water Supply, Conservation and Management\u003c/em\u003e (pp. 83\u0026ndash;107). Cham: Springer International Publishing.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Kalyani","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":"Algorithm, Landscape, Scenario, Suri, Remote Sensing, and Geographic Information System","lastPublishedDoi":"10.21203/rs.3.rs-8723388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8723388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use and land cover classification mapping, using Geographic Information Systems (GIS) and remote sensing, is a systematic process of identifying and mapping different types of surface features and human activities on the Earth's surface with the help of satellite images and spatial analysis tools. This study focuses on examining the existing land cover conditions of the Suri sub-division through the application of satellite data and GIS techniques, to understand current land characteristics and determine future land use requirements. The study particularly focused on interpreting the spatial features in the study area through satellite data. The obtained maps have been studied to explore the spatial characteristics in the studied area. The main objective of the image processing was to accurately extract the built-up coverage of the Study Area for the year 2025. The supervised image classification with the maximum likelihood algorithm method was very useful for this type of classification. Five land use \u0026amp; land cover classes were identified in the false colour composite of the satellite images. The result shows that LULC mapping is perfect and precisely represents the current landscape situation of this study area.\u003c/p\u003e","manuscriptTitle":"Reviewing the present landscape scenario in Suri subdivision, Birbhum District, West Bengal, using the land use and land cover map","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 10:41:55","doi":"10.21203/rs.3.rs-8723388/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":"4919a9a5-bb4f-4714-9af4-dc927320b0db","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61915932,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2026-01-29T10:41:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 10:41:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8723388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8723388","identity":"rs-8723388","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}