BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 7(1) (2023), 22-34 22 MULTIDISCIPLINARY SCIENTIFIC RESEARCH BJMSR VOL 7 NO 1 (2023) P-ISSN 2687-850X E-ISSN 2687-8518 Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR Published by CRIBFB, USA WATER AND VEGETATION COVER CHANGE DETECTION USING MULTISPECTRAL SATELLITE IMAGERY: A CASE STUDY ON JHENAIDAH DISTRICT OF BANGLADESH Abul Bashar (a) Md. Inzamul Haque (b)1 Most. Atia Parvin (c) Md. Anik Hossain (d) (a) Graduate Student, Dept. of Geography and Environment, Islamic University, Kushtia-7003, Bangladesh; E-mail: bashar.ge.iu@gmail.com (b) Assistant Professor, Dept. of Geography and Environment, Islamic University, Kushtia-7003, Bangladesh; E-mail: mihaque.iu@gmail.com (c) Graduate Student, Dept. of Geography and Environment, Islamic University, Kushtia-7003, Bangladesh; E-mail: atiabd99@gmail.com (d) Graduate Student, Dept. of Geography and Environment, Islamic University, Kushtia-7003, Bangladesh; E-mail: anikhossain1998.ah@gmail.com A R T I C L E I N F O Article History: Received: 2nd May 2023 Revised: 27th July 2023 Accepted: 5th August 2023 Published: 15th August 2023 Keywords: Baor, GIS, RS, SAVI, LULC JEL Classification Codes: C21, Q24, Q25 A B S T R A C T Water and vegetation cover are the two most important land cover features of any natural setting. The Jhenaidah District of Bangladesh is known for its remarkable physical geography, featuring diversified vegetation cover and numerous oxbow lakes. Due to several anthropogenic causes this majestic land cover is degrading rapidly. This study examines the spatiotemporal water and vegetation cover change of the study area from 1990 to 2020. Freeware Satellite imageries from USGS data archive was used as the main secondary data source, ensuring consistency by collecting images of the dry season. In addition, open discussion with the residents provided valuable insights into the situation. Remote sensing (RS) based Soil Adjusted Vegetation Index (SAVI) was used to detect the water and vegetation cover from the preprocessed satellite imageries. Furthermore, the water and vegetation cover were classified based on a classification scheme developed by field observation and discussion with the residents. The analysis reveals an overall 84.47% decline in dense vegetation, 63.01% decline in deep water cover, 185.69% increase in shallow water cover and 16.08% increase in agricultural lands within the mentioned time frame. Almost all the upazila of Jhenauidah district experience the criticality of the land cover change. Among the upazila Shailkupa faced unprecedented decline in deep water (95.29%), Kaliganj faced heavy decrease in forested vegetation (92.40%) whereas shallow water expanded significantly in Sadar upazila (251.37%) and agricultural land experienced most increasing trend (32.70%) in Shailakupa Upazila. © 2023 by the authors. Licensee CRIBFB, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION Bangladesh, a riverine and agriculture-dependent nation, is acutely aware of the paramount significance of water as the primary resource for agricultural sustenance (Huque et al., 2013). The absence of this vital element can unleash a profound negative impact on agricultural production, thereby engendering potential shifts in the delicate balance of the ecosystem (van Schilfgaarde, 1994). Terrestrial vegetation, on the other hand, can play an important role in providing the environmental context and shaping the dynamics of regional and global ecosystem processes by meeting a variety of needs ranging from local uses such as cooking fuel to industrial utilization for construction materials. (Samrat et al., 2023; Hérault & Piponiot, 2018; Lafleur et al., 2018; Lee et al., 2014; Sun & Liu, 2020). Jhenaidah, a region nestled in the southwestern part of Bangladesh, boasts a prominent expanse of Baor, which is considered one of the invaluable wetlands dotting the Bangladeshi landscape (Samad et al., 2022) and is reputed for moderate to dense vegetation cover but urbanization causing deforestation. Anthropogenic factors also reduce the number of wetlands in this region has undergone a drastic decline over the past four decades (Mustafa et al., 2010). Most of the areas in Bangladesh facing the changing trend of LULC and as a result the biodiversity is despoiling day by day. Furthermore, due to high industrialization, the water and vegetation cover declining rapidly not only in Bangladesh but also all over the world. Water availability and vegetation cover serve as pivotal 1Corresponding author: ORCID ID: 0000-0003-1756-5561 © 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA. https://doi.org/10.46281/bjmsr.v7i1.2061 To cite this article: Bashar, A., Haque, M. I., Parvin, M. A., & Hossain, M. A. (2023). WATER AND VEGETATION COVER CHANGE DETECTION USING MULTISPECTRAL SATELLITE IMAGERY: A CASE STUDY ON JHENAIDAH DISTRICT OF BANGLADESH. Bangladesh Journal of Multidisciplinary Scientific Research, 7(1), 22-35. https://doi.org/10.46281/bjmsr.v7i1.2061 mailto:bashar.ge.iu@gmail.com mailto:atiabd99@gmail.com http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://doi.org/10.46281/bjmsr.v7i1.2061 https://doi.org/10.46281/bjmsr.v7i1.2061 https://orcid.org/0009-0009-5602-9009 https://orcid.org/0000-0003-1756-5561 https://orcid.org/0000-0002-2718-9259 https://orcid.org/0000-0002-3670-8727 Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 23 components within Earth's intricate ecosystems, wielding significant implications for both ecosystem services and human well-being. Diminished water supply and vegetation cover can lead to drought conditions, adversely affecting the survival of plants, animals, and humans particularly in areas already grappling with the scarcity of this vital resource. The consequences of water scarcity can manifest in reduced crop yields, thereby unleashing serious ramifications for food security. According to a study by the United Nations, certain regions could experience up to a 50% reduction in crop yields due to water scarcity by 2050 (Mekouar, 2019). As the reduction in vegetation cover and the loss of wetland ecosystems are interconnected, LULC assessment can provide valuable insights into the dynamic change in nature, biodiversity, and the economic condition of the people and overall ecological health of the region. The primary goal of this study is to evaluate the overall spatiotemporal water and vegetation cover change of the Jhenaidah district from 1990 to 2020 using multispectral satellite imageries. Additionally, this study tries to find out the upazila wise change dynamics focusing on the change magnitude of each vegetation and water cover. Furthermore, it also attempts to understand the possible causes behind the change. RS index-based method e.g., SAVI was applied to detect the water and vegetation cover from the preprocessed satellite imageries. Moreover, the water and vegetation cover were classified based on a classification scheme developed by field observation and discussion with the residents. This article is divided into six major sections e.g., introduction, literature review, materials and methods, results, discussions, and conclusions. In the first section, background, justification, objectives, and brief information on methods is presented. In the literature review section, some latest relevant research is presented which helps to find out the research gaps and formulation of hypothesis. In the third section, detailed methodological framework is discussed sequentially which covers details of the materials and methods used. The result section represents the findings of the upazila wise LULC assessment and the discussion section explains the overall change dynamics of the study area. The last section represents the general summary of the findings, implications and recommendations, research limitations and suggestions for future research. LITERATURE REVIEW In the arid regions of Northwest China, alterations in water availability have been found to strongly correlate with changes in vegetation cover, highlighting the profound influence of water on vegetation extent (Gao et al., 2017). Given Bangladesh's heavy reliance on agriculture and vulnerability to climate change, it presents an intriguing case study for investigating water and vegetation cover changes using multispectral satellite images. In a separate investigation, Uzzaman et al. (2020) utilized multispectral Landsat satellite imageries of 1991, 2001, 2011, and 2021 to identify transformations in water bodies and vegetation cover within the Sundarbans, an expansive mangrove forest in southwestern Bangladesh. The study revealed a decreasing trend of forest vegetation and a subsequent increase of water bodies during the study period, attributed to a combination of anthropogenic factors like deforestation, coastal accretion, and erosion. Water scarcity is a major global concern, inducing shifts in plant communities with some species attaining dominance while others face extinction (Jury & Vaux, 2007). Dhaka city has experienced significant reductions in wetland area (76.67%) and rivers and canals (18.72%) over the past three decades (Mahmud et al., 2011). This undeniable alteration can be attributed to the relentless encroachment of agricultural pursuits and the relentless march of urbanization that has persistently ravaged the pristine wilderness (Hossain et al., 2023). According to a study that employed NDVI and supervised classification approach, between 1989 and 2020, urbanization, agricultural activity, and weather condition changed about 49.25 percent of the vegetation cover in the Barguna district (Islam et al., 2023; Morshed et al., 2022). In Jhenaidah, a region in the southwestern expanse of Bangladesh, water and vegetation dynamics are of utmost importance due to its substantial expanse of Baor, an influential wetland (Kundu et al., 2018; Mredul et al., 2021). Regrettably, these vital wetlands have undergone a precipitous decline over the past four decades (Mustafa et al., 2010), underscoring the need for meticulous analysis to understand the implications on nature, biodiversity, and socioeconomic well-being. According to Rahman et al. (2017), the rate of increase in urban areas in Jhenaidah is 0.25 km2 /year leading agriculture and vegetation to a decline of 0.14 km2 and 0.06 km2 per year respectively. In a study conducted by Hasan et al. (2021), over 29 years, notable changes were observed in the urban area, woodland, water bodies, and vegetation cover. Water bodies and forest areas accounted for 9.20% and 3.86% of the total area, respectively. During this period, urban areas expanded, converting 5.18% of the land, which now comprises 6.27% of the total area. Additionally, there was a positive development in vegetation cover, increasing by 3.36%. It is evident that the study on vegetation and water cover is highly important in Bangladesh. While significant water and vegetation-related research has been conducted in adjacent areas, Jhenaidah remains unexplored in this regard. Furthermore, spatiotemporal change study on natural land cover using satellite indices is highly admissible throughout the world, which can help land use planners and policymakers in sustainable land use planning and development. MATERIALS AND METHODS Study Area Jhenaidah District lies between 23º13ʹ and 23º46ʹ north latitude and between 88º42ʹ and 89º23ʹ east longitude. Jhenaidah is surrounded on the north by Kushtia and Rajbari districts, on the east by Magura district, on the south by Jessore district, and on the west by Chuadanga district and India (Fig. 1). The total area of the district is 1,964.77 sq. km (758.60 sq. miles) and is situated within the Ganges-Brahmaputra-Meghna delta region. Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 24 Figure 1. Study Area Satellite image selection and preprocessing Landsat satellite images are regarded as an effective tool for detecting and assessing environmental changes owing to their intermediate spatial resolution and accessibility to long-term data. In this study, high resolution multispectral Landsat 5 (TM) and Landsat 8 (OLI) satellite data were acquired from USGS satellite data archive (https://earthexplorer.usgs.gov/). The chosen images were in GeoTIFF format, Level-1 with a minimum percentage of cloud cover (less than 10%) and were projected to Universal Transverse System Zone 46 of the WGS 1984 (World Geodetic System). The details of the acquired satellite imageries are given in table 1. To obtain the bottom-of-atmosphere reflectance, the top-of-atmosphere (TOA) reflectance values of each image were converted by atmospheric modification. Table 1. Details of acquired Satellite Images Spectral Index In remote sensing, various indices such as NDVI, NDWI, NDBI, SAVI, NDSSI, etc. are used to perform change detection. These indices generate values ranging from -1 to +1. In this study, the soil-adjusted vegetation index (SAVI) was calculated using Equation 1. The SAVI is an index used to assess vegetation cover and health while accounting for variations in soil brightness. It was developed as an enhancement of the Normalized Difference Vegetation Index (NDVI) to minimize the influence of soil reflectance on vegetation measurements (Fatiha et al., 2013). The SAVI is useful for a variety of applications, such as monitoring agricultural crops, assessing land cover changes, and estimating carbon stocks in forests. Details of the bands used for SAVI analysis are presented in Table 2. The SAVI formula incorporates a soil adjustment factor, "L," which is determined based on the background soil brightness. Here, L is a soil-adjustment factor ranging from 0 to 1 that controls the influence of soil reflectance on the index. SAVI values typically range from -1 to 1, with higher values indicating denser and healthier vegetation reflectance on vegetation indices (Huete, 1988). The formula is as follows: SAVI = ((NIR - Red) / (NIR + Red + L)) x (1 + L) ………………………………………………………………. (1) Table 2. Spectral properties of the bands used in this study Spectral Region Landsat 5(TM) Landsat 8(OLI_TIRS) Band Wavelength (µm) Resolution (m) Band Wavelength (µm) Resolution (m) Red 3 0.63-0.69 30 4 0.64-0.67 30 Near Infrared (NIR) 4 0.76-0.90 30 5 0.85-0.88 30 Land cover change assessment using the SAVI index Assessing land cover change using the SAVI can provide valuable insights into vegetation and water cover and changes in land cover over time. Four distinct land cover classes deep water, shallow water, agricultural land, and forested vegetation were determined by on-field observation and interviewing the local people. Then the SAVI values were categorized into four distinct classes by partitioning the threshold values using random classifier and specifying the thresholds for SAVI Satellite ID Sensor ID Path/Row Acquisition Date Spatial Resolution Image Quality LANDSAT 5 TM 138/44 1990-01-30 30 9 LANDSAT 5 TM 138/44 2000-02-11 30 7 LANDSAT 5 TM 138/44 2010-02-06 30 9 LANDSAT 8 OLI_TIRS 138/44 2020-02-02 30 9 Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 25 classification, which are represented in Table 3. Table 3. The threshold value range used in SAVI classification and classification scheme Types Description Threshold value 1990 2000 2010 2020 Deep Water Perennial waterbodies such as- Rivers, Lakes, Beels, Boar -0.08-0.03 -0.14-0.03 -0.08-0.03 -0.07-0.03 Shallow Water Ephemeral water bodies and semi-inundated land 0.03-0.15 0.03-0.09 0.03-0.11 0.03-0.12 Agricultural Land Crops, paddy, vegetable field 0.15-0.22 0.09-0.18 0.11-0.21 0.12-0.24 Dense/Forested Vegetation Natural or manmade forests, Plantation 0.22-0.48 0.18-0.41 0.21-0.44 0.24-0.42 Accuracy Assessment The validity of a defined land cover is determined by its accuracy. Accuracy assessment operations have typically been carried out using either ground truth data or, in contrast, using a few designated points on a previously classified map (Arifeen et al. 2021). To accomplish this assessment, approximately 100 random sample points were selected from each classified image (2001, 2011, and 2021). After that those sample points were validated through a reference map. In this study, Google Earth Pro was used as a reference map for assessing the ground truth. A proper examination of the results was carried out using a confusion matrix, which is designated as a powerful tool to ascertain key performance indicators such as user accuracy, producer accuracy, total accuracy, and the Kappa coefficient for every selective year (eq. 2-5). Overall Accuracy = Total Number of Corrected Pixel Total Number of Reference Pixel ×100........................................................ (2) User Accuracy = Number of correctly classified pixels Total Number of classified pixels (Row total ) ×100............................................. (3) Producer Accuracy = Number of correctly classified pixels Total Number of classified pixels(Column total) ×100.................................. (4) Kappa coefficient = (TS×TCS)−∑(Column total ×Row total) (TS×TS)−∑(Column total×Row total) ......................................................... (5) Where, TS is the total sample and TCS is the total corrected sample. RESULTS Accuracy Assessment Results The validity of a classified land cover depends on accuracy evaluation. To validate the land cover class obtained from the SAVI for 1990, 2000, 2010, and 2020, the following validations were performed: overall accuracy, kappa coefficient, user accuracy, and producer accuracy. For the entire study area, a total of 100 arbitrary reference points were taken and visualized using Google Earth Pro. A kappa value greater than 0.75 indicates that the classification accuracy is very good, whereas a kappa value less than 40 indicates poor accuracy. (Rahman & Shozib, 2021; Congalton, 1991). Table 4. Accuracy assessment result of the LULC map Year LULC Type User Accuracy Producer Accuracy Overall Accuracy Kappa Coefficient 1990 Deep Water 100% 96% 76% 0.68 Shallow Water 65% 96% Agricultural Land 75% 31% Dense Vegetation 69% 88% 2000 Deep Water 84% 100% 88% 0.84 Shallow Water 92% 84.46% Agricultural Land 88% 78.57% Dense Vegetation 88% 88% 2010 Deep Water 88% 95.65 % 90% 0.87 Shallow Water 80% 86.96 % Agricultural Land 96% 82.76 % Dense Vegetation 80% 96% 2020 Deep Water 85% 94.44% 88% 0.84 Shallow Water 88.24% 96.77% Agricultural Land 90% 69.23% Dense Vegetation 88.46% 95.83% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 26 Upazila-based LULC Assessment Shailkupa Upazila The result of the change assessment of water and vegetation cover of Shailkupa upazila is presented in Figure 2 and 3. The analysis found that the dense vegetation cover decreased almost all over the area of Shailkupa. Much of the deep-water cover vanished from 1990 to 2020. Agricultural land expands rapidly in the western part of the Upazila and shallow water cover in the northern part of Shailakupa Upazila. The Kumar River and Nabaganga River in Shailakupa almost died. The main Madhumoti River is in danger. The LULC change statistics of Shailakupa Upazila are given below in Table 5 and Figure 4. Figure 2. Shailakupa land cover in 1990 Figure 3. Shailakupa land cover in 2020 Table 5. LULC of Shailakupa Upazila Figure 4. Water and vegetation cover change of Shailkupa Upazila Harinakunda Upazila Figure 5 and 6 show that the deep-water cover was replaced by shallow-water cover, and the dense vegetation cover decreased significantly in the whole Harinakundu Upazila. Agricultural land increased mostly in the eastern part of Harinakundu Upazila. The LULC change of Harinakundu Upazila is given below in Table 6 and Figure 7. 0 10000 20000 30000 40000 50000 60000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Shailakupa Upazila from 1990 to 2020 1990 2020 LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 15476.82 727.93 -14748.89 -95.29% Shallow Water 14929.94 34689.35 19759.41 132.35% Agricultural Land 40772.98 54105.28 13332.30 32.70% Dense Vegetation 21942.17 3580.01 -18362.16 -83.68% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 27 Figure 5. Harinakundu land cover in 1990 Figure 6. Harinakundu land cover in 2020 Table 6. LULC of Harinakundu Upazila Figure 7. Water and vegetation cover change of Harinakundu Upazila Jhenaidah Sadar Upazila Figure 8 and 9 show that the deep-water cover was replaced by shallow water cover, and the dense vegetation cover decreased significantly throughout Jhenaidah Sadar Upazila due to extensive urbanization and industrialization. Agricultural land increased mostly in the eastern part of Jhenaidah Sadar Upazila. The LULC change in Jhenaidah Sadar is given below in Table 7 and Figure 10. Figure 8. Jhenaidah Sadar land cover in 1990 Figure 9. Jhenaidah Sadar land cover in 2020 0 5000 10000 15000 20000 25000 30000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Harinakundu Upazila from 1990 to 2020 1990 2020 LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 10854.88 3420.561 -7434.32 -68.48% Shallow Water 8466.95 24808.31 16341.36 193% Agricultural Land 22853.65 25940.59 3086.94 13.50% Dense Vegetation 14054.71 2064.29 -11990.42 -85.31% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 28 Table 7. LULC of Jhenaidah Sadar Upazila Figure 10. Water and vegetation cover change of Jhenaidah Sadar Upazila Kaliganj Upazila Figure 11 and 12 show that the deep-water cover was significantly replaced by the shallow-water cover, and the dense vegetation cover decreased significantly throughout Kaliganj Upazila due to extensive urbanization and industrialization. The dense vegetation cover almost vanished in Kaliganj Upazila. The largest baor (Oxbow Lake) “Marjad Baor” in Bangladesh (Alam & Jahan, 2014) is also in an alarming situation. The agricultural land in Kaliganj Upazila decreased by 2624.07 acres over the last 3 decades. The LULC change in Kaliganj Upazila is given below in Table 8 and Figure 13. Table 8. LULC of Kaliganj Upazila Figure 11. Kaliganj land cover in 1990 Figure 12. Kaliganj land cover in 2020 0 10000 20000 30000 40000 50000 60000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Jhenaidah Sadar Upazila from 1990 to 2020 1990 2020 LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 31581.15 14138.83 -17442.32 -55.54% Shallow Water 14851.91 52185.55 37333.64 251.37% Agricultural Land 42516.47 45100.06 2583.93 06.07% Dense Vegetation 26411.81 3927.65 -22484.16 -85.13% LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 20489.02 10321.35 -10167.67 -49.62% Shallow Water 11038.83 37217.13 26178.30 237.15% Agricultural Land 31721.02 29096.95 -2624.07 8.27% Dense Vegetation 14486.56 1101.51 -13385.05 -92.40% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 29 Figure 13. Water and vegetation cover change of Kaliganj Upazila Kotchadpur Upazila Figure 14 and 15 show that the deep-water cover was significantly replaced by shallow water cover, and the dense vegetation cover decreased significantly throughout Kotchapur Upazila due to extensive urbanization and industrialization. The dense vegetation cover almost vanished in Kotchadpur Upazila. The Joydia Baor and Boluhar Baor (Oxbow Lake) in Kotchadpur Upazila are also in an alarming situation. Boluhar Baor almost died in the last 3 decades. In the northeastern part of the Kotchadpur, Upazila had Beels that almost died and were replaced by shallow water cover. The agricultural land in Kotchadpur Upazila increased mostly in the western part of Kotchadpur Upazila. The LULC change in Kaliganj Upazila is given below in Table 9 and Figure 16. Figure 14. Kotchadpur land cover in 1990 Figure 15. Kotchadpur land cover in 2020 Table 9. LULC of Kotchadpur Upazila 0 5000 10000 15000 20000 25000 30000 35000 40000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Kaliganj Upazila from 1990 to 2020 1990 2020 LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 9701.99 2924.56 -6777.43 -69.86% Shallow Water 5695.16 17350.30 11655.14 204.65% Agricultural Land 15854.98 20159.06 4304.08 27.15% Dense Vegetation 10865.90 1684.83 -9181.07 -84.49% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 30 Figure 16. Water and vegetation cover change of Kotchadpur Upazila Mohespur Upazila Figure 17 and 18 show that the deep-water cover was significantly replaced by shallow water cover, and the dense vegetation cover decreased significantly throughout Mohespur Upazila due to extensive urbanization and industrialization. The dense vegetation cover almost vanished in Mohespur Upazila. Mohespur Upazila is very important for the wetland area, as there are many baors and beels situated in this Upazila. The Purapara Baor, Nostir Baor, Nepar Baor, Baghadangar Baor, Fatepur Baor, Chapatola-Vabnagar-Srinathpur Baor, Golla Baor, Mirzapur Baor, Katgara Baor, Khusolpur Baor (Oxbow Lake) in Mohespur Upazila and many smaller baors. The beels in Mohespur Upazila include Mailbariya Beel, Ukhri Beel, Talsar Beel, Dubli Beel, Pakrail Beel and many more. Those Baors and beels are also an alarming situation. From the mid to southwestern part of the Mohespur, Upazila is mainly in a wetland area, as there are many baor and beels situated, which almost died in the last 3 decades and were replaced by shallow water cover. Agricultural activity in the northeastern part of Mohespur Upazila increased. The LULC change in Mohespur Upazila is given below in Table 10 and Figure 19. Figure 17. Mohespur land cover in 1990 Figure 18. Mohespur land cover in 2020 Table 10. LULC of Mohespur Upazila Figure 19. Water and vegetation cover change of Mohespur Upazila 0 5000 10000 15000 20000 25000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Kotchadpur Upazila from 1990 to 2020 1990 2020 0 20000 40000 60000 Deep Water Shallow Water Agricultural Land Dense Vegetation ac re → LULC of Mohespur Upazila from 1990 to 2020 1990 2020 LULC type Change area (ac) 1990 2020 Change Area Percentage (%) Deep Water 29401.04 10484.30 -18916.74 -64.34% Shallow Water 15490.85 47665.86 32175.01 207.70% Agricultural Land 32934.64 39673.48 6738.84 20.46% Dense Vegetation 23202.22 3213.806 -19988.42 -86.15% Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 31 DISCUSSIONS Year-wise temporal LULC change of the study area is illustrated in Fig. 14 and the overall trend of change is presented in Figure 21. From the figure it is evident that the deep-water bodies and dense or forested vegetation are decreasing significantly whereas the shallow water bodies and agricultural lands are following the opposite trend. The graph shows that from 1990 to 2020, the deep-water cover decreased by 63.01%, and the dense vegetation cover decreased by 84.47%, which is alarming for the environment and biodiversity. In the meantime, shallow water cover and agricultural land increased by 185.69% and 16.08%, respectively. Figure 20. Area covered by different land cover features from 1990-2020 Table 11. Area covered by different Land Cover Feature from 1990-2020 LULC Category 1990 2000 2010 2020 Area in Acre % Area in Acre % Area in Acre % Area in Acre % Deep Water 116382.9 23.95 30280.59 6.23 26648.66 5.48 43045.38 8.86 Shallow Water 74773.09 15.38 153150.1 31.52 168868.3 34.75 213620.7 43.96 Agricultural Land 182484.1 37.55 251281.3 51.71 233254.6 48 211822.9 43.59 Dense Vegetation 112282.8 23.11 51211.04 10.54 57151.42 11.76 17433.96 3.59 Total Area 485921.97 100 485923.03 100 485922.98 100 485922.96 100 Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 32 Figure 21. Area covered by different Land Cover Feature from 1990-2020 Table 11 summarizes the temporal magnitude of each land cover feature from 1990-2020, and Table 12 shows the overall temporal LULC change summary for each classified land cover. Most of the deep-water bodies decreased between 1990-2020 about 86100 acres, while shallow water bodies increased heavily about 78377 acres in the same time period. The residents of the study area blamed unplanned urbanization and extensive agricultural growth behind the widespread water cover change. The response of the locals also aligned with the results of the agricultural change as the table 12 shows around 68797 acres of agricultural land have been increased between 1990 and 2000. Most of the dense vegetation also decreased between the mentioned time period. Table 12. Temporal LULC Change summary of each classified land cover. From informal interview with the locals, it was found that local government had taken some necessary steps such as extensive afforestation, riverbank management, baor conservation, agricultural innovation with mechanization and introduction of hybrid variety etc. after the year 2000. But the management system was not sustainable as the deep-water cover, especially the baors and forested vegetation found its declining trend. CONCLUSIONS The LULC analysis of Jhenaidah District from 1990 to 2020 demonstrates considerable changes in the landscape. Deep Water and Dense Vegetation coverage declined by 63.01% and 84.47%, respectively, while Shallow Water and Agricultural Land inclined by 185.69% and 16.08%, respectively. At the upazila level, Shailkupa experienced the greatest decline in Deep Water cover (95.29%) and Dense Vegetation (83.68%), while Kaliganj experienced a more modest decrease (49.62% and 92.40%, respectively). Jhenaidah Sadar saw the most increase in Shallow Water (251.37%), while Shailkupa saw the least (132.35%). Shailkupa also experienced the greatest increase in Agricultural Land (32.70%), whereas Jhenaidah Sadar experienced the smallest increase (6.07%). These shifts are mostly driven by growing food supply demand, which leads to increased agricultural land, gentrification, and river management behaviors, all of which contribute to an increase in Shallow Water coverage. Meanwhile, the abundance of built-up regions has reduced Dense Vegetation across Jhenaidah. This research can be a cost-effective solution to land use land cover monitoring and management. Index based method assures reliability and accuracy as it consists of both automated and manual approach which is maintained in every step of this research. Furthermore, the calculated threshold values for water and vegetation cover identification might be applied to satellite-based vegetation and water cover delineation of similar landscapes. One of the study's major weaknesses is its reliance on publicly available data sources for land cover research. The precision of the conclusions may be influenced by the accuracy and resolution of the data used. To increase accuracy, future studies could benefit from employing higher-resolution and more recent satellite images or combining ground-based data. While this analysis identifies significant changes in land cover, no direct causal links are established. Various underlying variables, such as socioeconomic advancements, policy initiatives, or climate fluctuations, could be driving these shifts. Further research using advanced modeling approaches or doing field surveys could aid in determining the underlying causes of the observed trends. The research is being carried out at the district level, which may obscure local-scale variability in 0 100000 200000 300000 Area Cover (ac) of 1990 Area Cover (ac) of 2000 Area Cover (ac) of 2010 Area Cover (ac) of 2020 a cr es Analysis of SAVI 1990 to 2020 Changing Trend Deep Water Cover Shallow Water Cover Agricultural Land Dense Vegetation Cover Linear (Deep Water Cover) Linear (Shallow Water Cover) Linear (Agricultural Land) Linear (Dense Vegetation Cover) LULC type Change area (Ac) 1990 - 2000 2000 -2010 2010 - 2020 Deep Water -86101.41 -3631.93 16396.72 Shallow Water 78377.01 15717.90 44752.70 Agricultural Land 68797.2 -18026.70 -21431.70 Dense Vegetation -61071.76 5940.38 -39717.46 Bashar et. al., Bangladesh Journal of Multidisciplinary Scientific Research 7(1) (2023), 22-34 33 land cover changes. Future research should look into doing studies at a finer geographical scale in order to capture more localized trends and understand the variety of land use changes within the district. Extending the analysis to larger time frames could also provide a more complete knowledge of land cover patterns and their effects throughout time. Author Contributions: Conceptualization, A.B. and M.I.H.; Methodology, A.B. and M.I.H.; Software, A.B., M.I.H, and M.A.P.; Validation, A.B., M.I.H., M.A.H., and M.A.P.; Formal Analysis, A.B. and M.A.P.; Investigation, A.B. and M.A.P.; Resources, A.B.; Data Curation, A.B., M.I.H., M.A.H., M.A.P.; Writing – Original Draft Preparation, A.B. and M.A.H.; Writing – Review & Editing, A.B., M.I.H., M.A.H., and M.A.P.; Visualization, M.I.H., M.A.H., M.A.P.; Supervision, M.I.H.; Project Administration, M.I.H. Author Agreement: All the authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study, due to the research does not dealing with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgments: We the authors like to acknowledge United States Geological Survey (USGS) to grant free access to Landsat Satellite data. We also extend our heartfelt gratitude to the local peoples of Jhenaidah district who helped us to validate the results. Finally, we would like to thank the teachers, students, and staffs of dept. of Geography and Environment of Islamic University, Kushtia, Bangladesh to help us in every step of this research. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions. 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