




































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. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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