Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

Kumar, Mithun; Mondal, Ismail; Pham, Quoc Bao

doi:10.1007/s11600-021-00551-3

Cited by 32 publications

(11 citation statements)

References 32 publications

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“…The study reveals that water bodies increased (2.52%) from 1991 to 2021, whereas healthy vegetation, unhealthy vegetation, and sandbar areas decreased during the same period. The increase of water bodies (1.30%) and subsequent decrease in vegetation cover in the Eastern Sundarban of Bangladesh was also reported by Kumar et al (2021). The increase of water bodies indicates the climate changeinduced sea-level rise that is progressed slowly in the study area.…”

Section: Insight Into the Change Records Of Sundarban Mangrove Forestsupporting

confidence: 67%

“…So, the detailed studies on Sundarban ground causing alteration in the seaside regions of Bangladesh using time series of satellite imagery will provide a comprehensive picture of the changes. Past research on Sundarban mangrove forest includes conservation and restoration (Romanach et al, 2018;Vyas and Sengupta, 2012); monitoring the forest cover changes (Islam et al, 2019;Kumar et al, 2021); quantification of ecosystem services (Hossain et al, 2016;Iqbal, 2020); livelihood dynamics of the communities (Kibria et al, 2018;Mozumder et al, 2018); degradation (Islam and Bhuiyan, 2018); species distribution (Barik et al, 2018); climate resilience of the Sundarban mangrove forest (Minar et al, 2013;Chakma et al, 2021). However, there is a definite knowledge gap regarding the updated status and distribution of healthy and unhealthy vegetation conditions of the Sundarban mangrove forest.…”

Section: Introductionmentioning

confidence: 99%

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Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

Uzzaman

Miah²,

Abdullah

et al. 2021

Ann. Bangladesh Agric.

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Accurate and realistic forest cover change assessment is essential for the conservation and management of the Sundarban mangrove forest of Bangladesh. With these views, an integrated way of the vegetation cover assessment was conducted using time-series Landsat satellite imageries of 1991, 2001, 2011, and 2021. During the last 30-year (1991-2021), variations in four land cover classes viz. healthy vegetation, unhealthy vegetation, water body, and sandbar were recorded. It showed a decreasing trend of forest vegetation and a subsequent increase of water bodies during the study period. The healthy vegetation and unhealthy vegetation decreased at 1.33 and 1.66%, respectively, whereas water bodies increased 2.55% at the same time. The healthy vegetation consistently decreased over the decades, but unhealthy vegetation decreased during the 2001-2011 period. Conversion from healthy vegetation to unhealthy vegetation and unhealthy vegetation to healthy vegetation during 1991-2001 was similar. Such transform was much higher from unhealthy to healthy vegetation during 2001-2011. Transformation of healthy vegetation to unhealthy vegetation was remarkably higher during the 2011-2021 period. Further continuous change detection and classification algorithm (CCDC) showed a stable pattern over the study period without significant breakpoints. This study reveals the need for regular mangrove forest monitoring. The findings of this study can be used as a reference in the formulation and implementation of sustainable mangrove forest conservation and management. Ann. Bangladesh Agric. (2020) 24(2): 15-32

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Section: Insight Into the Change Records Of Sundarban Mangrove Forestsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

Uzzaman

Miah²,

Abdullah

et al. 2021

Ann. Bangladesh Agric.

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“…A variety of image classification techniques are used for mapping and studying LULC change (Billah et al, 2021;Lu and Weng, 2007). Supervised classification using maximum likelihood (ML) algorithm have been used worldwide over the past two decades to study mangrove LULC (Kumar et al, 2021;Bera and Chatterjee, 2019;Jones et al, 2016;Ghosh et al, 2016;Pham and Yoshino, 2015;Chen et al, 2013;Giri et al, 2010;Giri and Muhlhausen 2008;Giri et al 2007). Because, the ML algorithm is one of the most well-known parametric classifiers used for supervised classification (Li et al, 2014) and is easy to use, thus, an extended training process is not essential (Chen et al, 2013;Datta and Deb, 2012).…”

Section: Training Sample Selection Image Classification and Accuracy ...mentioning

confidence: 99%

A Comparative Assessment of Land Use-Land Cover Dynamics Between Bangladesh and Indian Sundarbans From 1975–2020: A Geospatial and Statistical-Based Approach

Hossain¹,

Pirotti

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The study aims to compare land use land cover (LULC) change between Bangladesh and Indian Sundarbans from 1975 to 2020 using Landsat Satellite images. We performed supervised maximum likelihood (ML) to classify the study area at four time periods over 45 years (1975, 1990, 2005, and 2020). The classification was assigned to five classes: dense forest, moderate forest, sparse forest, barren land, and water body. Accuracy assessment of the classified images was completed with 250 control points for each year. The findings of our study revealed that the dense forest cover of Bangladesh and Indian parts was 54% and 31%, respectively, whereas, for the whole Sundarbans, it was 45% in 1975. However, the dense forest of Bangladesh and Indian Sundarbans decreased by an annual rate of 1.20% and 1.60%, respectively, from 1975 to 2020. From 1990 to 2005, Bangladesh Sundarbans slightly increased the dense forest cover by an annual rate of 0.68%, while the Indian Sundarbans decreased by an annual rate of 0.63%. The moderate dense forest of Bangladesh and Indian Sundarbans increased by giving almost the same annual rate of 3.62% and 3.59% from 1975 to 2020, whereas the increasing rate of the sparse forest was much higher for Bangladesh (8.36%) Sundarbans than Indian (3.36%) parts. The water bodies of Bangladesh and Indian Sundarbans increased by giving an annual rate of 0.48% and 0.71%, respectively, from 1975 to 2020. Our study found that most of the barren lands were located near the boundary between forest and human settlement of Indian Sundarbans compared to Bangladesh. The findings of the comparative assessment between these two countries can support sustainable forest management and planning by considering the best policy options.

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“…For the calculation of area covered by different land use classes, the "attribute table" was obtained after the land use classification was analyzed. The pixels covered by each class were divided by the total pixels of the study area to obtain percentage change in cover for each class, as shown in Equation ( 4) [58].…”

Section: Change Detectionmentioning

confidence: 99%

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Zeshan

Mustafa

Baig

2021

Water

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Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

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Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

Cited by 32 publications

References 32 publications

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

A Comparative Assessment of Land Use-Land Cover Dynamics Between Bangladesh and Indian Sundarbans From 1975–2020: A Geospatial and Statistical-Based Approach

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

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