World's Largest Mangrove Forest Research Articles

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Spatio-Temporal Changes in the World Largest Mangrove Forest and Human Perceptions in Bangladesh: A Study Using GIS-RS and Social Survey Techniques

Mangrove forests are disappearing at a faster rate than any other forests in many places around the world due to natural and anthropogenic causes; as a result, natural resources are decreasing. Considering these issues, present study focuses on the changing pattern of the world’s largest mangrove forest which locally known as Sundarbans, and utilized Landsat satellite imagery during the periods 2000 to 2021 as the primary data source. Using Google Earth Engine, the Normalized Difference Vegetation Index (NDVI) was applied to classify the images into 4 LULC categories (water bodies, sediment, forest, and others). The accuracies of the image classifications were between 89.76 and 92.21 percent. Classified images for the years 2000, 2010, and 2021 were then used to train and validate the ANN-CA (artificial neural network-based cellular automaton) model applied to produce the LULC scenario for the year 2031. On the other hand, a questionnaire survey was conducted in Sarankhola, and Shyamnagar Upazila to identify locals’ perceptions of the causes of LULC changes in the study area. The findings reveal a great change from 4036.92 to 3969.33 km2 of forest cover between 2000 and 2021, and further degradation of 3913.03 km2 by the projected year (2031). The results show that all the 13 identified causes are driving the LULC changes in Sundarbans. The study also suggests if the appropriate management practices are not implemented, the study area's forest land degradation will likely continue in the years to come.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 89-105

Diversity of bacterial communities in the Sundarbans mangrove, Bangladesh, with special focus on pathogens affecting aquatic organisms

The Sundarbans, located in Bangladesh, is the world's largest mangrove forest with a rich and diverse microbial community. The main objective of this study was to use 16S rRNA sequencing to identify the bacterial community in the Sundarban mangrove forest, particularly pathogenic bacteria to aquatic organisms. This study conducted in Bangladesh collected 225 samples of soil, water, fish, and shellfish from five locations with a typical mangrove environment. All samples underwent genomic DNA extraction, quantification, gel documentation, and subsequent sequencing. Sanger sequencing was then carried out, followed by sequencing 11 concentrated samples using the universal bacterial primer 16S rRNA. The study found 872 bacterial species, 94 genera, 38 families, and 6 phyla. Pseudomonades or proteobacteria accounted for 92% of the phylum composition, with firmicutes, bactericides, cyanobacteria, Verrucomicrobia, and actinobacteria also present at smaller percentages. Chromatiaceae had the highest species count (20.3%), followed by Ectothiorhodospiraceae (15.5%), Methylococcaceae (12.3%), Moraxellaceae (10.7%), Thiotrichaceae (8.7%), Idiomarinaceae (5.4%), and smaller groups. All the species that were identified were gram-negative bacteria and were found in different environments, mainly halophilic ones. Based on 16 s rRNA sequencing, additional experimental and clinical data, as well as NCBI (BLAST), approximately 49.3% of the 872 bacterial species are typically harmless and not classified as pathogenic. The pathogenic characteristics of 24.9% of the species are still unidentified. 13.4% showed either pathogenic or non-pathogenic characteristics (a bacterium as having the potential to exhibit pathogenic or non-pathogenic characteristics in certain circumstances), while 8.4% were exclusively pathogenic, potentially capable of causing disease. Also, the bacterial species are categorized as 2.6% non-highly pathogenic (a bacterium can still have a significant impact on health, especially in weakened immune systems or other underlying health conditions), 1.3% sometimes pathogenic and 0.1% potentially pathogenic (a bacterium don't always cause disease). Sequence analysis, combination of phylogenetic, biochemical characterization (data were not shown), and other experimental validation conclude a genetic baseline of bacterial community, this research may serve as a valuable reference for future ecological and taxonomical studies as well as addressing further research scope on bacterial diversity found in the Sundarbans mangrove of Bangladesh.

An accidental encounter with bloom of diatom Palmerina hardmaniana (Greville) Hasle in Sundarbans, India: Impact on zooplankton community structure in tidal creek environment

The present study reports on a transient diatom bloom of Palmerina hardmaniana (Greville) Hasle 1996 encountered accidentally in May 2018 in the world's largest mangrove forest of Sundarbans, India. This bloom was the sixth record of diatom blooms from Indian coastal waters and the fourth from Sundarbans waters. The diatom cell count ranged from 2.571 × 104 to 6.857 × 104 cells L−1 during the bloom. The zooplankton taxa count decreased from 39 during the pre-bloom to 30 during bloom and 31 during the post-bloom phase. However, no significant (p < 0.05) change in zooplankton species richness was observed between the three different phases. The consistent occurrence of copepod Bestiolina simlis across sampling points indicated a high tolerance to environmental parameters. The zooplankton population also did not respond vehemently to bloom-forming P. hardmaniana in this creek environment. These observations warrant further studies to examine the effect of blooms on the overall marine ecological food chain of creek ecosystems.

Measurement of radioactivity in soils of Karamjal and Harbaria mangrove forest of Sundarbans for establishment of radiological database.

This work presents the first in-depth study of soil radioactivity in the mangrove forest of Bangladesh part of the Sundarbans. It used HPGe gamma-ray spectrometry to measure the amount of natural radioactivity in soil samples from Karamjal and Harbaria sites of the world's largest mangrove forest. The activity concentrations of most of the 226Ra (14±2 Bqkg-1 to 35±4 Bqkg-1) and 232Th (30±5 Bqkg-1 to 50±9 Bqkg-1) lie within the world average values, but the 40K concentration (370± 44 Bqkg-1 to 660±72 Bqkg-1) was found to have exceeded the world average value. The evaluation of radiological hazard parameters revealed that the outdoor absorbed dose rate (maximum 73.25 nGyh-1) and outdoor annual effective dose (maximum 0.09 mSvy-1) for most samples exceeded the corresponding world average values. The elevated concentration of 40K is mainly due to the salinity intrusion, usage of fertilizers and agricultural runoff, and migration of waste effluents along the riverbanks. Being the pioneering comprehensive research on the Bangladesh side of the Sundarbans, this study forms a baseline radioactivity for the Sundarbans before the commissioning of the Rooppur Nuclear Power Plant in Bangladesh.

Modeling and optimal control applied to reduce the effects of greenhouse gases emitted from the coal-based power plant in Bangladesh

The coal-fired power station is believed to be one of the major emitters of air pollutants, particularly carbon dioxide (CO2), which is the main sensitive driver of climate change due to global warming, consequently causing significant intimidation for the Sundarbans, the world's largest mangrove forest and nearby due to high emissions of air pollutants such as Carbon-Di-Oxide (CO2). Here, we used a compartmental mathematical model with 3 compartments to study the dynamics of greenhouse gas emissions, concentration, and uptake, which we can control by installing a chemical reactor system near the power plant and naturally afforesting the regions. The model was built from scratch to study these types of problems. First, we formulated the optimal control problem by connecting two control measurement systems: a chemical reactor system and natural afforestation. For this purpose, Pontryagin's maximum principle is used. The novelty of this work is the investigation of optimal strategies to minimize the impact of gases emitted by Coal based power plants on neighboring regions. More realistic facts such as system damage from excess emissions, most absorbers, and other facts are covered here. The numerical solution obtained illustrates the outcome of the system with initial values and theoretical parameters that best represent reality. By evaluating the performance index scores, and objective function values, we found that both controls (the chemical reactor system and natural afforestation) help minimize air pollution. We then simulated our model with 5 different control strategies to observe its performance in reducing pollutants. Once we determine that two control strategies are equally effective in reducing pollution, let's compare them by looking at the costs associated with each strategy. Therefore, using both control systems (chemical reactor and natural afforestation) with a higher reaction rate, we suggested chemical reactor system control as the best strategy.

Sundarbans Mangrove Mapping and Above Ground Biomass Estimation Using Earth Observation Techniques

The Sundarbans, the world's largest mangrove forest, plays a crucial role in Bangladesh's economy and environment. However, overexploitation, anthropogenic activities like tree cutting for development and agriculture, as well as natural disasters, have caused severe damage and changes to the Sundarbans. This research aims to detect changes in the mangrove forest areas and create an above-ground biomass map of the Sundarbans Forest. Previous studies relied on time-consuming and inaccurate traditional methods, while this study seeks to make a significant contribution to mangrove mapping and forest resource management. Landsat 5 and 8 images from 2007 to 2017 were used to generate the mangrove index (including CMRI and MI) for different years. ALOS-PALSAR 2 and JERS images from 1996, 2007, and 2017, with HV+HH polarization, were processed to calculate the backscatter ratio, enabling identification and estimation of vegetation loss and gain over the years. To estimate the above-ground biomass (AGB), tree height was derived from SRTM DEM, and an allometric equation was used to calculate AGB. The results indicate a continuous shrinkage of the mangrove forest. The 2017 maps of the two different indexes reveal significant mangrove vegetation loss compared to 1996 and 2007. Additionally, the study estimates the total above-ground biomass of the Sundarbans mangrove forest to be 329 million tons. The findings can assist relevant authorities in taking necessary action, formulating policies, and implementing plans for sustainable management of the Sundarbans mangrove forest resources.

Pollution status and ecological risk assessment of metal(loid)s in the sediments of the world's largest mangrove forest: A data synthesis in the Sundarbans

The Sundarbans is the largest single-mass mangrove forest in the world, experiencing environmental and anthropogenic stress from metal(loid) inputs. We undertook a comprehensive assessment of sediment contamination and ecological risks posed by metal(loid)s in the Sundarbans using previously published data. There was a distinct difference in metal(loid) content, pollution level and ecological risk in Bangladeshi and Indian parts of the Sundarbans, with the Indian counterpart experiencing relatively higher metal(loid) pollution. The higher pollution level in India might be attributed to its vicinity to municipal and industrial areas that act the primary source of metal(loid)s in the Sundarbans. The cumulative ecological risks of metal(loid)s pointed out that the south-eastern part of Bangladeshi Sundarbans and north-eastern Indian part are at moderate ecological risk. This research will provide valuable data to inform the responsible authorities and will underpin future policies and management to reduce future metal(loid) inputs in the Sundarbans.

Microplastics in different fish and shellfish species in the mangrove estuary of Bangladesh and evaluation of human exposure

The water bodies in Bangladesh thrive from plastic pollution. Estuaries are pools of environmental contaminants, and the world's largest mangrove forest, Sundarbans' estuary, is no exception. Thus, for the first time, we investigate MPs abundance in the muscle and gastrointestinal tract (GIT) of twenty estuarine species of fish and shellfish of the Sundarbans, as well as the human health risk. MPs abundance was evident in all the samples ranging from 5.37 ± 1.07 to 54.30 ± 16.53 MP items/g wet body weight (dw) in muscle samples and 7.33 ± 1.89 to 205.61 ± 136.88 MP items/g (dw) in GIT samples. The estimated health risk from MPs is substantial, where the average human intake will be 85,710.08 items of MPs per year per capita for the population of Bangladesh. The dominant polymer types observed using ATR-FTIR are PP and PE (17.5 %), PA (17.5 %) in the muscle tissues, and PP and PE (11.11 %), and EVA (11.11 %) in the GITs. Bottom-feeding species, such as demersal and benthic species, are more contaminated. However, the level of MPs in the species shows a negative correlation with the length and weight of the species. This study reveals that MP pollution is widespread and concerning in Bangladesh's Sundarban mangrove estuarine zone.

Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India

There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (<∼30%), large areas of agricultural land (>∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.

Falling "fortresses": Unlocking Governance Entanglements and Shifting Knowledge Paradigms to Counter Climate Change Threats in Biodiversity Conservation.

Biodiversity conservation is facing unprecedented challenges at the intersection of rapidly changing climates, widespread ecosystem degradation under the influence of global warming and resultant human tragedies over livelihood, habitation, adaptation and coping needs. These challenges are more acute across biodiversity hotspots in the Global South. This study disentangles the complex interplay to propose alternative paradigms of governance and policy thinking necessary for sustainable biodiversity conservation. Climate change impacts are exposing critical deficiencies of 'scientific forest management' pursued for over a century. For example, recurrent disasters and ecological shifts are increasingly obfuscating cognitive and physical boundaries between the reserve forest and human habitations; putting additional stress on livelihoods which in turn escalate pressures on the forest commons and fuel further conflicts between conservation governance and local communities. Instead of assisting in adaptation, the existing conservation governance mechanisms are producing further conflicts between humans and non-humans; livelihoods and conservation; disaster management and development. Conducted in the Sundarbans Biosphere Reserve -world's largest mangrove forest ecosystem and a climate change hotspot located along the Bay of Bengal across India and Bangladesh -the study finds an urgent need of rethinking and recalibrating biodiversity conservation in the times of climate change. However, institutional and market-based approaches such as promoting ecotourism or mangrove plantations may have little impact in this regard, the study finds. Instead, integrating cultural ecosystem services and co-producing knowledge will be critical to tackle the entanglements of climate change and its impacts on local lives, livelihoods and biodiversity conservation.

Ethnobotanical study of plants used by the Munda ethnic group living around the Sundarbans, the world's largest mangrove forest in southwestern Bangladesh

Ethnopharmacological relevanceIndigenous knowledge of medicinal plants is an integral part of the primary health care (PHC) system in almost every society. For more than two centuries, Munda, a small ethnic group in Bangladesh, has lived around the Sundarbans, the world's largest mangrove forest. This mangrove is rich in biodiversity but is threatened by global climate change. Information on the therapeutic use of plants by Munda ethnic minorities remains completely unknown. Therefore, it needs urgent documentation. Aim of the studyThe purpose of the study is (1) to search and compile data on the diversity of medicinal plants used by the Munda people for PHC needs, and (2) Quantitative analysis of these data to identify important medicinal plants and diseases related to treatment by this species. Material and methodsWe conducted repeated field surveys and interviews among 79 Munda informants to collect ethnobotanical data. Informants were selected through random sampling techniques and interviewed using an open and semi-structured questionnaire. We reported the primary (absolute) data as use reports (URs) with frequency citation (FC). The International Classification of Primary Care-2 (ICPC-2) was followed to categorize the therapeutic use of medicinal plants, and quantitative analysis was performed using the FC and informant consensus factor (ICF). ResultsThe present study explored and compiled a total of 3199 medicinal URs for 98 medicinal plant species to treat 132 ailment conditions under sixteen (16) ICPC-2 pathological groups. The highest URs (948) were noted for the digestive (D) group treated by 69 plant species, where the highest ICF value was measured for the social problem (Z) disease category (ICF: 1.00). Of the recorded medicinal plants, 17 were identified as true Mangrove (MNG), 24 as Mangrove Associates (MNA), and 57 as Non-Mangrove (NMG) species. Fabaceae (13 species) represented the leading family, followed by Lamiaceae and Compositae (5 species). Herbs (43%) have shown dominant life forms, and the leaves (41%) were frequently used plant parts. The most commonly cited preparation method was juice (24%), and the prevalent mode of administration was oral (62%). Azadirachta indica A. Juss. was the most widely used therapeutic plant species based on FC (39) values. The comparative literature review study reveals that the practices of 15 plants and their ethnomedicinal use by the Munda people are still entirely unexplored and newly reported in Bangladesh. Additionally, therapeutic use of 2 species, Brownlowia tersa (L.) Kosterm., and Dalbergia candenatensis (Dennst.) Prain has not been previously reported worldwide. In addition, 51 plant species (52%) of the total plants studied enlisted on the IUCN Red List of Threatened Species. ConclusionTo our knowledge, this is the first ethnobotanical study on the Munda ethnic group in Bangladesh. This study indicates that Munda people still rely on medicinal plants for PHC and have a rich and varied traditional knowledge about the therapeutic use of plants. This study also warns of the high risk of the current availability status of plants in the study area. Therefore, this study calls for urgent steps to protect and conserve high-risk plants that can be done by taking both in-situ and ex-situ measures. In addition, further phytochemical and pharmacological investigations of the important medicinal plants cited in the study have been suggested.

Ecologies of capture in Bangladesh's Sundarbans

ABSTRACTWhat is the relationship between climate change and criminal predation? Bangladesh's Sundarbans, the world's largest mangrove forest, has, over the past decade, emerged as a climate frontier. It is a space viewed not only as a climate hot spot but also as a zone where control and opportunity emerge out of friction between long‐standing political economies, new conservation interventions, and the materialities of the mangrove forest. Concomitantly, those who work in the Sundarbans have reported a dramatic increase in banditry and kidnapping. This article proposes the concept of an “ecology of capture” to chart the articulations between such kidnappings and other attempts to control rents, resources, and territory. Seen through the lens of capture, the Sundarbans highlights how the global rush to secure climate hot spots against degradation and displacement produces new configurations of expropriation and exploitation. [climate change, piracy, capture, conservation, Sundarbans, Bangladesh]

Climate vulnerability and economic determinants: Linkages and risk reduction in Sagar Island, India; A geospatial approach

Climate vulnerability is the propensity to be adversely affected by climatic variability and natural hazards. People of low-lying islands face accelerating physical and non-physical stresses due to their greater exposure, higher sensitivity, and lower adaptive capacity towards climate change. Being home to over 0.2 million people, Sagar Island, the largest Island of Sundarban, is highly susceptible to coastal erosion, cyclonic storm surges, flooding, and embankment breaching, causing loss of land, property, and livelihood that eventually lead to displacements and induced migration. Over the past two decades, residents of this fragile sinking island of this world's largest mangrove forest have seen their homes engulfed, farmlands salinized and livelihood depleted by the rising sea. This study investigates the association between socio-economic vulnerabilities through the contextual indicators of land loss, productivity loss, loss of livelihood, and induced migration through regression modeling to find out linkages and scopes of risk reduction. The result reflects a significant positive correlation between land loss and loss of livelihood (p 0.0494*) that negatively correlates with the occurrence of in-migration (z −0.826), while the correlation between productivity loss and loss of livelihood is 0.6318. Mouzas like Dhablat, Shibpur, Beguakhali show outlier characteristics due to severe rates of erosion and embankment failure. The secondary data from the India Meteorological Department, National Centre for Ocean Information Services, have been used to identify considerable changes from cumulative erosion and accretion employing statistical and GIS techniques. This bottom-up assessment can contribute to disaster management and policy implications by identifying the particular intercession at the mouza level that increases the resilience of these vulnerable communities to climate change.

Hot spot ($${G}_{i}^{{*}}$$) model for forest vulnerability assessment: a remote sensing-based geo-statistical investigation of the Sundarbans mangrove forest, Bangladesh

The 5th category super-cyclone Sidr disrupts the world's largest mangrove forest Sundarbans on November 15, 2007. It seriously shatters about 1900 km2 that 31% of the total area of the Sundarbans. That makes a great threat to the mangrove ecosystem and biodiversity, which convey to forest vulnerability monitoring of Sundarbans. This research emphasizes on mangrove forest monitoring with vulnerability assessment using Landsat-5 and Landsat-8 remote sensing data based on geo-statistical hot spot ( $${G}_{i}^{{*}}$$ ) model, normalized difference vegetation index (NDVI) and forest discrimination index (FDI). However, the analysis works with statistical algorithm Gi(d) and G(d) in terms of geo-statistical nearest neighborhood spatial autocorrelation analysis. Hot spot ( $${G}_{i}^{{*}}$$ ) model used to explore the hot and cold confidence zone, which provided the mangrove vulnerability confidence level. The simulated, ~ 14.1% extreme safe zone is increased from 2001 to 2015 and extremely vulnerable zone also increased 4.1% at the same time, although 4.3% stable zone also decreased in that time. Even, high-density mangrove area was decreased in 2009, and the low-density mangrove area increased due to cyclone Sidr. In addition, FDI denotes the mangrove density, and NDVI provides vegetation health condition and represents the mangrove variability scenario with geospatial location those signify to detect the threatening condition of mangrove population and density. Furthermore, this study’s methods and results will provide the base for further long-term studies on this world’s largest mangrove forest and would have an implication for the mangrove monitoring and disaster risk reduction strategies.

Climate change and variability impacts on the forests of Bangladesh – a diagnostic discussion based on CMIP5 GCMs and ENSO

ABSTRACTThe aim of this study is to assess the evolution of forests in Bangladesh from the perspective of changing climate, with particular emphasis on the Sundarbans, the world's largest mangrove forest. Projections for temperature and rainfall change have been made using the latest Intergovernmental Panel on Climate Change‐Fifth Assessment Report (IPCC‐AR5) general circulation models (GCMs) protocol (Coupled Model Intercomparison Project Phase 5, CMIP5) with 38 various GCMs and up to 105 model runs. The GCMs findings were verified by the published results of 11 regional climate models (RCMs) in the Coordinated Regional Climate Downscaling Experiment (CORDEX)‐South Asia models with finer spatial resolution. A CMIP5 model‐based synthesis review on the future variability of El Niño‐Southern Oscillation (ENSO) has also been made, as both the seasonal temperature and rainfall variability in Bangladesh are correlated to ENSO. Both global and regional climate change scenarios indicate that temperature would continue to rise and total annual rainfall is likely to increase in the future. However, weak increases in rainfall combined with high increases in temperature would create a high demand and competition for water because of the higher evapotranspiration environment. In addition to changing climate, the interannual variability of ENSO would further aggravate problems because it displays important correlations with the variability of rainfall, temperatures, sea‐level, lightning, and forest fires in Bangladesh. This study strongly recommends that future climate change projections should be combined with interannual variability ENSO to improve climate perspectives for stronger forest capacity building efforts in Bangladesh.

Two and Half Century’s Changes of World Largest Mangrove Forest: A Geo-informatics Based Study on Sundarbans Mangrove Forest, Bangladesh, India

Two and Half Century’s Changes of World Largest Mangrove Forest: A Geo-informatics Based Study on Sundarbans Mangrove Forest, Bangladesh, India

Environmental Impact of Coal based Power Plant of Rampal on the Sundarbans (World Largest Mangrove Forest) and Surrounding Areas

The physico chemical conditions of air water and soil and biological conditions of the proposed Coal based Power Plant area Rampal Mongla and the Sundrabans were studied from August to July to assess the possible environmental impact on the Sundarbans and surrounding areas Environmental Impact Assessment EIA of physical biological social and economic environment of the study areas indicate that most of the impacts of coal fired power plant are negative and irreversible which can rsquo t be mitigated in any way It is indicating that climate topography land use pattern air and water quality floral and faunal diversity aquatic ecosystems capture fisheries and tourism of the Sundarbans and the surroundings areas would be affected permanently due to proposed coal fired power plant Increasing of water logging conditions river erosion noise pollution and health hazards decreasing of ground water table loss of culture fisheries social forestry and major destruction of agriculture would be happened due to coal fired power plant The benefits of proposed coal fired power plant of Rampal is very poor S than that of negative irreversible impact So the proposed area is not suitable to establish the coal based power plant as the Sundarbans and surrounding areas would be affected permanently by establishing the proposed coal power plant

Local community attitudes towards mangrove forest conservation: Lessons from Bangladesh

Degradation of mangrove ecosystems is a major concern in many countries of the world. This study examines the attitudes and perceptions of local communities towards the conservation of the world's largest mangrove forest, the Sundarbans in Bangladesh. Village households were interviewed. The Schlager and Ostrom's theoretical framework was applied to reveal local forest-dependent people's positive attitudes towards conservation and that their socio-economic and demographic characteristics influenced these attitudes. They valued those functions that were directly linked to their economic wellbeing and were inclined to engage in alternative livelihood activities provided illegal access to forest resources were curtailed. Respondents living adjacent to the forests admitted their illegal access due to a lack of alternative economic opportunities. 60% of the respondents viewed that present state property rights regime violated their harvesting rights. More than half of the people (57%) surveyed desired to participate in conservation practices as proprietors. Policy directives in line with Schlager and Ostrom's theory for de facto and de jure ownerships and management change may provide some scope for development of sustainable livelihoods for forest dependent people from the conservation of critical mangrove ecosystems like the Sundarbans.

Examining the ecosystem health and sustainability of the world's largest mangrove forest using multi-temporal MODIS products

Sweeping across Bangladesh and India, the Sundarbans forest is the world's largest contiguous mangrove forest. Although the human population density is high at the edge, Sundarbans has not encountered significant areal transformation in the last four decades. However, we argue that forest degradation can occur discontinuously within the forest without alteration of the entire forest area. In this paper, we used MODIS land products to compare the spatiotemporal ecological dynamics of the Bangladesh and Indian part of this mangrove forest between 2000 and 2010. We used the following 5 ecological parameters for our analysis: the Percent Tree Cover (PTC), Enhanced Vegetation Index (EVI), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Evapotranspiration (ET). Our pixel-based time-series trend analysis for each MODIS image stack, using an ordinary least square (OLS) regression method, showed that forest degradation is happening in fragmented parcels within the forest. The degradation rate is comparatively higher in the Bangladesh part than in the Indian part of Sundarbans. Compartments 8, 10, 12, and 15 in the Bangladesh part, in particular, show high degradation, while compartment 48 and the southern edge of 45 show slight increases in PTC or EVI. Forest degradation in the Indian part of the forest is evident in the National Park and Reserve Forest blocks; however, no substantial degradation is evident in the western section. We have identified certain anthropogenic stressors (i.e., oil pollution, shrimp farming) and natural stressors (i.e., increased salinity, cyclones, forest fire) which might be responsible for the observed degradation. We have provided sustainable planning options and policy transformation alternatives for those areas under pressure from these stressors. We anticipate that our analysis of forest degradation will help management agencies, conservators, and policy makers achieve better management of this world's largest mangrove forest for a sustainable future.