Analysis of erosion–accretion dynamics of major rivers of world’s largest mangrove forest using geospatial techniques

Sohel, Md. Shawkat Islam; Hore, Sudipta Kumar; Salam, Mohammed Abdus; Hoque, Muhammad Al-Amin; Ahmed, Naser; Rahman, Mohammad Moshiur; Khan, Haniyum Maria; Rahman, Md. Sohanur

doi:10.1016/j.rsma.2021.101901

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…This oil deposits on soil, plant leaves, and roots which cause substantial damage to biodiversity and ecological balance of the surrounding area (Rahman Sunny, 2017). The southwestern part of Bangladesh especially the mangrove forest near Mongla Port experienced tremendous vegetation loss over the last 40 years (Sohel et al, 2021). The southern part of Bangladesh is now facing a higher increasing rate of daily temperature than other parts due to global warming (Hamjah, 2014).…”

Section: Introductionmentioning

confidence: 99%

Effect of Uncontrolled Industrialization on Environmental Parameter: A Case Study of Mongla EPZ Using Machine Learning Approach

Ahmed,

Uddin,

Saha

2024

Preprint

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The unplanned and uncontrolled industrialization leads to environmental pollution which ends in impacting human life and destroying the economy. Especially in the era of global warming coastal regions of all over the world are the most vulnerable which holds significant ecological importance for human habitations. In 1998 the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal thana Mongla which is already famous for its seaport has led the area to the challenges of salinity intrusion and shrinking of agricultural land and its fertility. In this paper, the effect of unplanned industrialization inside the Mongla EPZ are on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from the year 2007 to 2023 has been investigated. Along with that a machine learning-based Artificial Neural Network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area will face drought which has become over 30% by 2023. The NDVI values are decreasing year-wise which reveals that the area is becoming vegetation less. Also, the increasing industrial activities in the EPZ turned LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27°C by 2031. Along with that the area's UHI values over 2°C than the rural surrounding will reach 6.5% by 2031. The study investigated the devastating impact of unplanned industrialization in EPZ on the surrounding environment and ecosystem.

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Section: Introductionmentioning

confidence: 99%

Effect of Uncontrolled Industrialization on Environmental Parameter: A Case Study of Mongla EPZ Using Machine Learning Approach

Ahmed,

Uddin,

Saha

2024

Preprint

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Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

Ahmed,

Uddin,

Saha

2024

Preprint

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The transformation induced by development in our environment leads to pollution, impacting both human life and economic output. Coastal regions, particularly vulnerable in the era of global climate change, bear significant ecological importance for habitation. The establishment of the Mongla Export Processing Zone in 1998 in the coastal thana of Mongla, Bangladesh, renowned for its seaport, has resulted in pronounced challenges such as salinity intrusion and diminished agricultural fertility. This study, spanning from 2007 to 2023, scrutinizes the influence of land use and land cover (LULC) on the land surface temperature (LST), urban heat island (UHI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) in the Mongla EPZ. Employing a deep learning-based Artificial Neural Network (ANN) model, predictions for 2027 and 2031 are derived. A noteworthy finding revolves around settlement dynamics, with virtually no settlement before 2011, experiencing a substantial increase (8.27%) thereafter. The NDWI analysis underscores the region's drought-free status before 2011, evolving into exposure to severe (10.12%) and moderate (15.06%) drought conditions with increased industrialization. The vegetation undergoes an inverse transformation. The mean temperature exhibits an ascending trend due to industrialization, soaring from 18.9°C in 2007 to 21.61°C in 2023. The predictive CA-ANN algorithm anticipates a further rise, projecting a substantial portion of the LST escalating to 27 degrees Celsius, covering an estimated 28.33% of the municipal area by 2031. Additionally, areas with LST values 2°C higher than the surroundings are expected to reach 6.5% by 2031. The study underscores the profound impact of industrialization within EPZs on the surrounding environment and ecosystem.

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Analysis of erosion–accretion dynamics of major rivers of world’s largest mangrove forest using geospatial techniques

Cited by 2 publications

References 35 publications

Effect of Uncontrolled Industrialization on Environmental Parameter: A Case Study of Mongla EPZ Using Machine Learning Approach

Effect of Uncontrolled Industrialization on Environmental Parameter: A Case Study of Mongla EPZ Using Machine Learning Approach

Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

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