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Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning.

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Title: Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning.
Authors: Mondal, Ismail1 (AUTHOR) ismailmondal58@gmail.com, Naskar, Pijush Kanti1 (AUTHOR) pijushkanti175@gmail.com, Alsulamy, Saleh2 (AUTHOR) s.alsulamy@kku.edu.sa, Jose, Felix3 (AUTHOR) fjose@fgcu.edu, Hossain, SK. Ariful4 (AUTHOR) hossainskariful@gmail.com, Mohammad, Lal5 (AUTHOR) lalmohammadwb@gmail.com, De, Tarun Kumar1 (AUTHOR) tarunde@yahoo.co.in, Khedher, Khaled Mohamed6 (AUTHOR) kkhedher@kku.edu.sa, Salem, Mohamed Abdelaziz7 (AUTHOR) moabdulaziz@kku.edu.sa, Benzougagh, Brahim8 (AUTHOR) brahim.benzougagh@is.um5.ac.ma, Juliev, Mukhiddin9,10,11 (AUTHOR) mukhiddinjuliev@gmail.com
Source: Environment, Development & Sustainability. Mar2026, Vol. 28 Issue 3, p6757-6782. 26p.
Subject Terms: *Mangrove forests, *Environmental degradation, *Habitat destruction, *Ecosystem services, *Nature conservation, *Machine learning, *Habitat conservation
Geographic Terms: Bangladesh
Abstract: The startling rate of biodiversity loss, particularly in ecologically delicate coastal environments, is a major environmental concern facing the globe today. As sustainable exploitation of natural resources, including timber and other forest products from tropical rainforests and mangrove habitats, is crucial this research will examine the factors and processes that degrade mangrove habitats -in terms of their health and resilience -and suggest ways to reduce human impact. The study evaluates the evolution of Sundarbans—the largest contiguous mangrove forest in the world—coastal habitat quality from 2017 to 2022 using the InVEST and machine learning-based ANN model. The mangrove habitat sustained heavy destruction, including structural damage from the landfall of cyclone Amphan in May 2020. Spatial auto-correlation method and Geotagging were employed for location-dependent habitat quality analysis. Study demonstrates that habitat quality and degradation vary significantly across the Sundarbans mangrove forest provinces, particularly for habitat quality spatial distribution and their degradation. Important determinants for habitat quality are per capita water usage, night-time light index (proxy for population density), forest area, and prevalence of fragmented and degrading forest area. All factor pairings are bifactor or non-linear enhanced, showing that impact of two variables combined is more powerful than one alone in determining ecosystem quality and degeneration. In particular, forest land coverage and per capita water consumption have strong correlations with the habitat quality in the region. Geographical disparity of habitat quality and its probable causes suggests an urgent need for system-wide approach in implementing conservation and restoration measures for preserving Sundarbans mangroves, which spread across the international boundary between India and Bangladesh. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mondal%2C+Ismail%22">Mondal, Ismail</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ismailmondal58@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Naskar%2C+Pijush+Kanti%22">Naskar, Pijush Kanti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pijushkanti175@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Alsulamy%2C+Saleh%22">Alsulamy, Saleh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> s.alsulamy@kku.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Jose%2C+Felix%22">Jose, Felix</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> fjose@fgcu.edu</i><br /><searchLink fieldCode="AR" term="%22Hossain%2C+SK%2E+Ariful%22">Hossain, SK. Ariful</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> hossainskariful@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mohammad%2C+Lal%22">Mohammad, Lal</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> lalmohammadwb@gmail.com</i><br /><searchLink fieldCode="AR" term="%22De%2C+Tarun+Kumar%22">De, Tarun Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tarunde@yahoo.co.in</i><br /><searchLink fieldCode="AR" term="%22Khedher%2C+Khaled+Mohamed%22">Khedher, Khaled Mohamed</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> kkhedher@kku.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Salem%2C+Mohamed+Abdelaziz%22">Salem, Mohamed Abdelaziz</searchLink><relatesTo>7</relatesTo> (AUTHOR)<i> moabdulaziz@kku.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Benzougagh%2C+Brahim%22">Benzougagh, Brahim</searchLink><relatesTo>8</relatesTo> (AUTHOR)<i> brahim.benzougagh@is.um5.ac.ma</i><br /><searchLink fieldCode="AR" term="%22Juliev%2C+Mukhiddin%22">Juliev, Mukhiddin</searchLink><relatesTo>9,10,11</relatesTo> (AUTHOR)<i> mukhiddinjuliev@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Mar2026, Vol. 28 Issue 3, p6757-6782. 26p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Mangrove+forests%22">Mangrove forests</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+degradation%22">Environmental degradation</searchLink><br />*<searchLink fieldCode="DE" term="%22Habitat+destruction%22">Habitat destruction</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecosystem+services%22">Ecosystem services</searchLink><br />*<searchLink fieldCode="DE" term="%22Nature+conservation%22">Nature conservation</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Habitat+conservation%22">Habitat conservation</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bangladesh%22">Bangladesh</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The startling rate of biodiversity loss, particularly in ecologically delicate coastal environments, is a major environmental concern facing the globe today. As sustainable exploitation of natural resources, including timber and other forest products from tropical rainforests and mangrove habitats, is crucial this research will examine the factors and processes that degrade mangrove habitats -in terms of their health and resilience -and suggest ways to reduce human impact. The study evaluates the evolution of Sundarbans—the largest contiguous mangrove forest in the world—coastal habitat quality from 2017 to 2022 using the InVEST and machine learning-based ANN model. The mangrove habitat sustained heavy destruction, including structural damage from the landfall of cyclone Amphan in May 2020. Spatial auto-correlation method and Geotagging were employed for location-dependent habitat quality analysis. Study demonstrates that habitat quality and degradation vary significantly across the Sundarbans mangrove forest provinces, particularly for habitat quality spatial distribution and their degradation. Important determinants for habitat quality are per capita water usage, night-time light index (proxy for population density), forest area, and prevalence of fragmented and degrading forest area. All factor pairings are bifactor or non-linear enhanced, showing that impact of two variables combined is more powerful than one alone in determining ecosystem quality and degeneration. In particular, forest land coverage and per capita water consumption have strong correlations with the habitat quality in the region. Geographical disparity of habitat quality and its probable causes suggests an urgent need for system-wide approach in implementing conservation and restoration measures for preserving Sundarbans mangroves, which spread across the international boundary between India and Bangladesh. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192418055
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10668-024-05257-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 6757
    Subjects:
      – SubjectFull: Mangrove forests
        Type: general
      – SubjectFull: Environmental degradation
        Type: general
      – SubjectFull: Habitat destruction
        Type: general
      – SubjectFull: Ecosystem services
        Type: general
      – SubjectFull: Nature conservation
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Habitat conservation
        Type: general
      – SubjectFull: Bangladesh
        Type: general
    Titles:
      – TitleFull: Habitat quality and degradation change analysis for the Sundarbans mangrove forest using invest habitat quality model and machine learning.
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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