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<p>Assessing coastal environmental changes has become increasingly complex due to the accelerating impacts of climate change. This study presents a systematic review of the effects of climate change on vegetation dynamics and coastal morphology along the Niger Delta coastline between 2006 and 2024, using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The analysis highlights the growing global adoption of advanced monitoring approaches, particularly those that integrate machine learning with hyperspectral satellite data, which significantly improve the accuracy of coastal predictions and environmental monitoring compared with conventional techniques. The findings reveal that widely applied approaches include machine learning algorithms, ALES+ signal processing, intertidal mapping, infrared sensing, and satellite-based monitoring, while several other techniques remain underutilized, indicating methodological gaps in coastal studies. The review also identifies major climate-driven threats affecting the Niger Delta coastal environment, including saltwater intrusion, sea-level rise, flooding, and stormwater impacts. The study underscores the urgent need for Nigeria to transition from reliance on conventional remote sensing and GIS approaches towards advanced hybrid machine-learning frameworks integrated with hyperspectral datasets. Adoption of these technologies will enhance early detection of coastal risks and support the sustainable management of vulnerable coastal ecosystems and communities in the Niger Delta.</p> Graphical Abstract <p></p>

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Evaluating the significance of climate change on vegetation and coastal morphology in the Niger Delta through bibliometric analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Temple Okah Arikpo,
  • Stanley Ugochukwu Nwoke,
  • Nchekwube D. Nweke,
  • Kevin Emeka Agbo,
  • Chiedozie Chukwuemeka Aralu,
  • Ifeanyi Adolphus Ucheana,
  • Michael Ekuru Omeka,
  • Arinze Longinus Ezugwu,
  • Johnson C. Agbasi,
  • Johnbosco C. Egbueri,
  • Hillary Onyeka Abugu

摘要

Assessing coastal environmental changes has become increasingly complex due to the accelerating impacts of climate change. This study presents a systematic review of the effects of climate change on vegetation dynamics and coastal morphology along the Niger Delta coastline between 2006 and 2024, using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The analysis highlights the growing global adoption of advanced monitoring approaches, particularly those that integrate machine learning with hyperspectral satellite data, which significantly improve the accuracy of coastal predictions and environmental monitoring compared with conventional techniques. The findings reveal that widely applied approaches include machine learning algorithms, ALES+ signal processing, intertidal mapping, infrared sensing, and satellite-based monitoring, while several other techniques remain underutilized, indicating methodological gaps in coastal studies. The review also identifies major climate-driven threats affecting the Niger Delta coastal environment, including saltwater intrusion, sea-level rise, flooding, and stormwater impacts. The study underscores the urgent need for Nigeria to transition from reliance on conventional remote sensing and GIS approaches towards advanced hybrid machine-learning frameworks integrated with hyperspectral datasets. Adoption of these technologies will enhance early detection of coastal risks and support the sustainable management of vulnerable coastal ecosystems and communities in the Niger Delta.

Graphical Abstract