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<p>Coastal erosion, driven by climate change, sea-level rise, and human activities, poses critical risks to coastal ecosystems and communities worldwide. Understanding how the scientific community has investigated shoreline change is essential for advancing monitoring and management strategies. This study presents a bibliometric review of global research on shoreline change detection, analysing 2,711 publications (2014–2024) retrieved from Scopus and Web of Science. Following duplicate and language screening, 2,009 studies were assessed using Bibliometric R and VOSviewer. Results indicate that the United States leads global output (33.7%), with collaborations concentrated among developed nations. A marked methodological shift was observed from traditional beach transects to geospatial technologies, with image-based approaches dominating (62.8%), followed by numerical (26.9%) and field-based (10.3%) methods. Tools such as DSAS and ArcGIS remain central, while machine learning applications, though limited (3.4%), show increasing potential for automation and predictive modeling. Research gaps persist, particularly in integrating nature-based shoreline protection measures and applying AI-driven approaches. Future directions should prioritize interdisciplinary frameworks that combine remote sensing, GIS, numerical modelling, and machine learning to improve shoreline monitoring and resilience.</p>

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Geospatial data and analytical tools for shoreline change monitoring: a global trend analysis

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Anan Al-Marri,
  • Noorashikin Md Noor,
  • Hazrat Bilal,
  • Khairul Nizam Abdul Maulud,
  • Ricky Anak Kemarau,
  • Tareq Al-Ansari

摘要

Coastal erosion, driven by climate change, sea-level rise, and human activities, poses critical risks to coastal ecosystems and communities worldwide. Understanding how the scientific community has investigated shoreline change is essential for advancing monitoring and management strategies. This study presents a bibliometric review of global research on shoreline change detection, analysing 2,711 publications (2014–2024) retrieved from Scopus and Web of Science. Following duplicate and language screening, 2,009 studies were assessed using Bibliometric R and VOSviewer. Results indicate that the United States leads global output (33.7%), with collaborations concentrated among developed nations. A marked methodological shift was observed from traditional beach transects to geospatial technologies, with image-based approaches dominating (62.8%), followed by numerical (26.9%) and field-based (10.3%) methods. Tools such as DSAS and ArcGIS remain central, while machine learning applications, though limited (3.4%), show increasing potential for automation and predictive modeling. Research gaps persist, particularly in integrating nature-based shoreline protection measures and applying AI-driven approaches. Future directions should prioritize interdisciplinary frameworks that combine remote sensing, GIS, numerical modelling, and machine learning to improve shoreline monitoring and resilience.