GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020.

Academic Article

Abstract

  • The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.
  • Authors

  • Zuo, Jian
  • Zhang, Li
  • Xiao, Jingfeng
  • Chen, Bowei
  • Zhang, Bo
  • Hu, Yingwen
  • Mamun, MM Abdullah Al
  • Wang, Yang
  • Li, Kaixin
  • Publication Date

  • January 22, 2025
  • Published In

  • Scientific data  Journal
  • Digital Object Identifier (doi)

    Start Page

  • 129
  • Volume

  • 12
  • Issue

  • 1