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  • 标题:Mapping Mangrove Opportunities with Open Access Data: A Case Study for Bangladesh
  • 本地全文:下载
  • 作者:Alejandra Gijón Mancheño ; Peter M. J. Herman ; Sebastiaan N. Jonkman
  • 期刊名称:Sustainability
  • 印刷版ISSN:2071-1050
  • 出版年度:2021
  • 卷号:13
  • 期号:15
  • 页码:8212
  • DOI:10.3390/su13158212
  • 语种:English
  • 出版社:MDPI, Open Access Journal
  • 摘要:Mangroves protect coastal areas against hazards like storms or cyclones by attenuating waves and currents, and by trapping floating debris during extreme events. Bangladesh is a very vulnerable country to floods and cyclones, and part of its coastal system is thus being upgraded to a higher safety standard. These upgrades include embankment reinforcement and mangrove afforestation schemes seawards of the embankments. To further strengthen the implementation of combined green–grey infrastructure in future programs, identifying potential mangrove development sites near the polder systems is a necessary first step. We thus developed a tool to systematically identify mangrove sites throughout the coastal area based on open access data. This method identifies potential sites for mangrove development based on their distance from existing mangrove patches and suggests the required technique to implement the vegetation depending on the rate of coastline change. Our method showed that approximately 600 km of the coastal stretches placed seawards of embankments are within 10 km of existing mangroves, and could thus be potential sites for mangrove establishment. Out of those 600 km, we identified 140 km of coastline where the landwards polders are particularly vulnerable to flooding. The sites with highest restoration potential and priority are located in Galachipa, Hatiya, Bhola, Manpura, Khangona, and Boro Moheshkhali. More detailed data collection and local assessments are recommended prior to executing mangrove afforestation schemes. Nevertheless, this method could serve as a useful systematic tool for feasibility studies that identify mangrove opportunities in data-scarce areas and help to prioritize data collection at the sites of highest interest.
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