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<p>Hurricane Ian caused aboveground biomass density (AGBD) losses across Florida’s forests in the United States, highlighting the need for accurate, large-scale monitoring tools. We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. Validation using <i>in situ</i> field data demonstrated high model performance, with an R<sup>2</sup> of 0.93 and a root mean square difference (RMSD) of 39.3%. Spatial uncertainty reflecting bootstrap-derived variance remained consistent, with relative standard errors around 90% across the years analyzed. The data are accessible through a web application, RapidFEM4D, enabling researchers and stakeholders to assess AGBD maps for areas of interest. These datasets support monitoring forest recovery, assessing carbon dynamics, and guiding post-hurricane management and restoration. The RapidFEM4D platform facilitates access and analysis of Hurricane Ian’s impact on Florida’s forests, empowering stakeholders with actionable insights and offering a model for similar efforts in other hurricane-prone regions.</p>

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Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

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  • Inacio T. Bueno,
  • Carlos A. Silva,
  • Caio Hamamura,
  • Victoria M. Donovan,
  • Ajay Sharma,
  • Jiangxiao Qiu,
  • Jinyi Xia,
  • Kody M. Brock,
  • Monique B. Schlickmann,
  • Jeff W. Atkins,
  • Denis R. Valle,
  • Jason Vogel,
  • Andres Susaeta,
  • Mauro A. Karasinski,
  • Carine Klauberg

摘要

Hurricane Ian caused aboveground biomass density (AGBD) losses across Florida’s forests in the United States, highlighting the need for accurate, large-scale monitoring tools. We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. Validation using in situ field data demonstrated high model performance, with an R2 of 0.93 and a root mean square difference (RMSD) of 39.3%. Spatial uncertainty reflecting bootstrap-derived variance remained consistent, with relative standard errors around 90% across the years analyzed. The data are accessible through a web application, RapidFEM4D, enabling researchers and stakeholders to assess AGBD maps for areas of interest. These datasets support monitoring forest recovery, assessing carbon dynamics, and guiding post-hurricane management and restoration. The RapidFEM4D platform facilitates access and analysis of Hurricane Ian’s impact on Florida’s forests, empowering stakeholders with actionable insights and offering a model for similar efforts in other hurricane-prone regions.