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<p>As a key ecological barrier and core carbon sink area in Southern China, Nanping City presents significant challenges for high-accuracy forest canopy height mapping due to its unique vegetation structure and complex topography. The current products for canopy height couldn’t meet the requirements for accurate carbon stock estimation. This study calibrated the biases in GEDI data by proposing an innovative Bias Calibration Model and combined with multi-source data including Sentinel-1, 2, UAV LiDAR, and topographic data, produced 10-meter canopy height products for 2022 and 2023 for Nanping City using a random forest model. The generated data products achieved R² = 0.62 for both 2022 and 2023 when validated against independent UAV data, with RMSE = 2.88 m and 3.09 m for 2022 and 2023 respectively, accurately characterizing the vertical structural attributes of the forest This study provides a reliable data foundation for the estimation of subsequent forest biomass and carbon stock.</p>

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A 10 m High-precision Canopy Height Product for Nanping City, Fujian Province, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Ling Yi,
  • Xiaojing Yao,
  • Aixia Yang,
  • Liqiang Zhang,
  • Dacheng Wang,
  • Yue Jiao,
  • Yaoliang Chen,
  • Shufu Liu,
  • Gang Chen,
  • Yalan Liu

摘要

As a key ecological barrier and core carbon sink area in Southern China, Nanping City presents significant challenges for high-accuracy forest canopy height mapping due to its unique vegetation structure and complex topography. The current products for canopy height couldn’t meet the requirements for accurate carbon stock estimation. This study calibrated the biases in GEDI data by proposing an innovative Bias Calibration Model and combined with multi-source data including Sentinel-1, 2, UAV LiDAR, and topographic data, produced 10-meter canopy height products for 2022 and 2023 for Nanping City using a random forest model. The generated data products achieved R² = 0.62 for both 2022 and 2023 when validated against independent UAV data, with RMSE = 2.88 m and 3.09 m for 2022 and 2023 respectively, accurately characterizing the vertical structural attributes of the forest This study provides a reliable data foundation for the estimation of subsequent forest biomass and carbon stock.