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<p>Accurately measuring vegetation height is essential for understanding ecosystem structure, carbon storage, and biodiversity, yet global height models have overwhelmingly focused on forests, excluding ecosystems with shorter herbaceous vegetation or shrubs. To address this gap in vegetation structure data, we developed the first global estimate of median vegetation height annually from 2000–2022 at 30 m resolution, using ICESat-2 satellite Lidar, Landsat cloud free composites, and other Earth Observation raster data. Thirty two (32) million ICESat-2 20 m segments were used within 10 independent draws to build ensemble Gradient Boosted Tree (GBT) models and estimate 90% prediction intervals. Our model achieves a root mean square error (RMSE) of 2.35 m, R<sup>2</sup> values of 0.515 and a D<sup>2</sup> regression score of 0.62 estimated on the testing set. Comparisons with existing global height products show that our approach increases detail and heterogeneity of height in short vegetation ecosystems. Output maps are publicly available together with reference samples and trained models under CC-BY license.</p>

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Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月23日

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  • Maria O. Hunter,
  • Leandro Parente,
  • Yu-feng Ho,
  • Carmelo Bonannella,
  • Laerte Guimarães Ferreira,
  • Douglas Morton,
  • Davide Consoli,
  • Lindsey Sloat

摘要

Accurately measuring vegetation height is essential for understanding ecosystem structure, carbon storage, and biodiversity, yet global height models have overwhelmingly focused on forests, excluding ecosystems with shorter herbaceous vegetation or shrubs. To address this gap in vegetation structure data, we developed the first global estimate of median vegetation height annually from 2000–2022 at 30 m resolution, using ICESat-2 satellite Lidar, Landsat cloud free composites, and other Earth Observation raster data. Thirty two (32) million ICESat-2 20 m segments were used within 10 independent draws to build ensemble Gradient Boosted Tree (GBT) models and estimate 90% prediction intervals. Our model achieves a root mean square error (RMSE) of 2.35 m, R2 values of 0.515 and a D2 regression score of 0.62 estimated on the testing set. Comparisons with existing global height products show that our approach increases detail and heterogeneity of height in short vegetation ecosystems. Output maps are publicly available together with reference samples and trained models under CC-BY license.