Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05739-6 |
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| author | Maria O. Hunter Leandro Parente Yu-feng Ho Carmelo Bonannella Laerte Guimarães Ferreira Douglas Morton Davide Consoli Lindsey Sloat |
| author_facet | Maria O. Hunter Leandro Parente Yu-feng Ho Carmelo Bonannella Laerte Guimarães Ferreira Douglas Morton Davide Consoli Lindsey Sloat |
| author_sort | Maria O. Hunter |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-68ebe1dcc8a044b8bf58895c755b2269 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-68ebe1dcc8a044b8bf58895c755b22692025-08-24T11:07:22ZengNature PortfolioScientific Data2052-44632025-08-0112111710.1038/s41597-025-05739-6Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine LearningMaria O. Hunter0Leandro Parente1Yu-feng Ho2Carmelo Bonannella3Laerte Guimarães Ferreira4Douglas Morton5Davide Consoli6Lindsey Sloat7Remote Sensing and GIS Laboratory (LAPIG/UFG)OpenGeoHub FoundationOpenGeoHub FoundationOpenGeoHub FoundationRemote Sensing and GIS Laboratory (LAPIG/UFG)NASA Goddard Space Flight CenterOpenGeoHub FoundationLand & Carbon Lab, World Resources InstituteAbstract 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.https://doi.org/10.1038/s41597-025-05739-6 |
| spellingShingle | Maria O. Hunter Leandro Parente Yu-feng Ho Carmelo Bonannella Laerte Guimarães Ferreira Douglas Morton Davide Consoli Lindsey Sloat Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning Scientific Data |
| title | Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning |
| title_full | Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning |
| title_fullStr | Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning |
| title_full_unstemmed | Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning |
| title_short | Global 30-m annual median vegetation height maps (2000–2022) based on ICESat-2 data and Machine Learning |
| title_sort | global 30 m annual median vegetation height maps 2000 2022 based on icesat 2 data and machine learning |
| url | https://doi.org/10.1038/s41597-025-05739-6 |
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