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...
Saved in:
| Main Authors: | Maria O. Hunter, Leandro Parente, Yu-feng Ho, Carmelo Bonannella, Laerte Guimarães Ferreira, Douglas Morton, Davide Consoli, Lindsey Sloat |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05739-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning
by: Leandro Parente, et al.
Published: (2024-12-01) -
Light use efficiency (LUE) based bimonthly gross primary productivity (GPP) for global grasslands at 30 m spatial resolution (2000–2022)
by: Mustafa Serkan Isik, et al.
Published: (2025-08-01) -
A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution
by: Davide Consoli, et al.
Published: (2024-12-01) -
Accuracy fluctuations of ICESat-2 height measurements in time series
by: Xu Wang, et al.
Published: (2024-12-01) -
ICESat-2 Performance for Terrain and Canopy Height Retrieval in Complex Mountainous Environments
by: Lianjin Fu, et al.
Published: (2025-05-01)