A high-resolution canopy height model of the Earth
Open accessAbstract
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change and prevent biodiversity loss. Here we present a comprehensive global canopy height map at 10 m ground sampling distance for the year 2020. We have developed a probabilistic deep learning model that fuses sparse height data from the Global Ecosystem Dynamics Investigation (GEDI) space-borne LiDAR mission with dense optical satellite images from Sentinel-2. This model retrieves canopy-top height from Sentinel-2 images anywhere on Earth and quantifies the uncertainty in these estimates. Our approach improves the retrieval of tall canopies with typically high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Further, we find that only 34% of these tall canopies are located within protected areas. Thus, the approach can serve ongoing efforts in forest conservation and has the potential to foster advances in climate, carbon and biodiversity modelling.
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- Authors
- Lang, N., Jetz, W., Schindler, K., & Wegner, J. D.
- Affiliations
- University of Copenhagen (DK)
- Yale University (US)
- University of Zurich (CH)
- Corresponding author
- Lang, Nico
- Wegner, Jan Dirk
- Venue
- Nature Ecology & Evolution
- Published
- 2023-09-28
- Volume
- 7
- Issue
- 11
- Pages
- 1778-1789
- ISSN
- 2397-334X (ISSN-L)
- PMC
- PMC10627820
- OpenAlex
- W4387141420
- Semantic Scholar
- d745e6ffb484bc8394f2d20734b27725d65493ef
- Cited by
- 810
- References
- 53
- Citations by year
- 2026: 330 · 2025: 264 · 2024: 146 · 2023: 56 · 2022: 13
- FWCI
- 138.46
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-02-19)
- Open access
- hybrid
- doi.org
- Locations
- Nature Ecology & Evolution (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Aeronautics and Space Administration
- Villum Fonden (34306)
- National Research Foundation
- Danmarks Grundforskningsfond
- Eidgenössische Technische Hochschule Zürich
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Forest ecology and management
- Keywords
- canopy height, GEDI, spaceborne lidar, Sentinel-2, deep learning fusion, forest carbon stocks, protected areas, uncertainty quantification, terrestrial ecosystems
- MeSH terms
- Carbon, Trees, Ecosystem, Biodiversity, Forests
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