A vertical vegetation structure of the Earth
Year: 2026
Type: preprint
Abstract: Knowing the global distribution of vegetation structure is an essential prerequisite to better quantify, model, and manage carbon, water, energy, and biodiversity in the Earth system. Despite its recognized importance, vertical structure measurements remain sparse at global scales, limiting our understanding of the links between structure and ecosystem functions and resilience. Here we present global vertical vegetation structure data for the year 2020 at 10 m spatial resolution. A deep learning approach was developed to combine satellite images from Sentinel-2 with 178 million sparse height profile measurements from the GEDI spaceborne LiDAR mission. While prior work has focused on modeling canopy top height or other scalar descriptors of structure, we provide a detailed global representation of the full vertical vegetation structure. Not only does this improve canopy top height estimates by up to 37% compared to commonly used top height proxies, but more importantly, the vertical structure estimates reveal conservation-relevant differences in canopy organization, lower-canopy structure, and canopy closure that are otherwise compressed or missed by top height and scalar complexity products. For example, we find that our full profile data improve the identification of natural forests by 42% relative to using canopy top height alone, likely being a result of the improved sensitivity to fine-scale structural signatures associated with anthropogenic disturbance, including structural changes caused by forest degradation or selective logging. By revealing subtle signatures associated with forest management and potential degradation, the presented vertical structure model provides a foundation for implementing global conservation initiatives and advancing early warning systems in ecosystem monitoring.
DOI: 10.31223/x5m22f
Language: English
Subfield: Forestry
Domain: Life Sciences
SDGs: Life on land, Climate action
Open Access status: green
OpenAlex ID: W7220670352