GEDI Launches a New Era of Biomass Inference from Space

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 2 of this Preprint.

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Comment #70 Ralph Dubayah @ 2022-08-05 07:38

This article is now published at Environmental Research Letters. The published article contains differences based on reviewer feedback. Please reference the ERL version at:


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Ralph Dubayah , John Armston, Sean Healey, Jamis Bruening, Paul Patterson, James Kellner, Laura Duncanson, Svetlana Saarela, Göran Ståhl, Zhiqiang Yang, hao tang, J. Bryan Blair, Lola Fatoyinbo, Scott Goetz, Steven Hancock, Matt Hansen, Michelle Hofton, George Hurtt, Scott Luthcke


Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.



Applied Statistics, Earth Sciences, Other Earth Sciences, Other Forestry and Forest Sciences, Terrestrial and Aquatic Ecology


GEDI, LiDAR, biomass, carbon, forest structure, hybrid inference


Published: 2022-04-20 19:50

Last Updated: 2022-04-21 23:56

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