GEDI launches a new era of biomass inference from space

• Published in: Environmental research letters Vol. 17; no. 9; pp. 95001 - 95017
• Main Authors: Dubayah, Ralph, Armston, John, Healey, Sean P, Bruening, Jamis M, Patterson, Paul L, Kellner, James R, Duncanson, Laura, Saarela, Svetlana, Ståhl, Göran, Yang, Zhiqiang, Tang, Hao, Blair, J Bryan, Fatoyinbo, Lola, Goetz, Scott, Hancock, Steven, Hansen, Matthew, Hofton, Michelle, Hurtt, George, Luthcke, Scott
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center IOP Publishing 01.09.2022
• Subjects: Biomass; carbon; Carbon dioxide; Deforestation; Earth Resources and Remote Sensing; Ecosystem dynamics; Estimates; Fjärranalysteknik; Forest biomass; Forest Science; forest structure; GEDI; hybrid inference; International Space Station; Land use; Lidar; Regrowth; Remote Sensing; Sampling designs; Skogsvetenskap; Space missions; Standard error; Statistical inference; Vegetation
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ac8694

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.