Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density. Issue 4 (20th April 2023)

• Record Type: Journal Article
• Authors: Kellner, James R.; Armston, John; Duncanson, Laura
• Is Part Of: Earth and space science. Volume 10:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-20
• Subjects: carbon cycle -- ecosystem structure -- Global Ecosystem Dynamics Investigation -- lidar -- remote sensing; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002516
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8, 587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product. Plain Language Summary: The amount of carbon stored in aboveground vegetation is uncertain. This uncertainty limits our ability to calculate fluxes of carbon between the land surface and the atmosphere, and prevents rigorous carbon offset crediting in forests. Much of this uncertainty is attributed to inconsistent measurement techniques and the use of Earth‐observation methods that were not designed to quantify carbon density. The Global Ecosystem Dynamics Investigation (GEDI) can largely overcome these challenges by producing measurements of vegetation height using a lidar sensor on the International Space Station. This document describes methods developed by the GEDI Science Team to convert spaceborne measurements of vegetation height into estimates of aboveground biomass density. The algorithms depend on the geographic world region and the type of vegetation that is present at a sampled location. For example, evergreen broadleaf forests of the humid tropics in South America and deciduous broadleaf forests of Europe use different algorithms. Statistical models were developed using comprehensive field measurements and simulated GEDI data. This document describes the importance of filtering GEDI data to reduce the impact of measurement artifacts on aboveground biomass predictions. Quality flags and ancillary data contained in the GEDI04_A data product ensure that the best predictions can be used. Key Points: Global Ecosystem Dynamics Investigation (GEDI) aboveground biomass density is from models trained on a comprehensive database of field measurements and simulated GEDI waveforms On‐orbit prediction requires stratification by plant functional type and world region Quality flags and metrics distinguish GEDI measurements that are representative of the conditions under which models were developed