A 1x1 km spatial resolution mean aboveground biomass density (AGBD) map derived from GEDI lidar data

The GEDI mission characterizes ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI lidar instrument is optimized for mapping canopy structure and aboveground biomass across Earth’s tropical and temperate forests between 51.6° N and 51.6° S latitude. Such maps support a broad range of scientific, policy, and management applications.

A statistical framework allows closed-form estimation of mean AGBD and its uncertainty at the scale of 1-km grid cells, and at coarser scales up to user defined grid cell size or areal extent. GEDI currently provides 1-km estimates of mean AGBD (Mg ha-1) and the uncertainty of these estimates are expressed as the standard error of the mean. The 1-km product does not have estimates for all grid cells because GEDI has not yet achieved global coverage at that spatial resolution. These represent our best understanding of the spatial distribution of Earth’s tropical and temperate forest biomass.

For grid cells with valid estimates, the estimated standard error of the mean depends upon the fit of footprint biomass models (model variance) and the density of observations and ground tracks (sample variance). Grid cells without a valid mean biomass estimate have no data (-9999). The distribution of no-data cells is not uniform, with higher non-response found: 1) earlier in the mission life; 2) closer to the equator where the ISS overpass pattern is sparser; 3) in cloudy areas; and 4) in areas where reference ground tracks were not sampled because of an orbital sampling problem experienced in the second year of the missions. This orbital sampling problem involved repeated coverage of some ground tracks at the expense of others because of an unscheduled change in ISS altitude.

A hybrid model-based mode of inference is used in the L4B product. The corresponding 1-km estimates of the standard error of the mean propagate the uncertainty due to both GEDI’s sampling of the 1-km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free. GEDI ground tracks are treated as cluster samples under the hybrid inference paradigm, and at least two clusters are required to create a valid estimate.

The 1 km2 resolution global EASE-Grid 2.0 is used to partition the GEDI footprint observations into grid cells by their footprint center location. This nested grid features equal-area cells and compatibility with many existing biosphere data sets. More information on this grid can be found from the U.S. National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/ease.

Uncertainty of the estimated mean biomass is determined through the estimator described by hybrid estimation. The hybrid variance estimator has two components, the first of which is model variance from the L4A field-to-GEDI AGBD model. The second variance component relates to GEDI’s sample design. These variance components allow the user to decompose uncertainty expressed in the Standard Error variable into its primary components.

Technical Characteristics

Spatial resolution: 1 km

Geographical coverage: 51.6° N and 51.6° S

Temporal coverage: 18-Apr-2019 to 09-Jun-2021

Update frequency: Annual

Format: GeoTIFF

Data Policy: Creative Commons Attribution 4.0 International (CC-BY-4.0)

Associated Guidance or User Manual

ATBD not yet available (October 2021), but it will be available from: https://daac.ornl.gov/

Dataset link not yet available (October 2021), but it will be available from: https://daac.ornl.gov/

Points of contact for queries

John Armston

Associate Research Professor

University of Maryland, College Park, USA

Email: armston@umd.edu

Sean Healey

Research Ecologist

U.S. Forest Service

Email: seanhealey@fs.fed.us

Ralph Dubayah

Professor

University of Maryland, College Park, USA

Email: dubayah@umd.edu