GEDI L4A Raster Aboveground Biomass Density, Version 2.1

LARSE/GEDI/GEDI04_A_002_MONTHLY
Dataset Availability
2019-03-25T00:00:00Z–2023-03-01T08:00:00
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("LARSE/GEDI/GEDI04_A_002_MONTHLY")
Tags
elevation gedi larse nasa tree-cover usgs

Description

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).The algorithm setting group selection used for GEDI02_A Version 2 has been modified for evergreen broadleaf trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The dataset LARSE/GEDI/GEDI04_A_002_MONTHLY is a raster version of the original GEDI04_A product. The raster images are organized as monthly composites of individual orbits in the corresponding month.

See User Guide for more information.

The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track.

Product Description
L2A Vector LARSE/GEDI/GEDI02_A_002
L2A Monthly raster LARSE/GEDI/GEDI02_A_002_MONTHLY
L2A table index LARSE/GEDI/GEDI02_A_002_INDEX
L2B Vector LARSE/GEDI/GEDI02_B_002
L2B Monthly raster LARSE/GEDI/GEDI02_B_002_MONTHLY
L2B table index LARSE/GEDI/GEDI02_B_002_INDEX
L4A Biomass Vector LARSE/GEDI/GEDI04_A_002
L4A Monthly raster LARSE/GEDI/GEDI04_A_002_MONTHLY
L4A table index LARSE/GEDI/GEDI04_A_002_INDEX
L4B Biomass LARSE/GEDI/GEDI04_B_002

Bands

Resolution
25 meters

Bands

Name Units Description
agbd Mg/ha

Predicted aboveground biomass density

agbd_pi_lower Mg/ha

Lower prediction interval (see "alpha" attribute for the level)

agbd_pi_upper Mg/ha

Upper prediction interval (see "alpha" attribute for the level)

agbd_se Mg/ha

Aboveground biomass density prediction standard error

agbd_t

Model prediction in fit units

agbd_t_se

Model prediction standard error in fit units (needed for calculation of custom prediction intervals)

algorithm_run_flag

The L4A algorithm is run if this flag is set to 1. This flag selects data that have sufficient waveform fidelity for AGBD estimation.

beam

Beam identifier

channel

Channel identifier

degrade_flag

Flag indicating degraded state of pointing and/or positioning information

delta_time seconds

Time since Jan 1 00:00 2018

elev_lowestmode m

Elevation of center of lowest mode relative to reference ellipsoid

l2_quality_flag

Flag identifying the most useful L2 data for biomass predictions

l4_quality_flag

Flag simplifying selection of most useful biomass predictions

lat_lowestmode deg

Latitude of center of lowest mode

lon_lowestmode deg

Longitude of center of lowest mode

master_frac seconds

Master time, fractional part. master_int+master_frac is equivalent to /BEAMXXXX/delta_time

master_int seconds

Master time, integer part. Seconds since master_time_epoch. master_int+master_frac is equivalent to /BEAMXXXX/delta_time',

predict_stratum

Prediction stratum identifier. Character ID of the prediction stratum name for the 1 km cell

predictor_limit_flag

Predictor value is outside the bounds of the training data (0=in bounds; 1=lower bound; 2=upper bound)

response_limit_flag

Prediction value is outside the bounds of the training data (0=in bounds; 1=lower bound; 2=upper bound)

selected_algorithm

Selected algorithm setting group

selected_mode

ID of mode selected as lowest non-noise mode

selected_mode_flag

Flag indicating status of selected_mode

sensitivity

Beam sensitivity. Maximum canopy cover that can be penetrated considering the SNR of the waveform

solar_elevation deg

Solar elevation angle

surface_flag

Indicates elev_lowestmode is within 300m of Digital Elevation Model (DEM) or Mean Sea Surface (MSS) elevation

shot_number

Shot number, a unique identifier. This field has the format of OOOOOBBRRGNNNNNNNN, where:

  • OOOOO: Orbit number
  • BB: Beam number
  • RR: Reserved for future use
  • G: Sub-orbit granule number
  • NNNNNNNN: Shot index
shot_number_within_beam

Shot number within beam

agbd_aN Mg/ha

Above ground biomass density; Geolocation latitude lowestmode

agbd_pi_lower_aN Mg/ha

Above ground biomass density lower prediction interval

agbd_pi_upper_aN Mg/ha

Above ground biomass density upper prediction interval

agbd_se_aN Mg/ha

Aboveground biomass density prediction standard error

agbd_t_aN Mg/ha

Aboveground biomass density model prediction in transform space

agbd_t_pi_lower_aN Mg/ha

Lower prediction interval in transform space

agbd_t_pi_upper_aN Mg/ha

Upper prediction interval in transform space

agbd_t_se_aN

Model prediction standard error in fit units

algorithm_run_flag_aN

Algorithm run flag-this algorithm is run if this flag is set to 1. This flag selects data that have sufficient waveform fidelity for AGBD estimation

l2_quality_flag_aN

Flag identifying the most useful L2 data for biomass predictions'

l4_quality_flag_aN

Flag simplifying selection of most useful biomass predictions

predictor_limit_flag_aN

Predictor value is outside the bounds of the training data

response_limit_flag_aN

Prediction value is outside the bounds of the training data

selected_mode_aN

ID of mode selected as lowest non-noise mode

selected_mode_flag_aN

Flag indicating status of selected mode

elev_lowestmode_aN m

Elevation of center of lowest mode relative to the reference ellipsoid

lat_lowestmode_aN deg

Latitude of center of lowest mode

lon_lowestmode_aN deg

Longitude of center of lowest mode

sensitivity_aN

Maximum canopy cover that can be penetrated considering the SNR of the waveform

stale_return_flag

Flag from digitizer indicating the real-time pulse detection algorithm did not detect a return signal above its detection threshold within the entire 10 km search window. The pulse location of the previous shot was used to select the telemetered waveform.

landsat_treecover %

Tree cover in the year 2010, defined as canopy closure for all vegetation taller than 5 m in height (Hansen et al., 2013) and encoded as a percentage per output grid cell.

landsat_water_persistence %

The percent UMD GLAD Landsat observations with classified surface water between 2018 and 2019. Values >80 usually represent permanent water while values <10 represent permanent land.

leaf_off_doy

GEDI 1 km EASE 2.0 grid leaf-off start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product.

leaf_off_flag

GEDI 1 km EASE 2.0 grid flag derived from leaf_off_doy, leaf_on_doy, and pft_class, indicating if the observation was recorded during leaf-off conditions in deciduous needleleaf or broadleaf forests and woodlands. 1=leaf-off, 0=leaf-on.

leaf_on_cycle

Flag that indicates the vegetation growing cycle for leaf-on observations. Values are 0=leaf-off conditions, 1=cycle 1, 2=cycle 2.

leaf_on_doy

GEDI 1 km EASE 2.0 grid leaf-on start day- of-year derived from the NPP VIIRS Global Land Surface Phenology product.

pft_class

GEDI 1 km EASE 2.0 grid Plant Functional Type (PFT) derived from the MODIS MCD12Q1v006 product. Values follow the Land Cover Type 5 Classification scheme.

region_class

GEDI 1 km EASE 2.0 grid world continental regions (0=Water, 1=Europe, 2=North Asia, 3=Australasia, 4=Africa, 5=South Asia, 6=South America, 7=North America).

urban_focal_window_size pixel

The focal window size used to calculate urban_proportion. Values are 3 (3x3 pixel window size) or 5 (5x5 pixel window size).

urban_proportion %

The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover was derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product.

Terms of Use

Terms of Use

This dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.

Explore with Earth Engine

Code Editor (JavaScript)

var qualityMask = function(im) {
  return im.updateMask(im.select('l4_quality_flag').eq(1))
      .updateMask(im.select('degrade_flag').eq(0));
};
var dataset = ee.ImageCollection('LARSE/GEDI/GEDI04_A_002_MONTHLY')
                  .map(qualityMask)
                  .select('solar_elevation');

var gediVis = {
  min: 1,
  max: 60,
  palette: 'red, green, blue',
};
Map.setCenter(5.0198, 51.7564, 12);
Map.addLayer(dataset, gediVis, 'Solar Elevation');
Open in Code Editor