L2A — Ground Elevation & Canopy Height¶

Overview¶

GEDI L2A provides ground elevation, canopy height metrics, and relative height (RH) metrics for each GEDI footprint. Data is organized by 8 beams.

Quick Start¶

using SpaceLiDAR, DataFrames

g = granule("GEDI02_A_2019242104318_O04046_01_T02343_02_003_02_V002.h5")
t = table(g)
df = DataFrame(t)

Default Columns¶

Column	HDF5 Path	Type
`longitude`	`lon_lowestmode`	Float64
`latitude`	`lat_lowestmode`	Float64
`height`	`elev_lowestmode`	Float32
`height_error`	`elevation_bin0_error`	Float32
`datetime`	`delta_time`	DateTime
`intensity`	`energy_total`	Float32
`sensitivity`	`sensitivity`	Float32
`surface`	`surface_flag`	Bool
`quality`	`quality_flag`	Bool
`nmodes`	`num_detectedmodes`	UInt8
`sun_angle`	`solar_elevation`	Float32
`height_reference`	`digital_elevation_model`	Float32
`strong_beam`	attribute	—

Default Tracks¶

SpaceLiDAR.default_tracks(g)

("BEAM0000", "BEAM0001", "BEAM0010", "BEAM0011", "BEAM0101", "BEAM0110", "BEAM1000", "BEAM1011")

Canopy Heights¶

Use gedi_l2a_canopy_variables() to read highest return instead of lowest mode:

t = table(g; variables=gedi_l2a_canopy_variables())

This reads elev_highestreturn / lon_highestreturn / lat_highestreturn.

Quality Filtering¶

df = DataFrame(t)

# Basic quality (matches quality_flag):
filter!(:quality => identity, df)

# L3-style filtering:
filter!(row -> row.quality && row.surface, df)
# For sensitivity filtering (optional):
filter!(row -> 0.9 < row.sensitivity <= 1.0, df)

For the full L3 filter (including algorithm-based zcross/toploc checks), you would need to read additional variables:

vars = [SpaceLiDAR.default_variables(g)...,
    Variable(:selected_algorithm, "selected_algorithm", UInt8),
    Variable(:rx_assess_quality_flag, "rx_assess/quality_flag", UInt8),
    Variable(:degrade_flag, "degrade_flag", UInt8),
    Variable(:stale_return_flag, "geolocation/stale_return_flag", UInt8),
    Variable(:rx_maxamp, "rx_assess/rx_maxamp", Float32),
    Variable(:sd_corrected, "rx_assess/sd_corrected", Float32),
]
t = table(g; variables=vars)
df = DataFrame(t)

# Apply L3 criteria:
filter!(df) do row
    row.rx_assess_quality_flag != 0 &&
    row.surface &&
    row.stale_return_flag == 0 &&
    row.degrade_flag == 0 &&
    row.rx_maxamp / row.sd_corrected >= 8
end