Data¶

ICESat¶

# SpaceLiDAR.points — Method.

points(g::ICESat_Granule{:GLAH06})

Retrieve the points for a given ICESat GLAH06 (Land Ice) granule as a list of namedtuples The names of the tuples are based on the following fields:

Variable	Original Field	Description	Units
`longitude`	`Data_40HZ/Geolocation/d_lon`	Longitude of segment center, WGS84, East=+	decimal degrees
`latitude`	`Data_40HZ/Geolocation/d_lat`	Latitude of segment center, WGS84, North=+	decimal degrees
`height`	`Data_40HZ/Elevation_Surfaces/d_elev`	+ `Data_40HZ/Elevation_Corrections/d_satElevCorr`	m above WGS84 ellipsoid
`datetime`	`Data_40HZ/DS_UTCTime_40`	Precise time of aquisiton	date-time
`quality` ¹	`Data_40HZ/Quality/elev_use_flg`	& `Data_40HZ/Quality/sigma_att_flg` = 0
	& `Data_40HZ/Waveform/i_numPk` = 1	& `Data_40HZ/Elevation_Corrections/d_satElevCorr` < 3	1 = high quality
`height_reference`	`land_ice_segments/dem/dem_h`	Height of the (best available) DEM	height above WGS84

You can get the output in a DataFrame with DataFrame(points(g)).

source

# SpaceLiDAR.points — Method.

points(g::ICESat_Granule{:GLAH14})

Retrieve the points for a given ICESat GLAH14 (Land Surface) granule as a list of namedtuples The names of the tuples are based on the following fields:

Variable	Original Field (in `Data_40HZ`)	Description	Units
`longitude`	`/Geolocation/d_lon`	Longitude of segment center, WGS84, East=+	decimal degrees
`latitude`	`Geolocation/d_lat`	Latitude of segment center, WGS84, North=+	decimal degrees
`height`	`Elevation_Surfaces/d_elev`	+ `Elevation_Corrections/d_satElevCorr`	m above WGS84 ellipsoid
`datetime`	`DS_UTCTime_40`	Precise time of aquisiton	date-time
`quality` ¹	`Quality/elev_use_flg`	& `Quality/sigma_att_flg` = 0
	& `Waveform/i_numPk` = 1	& `Elevation_Corrections/d_satElevCorr` < 3	1=high quality
`clouds`	`Elevation_Flags/elv_cloud_flg`	Cloud contamination	-
`height_reference`	`Geophysical/d_DEM_elv`	Height of the (best available) DEM	height above WGS84
`gain`	`Waveform/i_gval_rcv`	Gain value used for received pulse.	-
`reflectivity`	`Reflectivity/d_reflctUC`	Reflectivity, not corrected	-
`attitude`	`Quality/sigma_att_flg`	Attitude quality indicator	0=good; 50=warning; 100=bad;
`saturation`	`Quality/sat_corr_flg`	Saturation Correction Flag	0=not_saturated;

You can get the output in a DataFrame with DataFrame(points(g)).

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ICESat-2¶

# SpaceLiDAR.classify — Function.

classify(granule::ICESat2_Granule{:ATL03}, atl08::Union{ICESat2_Granule{:ATL08},Nothing} = nothing, tracks = icesat2_tracks)

Like points(::ICESat2_Granule{:ATL03}) but with the classification from the ATL08 dataset. If an ATL08 granule is not provided, we try to find it based on the ATL03 name using convert.

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# SpaceLiDAR.points — Method.

points(g::ICESat2_Granule{:ATL03}, tracks=icesat2_tracks, step=1)

Retrieve the points for a given ICESat-2 ATL03 (Global Geolocated Photon Data) granule as a list of namedtuples, one for each beam. The names of the tuples are based on the following fields:

Column	Field	Description	Units
`longitude`	`heights/lon_ph`	Longitude of photon, WGS84, East=+	decimal degrees
`latitude`	`heights/lat_ph`	Latitude of photon, WGS84, North=+	decimal degrees
`height`	`heights/h_ph`	Photon WGS84 Height	m above the WGS 84 ellipsoid
`quality`	`heights/quality_ph`	Indicates the quality of the associated photon	0 = nominal
`uncertainty`	`geolocation/sigma_h`	Estimated height uncertainty	m
`datetime`	`heights/delta_time`	+ `ancillary_data/atlas_sdp_gps_epoch` + `gps_offset`	date-time
`confidence`	`heights/signal_conf_ph`	Photon Signal Confidence	2=low; 3=med; 4=high
`segment`	`geolocation/segment_id`	Along-track segment ID number	-
`track`	`gt1l` - `gt3r` groups	-	-
`strong_beam`	`-`	"strong" (true) or "weak" (false) laser power	-
`sun_angle`	`geolocation/solar_elevation`	Sun angle	° above horizon
`detector_id`	`atlas_spot_number attribute`	-	-
`height_reference`	`heights/dem/dem_h`	Height of the (best available) DEM	m above the WGS 84 ellipsoid

You can combine the output in a DataFrame with reduce(vcat, DataFrame.(points(g))) if you want to change the default arguments or DataFrame(g) with the default options.

source

# SpaceLiDAR.points — Method.

points(g::ICESat2_Granule{:ATL06}, tracks=icesat2_tracks, step=1)

Retrieve the points for a given ICESat-2 ATL06 (Land Ice) granule as a list of namedtuples, one for each beam. The names of the tuples are based on the following fields:

Column	Field	Description	Units
`longitude`	`land_ice_segments/longitude`	Longitude of segment center, WGS84, East=+	decimal degrees
`latitude`	`land_ice_segments/latitude`	Latitude of segment center, WGS84, North=+	decimal degrees
`height`	`land_ice_segments/h_li`	Standard land-ice segment height	m above the WGS 84 ellipsoid
`height_error`	√(`land_ice_segments/sigma_geo_h`² +	Total vertical geolocation error	m above the WGS 84 ellipsoid
	`land_ice_segments/h_li_sigma`²)
`datetime`	`land_ice_segments/delta_time`	+ `ancillary_data/atlas_sdp_gps_epoch` + `gps_offset`	date-time
`quality`	`land_ice_segments/atl06_quality_summary`	Boolean flag indicating the best-quality subset	1 = high quality
`track`	`gt1l` - `gt3r` groups	-	-
`strong_beam`	`-`	"strong" (true) or "weak" (false) laser power	-
`detector_id`	`atlas_spot_number attribute`	-	-
`height_reference`	`land_ice_segments/dem/dem_h`	Height of the (best available) DEM	-

You can combine the output in a DataFrame with reduce(vcat, DataFrame.(points(g))) if you want to change the default arguments or DataFrame(g) with the default options.

source

# SpaceLiDAR.points — Method.

points(g::ICESat2_Granule{:ATL08}; tracks=icesat2_tracks, step=1, canopy=false, ground=true))

Retrieve the points for a given ICESat-2 ATL08 (Land and Vegetation Height) granule as a list of namedtuples, one for each beam. The names of the tuples are based on the following fields:

Column	Field	Description	Units
`longitude`	`land_segments/longitude`	Longitude of segment center, WGS84, East=+	decimal degrees
`latitude`	`land_segments/latitude`	Latitude of segment center, WGS84, North=+	decimal degrees
`height`	`land_segments/terrain/h_te_mean`	Standard land-ice segment height	m above the WGS 84 ellipsoid
`height_error`	`land_segments/terrain/h_te_uncertainty`	Total vertical geolocation error	m
`datetime`	`land_segments/delta_time`	+ `ancillary_data/atlas_sdp_gps_epoch` + `gps_offset`	date-time
`quality`	`land_segments/terrain_flg`	Boolean flag indicating the best-quality subset	1 = high quality
`phr`	`land_segments/ph_removal_flag`	More than 50% of photons removed	-
`sensitivity`	`land_segments/snr`	The signal to noise ratio	-
`scattered`	`land_segments/msw_flag`	Multiple Scattering warning flag	-1=unknown; 0=none
`saturated`	`land_segments/sat_flag`	Saturation detected	-
`clouds`	`land_segments/layer_flag`	Clouds or blowing snow are likely present	-
`track`	`gt1l` - `gt3r` groups	-	-
`strong_beam`	`-`	"strong" (true) or "weak" (false) laser power	-
`classification`	`-`	"ground", "high_canopy"	-
`height_reference`	`land_segments/dem_h`	Height of the (best available) DEM	m above the WGS 84 ellipsoid
`detector_id`	`atlas_spot_number attribute`	-	-

You can combine the output in a DataFrame with reduce(vcat, DataFrame.(points(g))) if you want to change the default arguments or DataFrame(g) with the default options.

source

# SpaceLiDAR.points — Function.

points(g::ICESat2_Granule{:ATL12}, tracks=icesat2_tracks)

Retrieve the points for a given ICESat-2 ATL12 (Ocean Surface Height) granule as a list of namedtuples, one for each beam. The names of the tuples are based on the following fields:

Column	Field	Description	Units
`longitude`	`ssh_segments/longitude`	Longitude of segment center, WGS84, East=+	decimal degrees
`latitude`	`ssh_segments/latitude`	Latitude of segment center, WGS84, North=+	decimal degrees
`height`	`ssh_segments/heights/h`	Standard land-ice segment height	m above the WGS 84 ellipsoid
`datetime`	`ssh_segments/delta_time`	+ `ancillary_data/atlas_sdp_gps_epoch` + `gps_offset`	date-time
`track`	`gt1l` - `gt3r` groups	-	-
`strong_beam`	`-`	"strong" (true) or "weak" (false) laser power	-
`detector_id`	`atlas_spot_number attribute`	-	-

You can combine the output in a DataFrame with reduce(vcat, DataFrame.(points(g))) if you want to change the default arguments or DataFrame(g) with the default options.

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GEDI¶

# SpaceLiDAR.points — Method.

points(g::GEDI_Granule{:GEDI02_A}; tracks=gedi_tracks, step=1, canopy=false, ground=true, filtered=true)

Retrieve the points for a given GEDI Level 2A (Geolocated Elevation and Height Metrics) granule as a list of namedtuples, one for each beam. The names of the tuples are based on the following fields:

Column	Field	Description	Units
`longitude`	`lon_lowestmode`	Longitude of center, WGS84, East=+	decimal degrees
`latitude`	`lat_lowestmode`	Latitude of center, WGS84, North=+	decimal degrees
`height`	`elev_lowestmode`	Standard land-ice segment height	m above the WGS 84 ellipsoid
`height_error`	`elevation_bin0_error`	Error in elevation of bin 0	m
`datetime`	`delta_time`	+ `ancillary_data/atlas_sdp_gps_epoch` + `gps_offset`	date-time
`quality`	`quality_flag`	Flag simpilfying selection of most useful data	1 = high quality
`surface`	`surface_flag`	Indicates elev_lowestmode is within 300m of DEM or MSS	1 = high quality
`nmodes`	`num_detectedmodes`	Number of detected modes in rxwaveform	-
`intensity`	`energy_total`	Integrated counts in the return waveform	-
`sensitivity`	`sensitivity`	Maxmimum canopy cover that can be penetrated	-
`track`	`BEAM0000` - `BEAM1011` groups	-	-
`strong_beam`	`-`	"strong" (true) or "weak" (false) laser power	-
`classification`	`-`	"ground", "high_canopy"	-
`sun_angle`	`solar_elevation`	Sun angle	° above horizon
`height_reference`	`digital_elevation_model`	TanDEM-X elevation at GEDI footprint location	m above the WGS 84 ellipsoid

You can combine the output in a DataFrame with reduce(vcat, DataFrame.(points(g))) if you want to change the default arguments or DataFrame(g) with the default options.

Data is filtered by default based on², except for the sensitivity field, which can be manually filtered to be above 0.9 and below or equal to 1.0 to match².

source

Smith, B., Fricker, H. A., Gardner, A. S., Medley, B., Nilsson, J., Paolo, F. S., ... & Zwally, H. J. (2020). Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes. Science, 368(6496), 1239-1242. ↩↩
Dubayah, R. O., S. B. Luthcke, T. J. Sabaka, J. B. Nicholas, S. Preaux, and M. A. Hofton. 2021. “GEDI L3 Gridded Land Surface Metrics, Version 2.” ORNL DAAC, November. https://doi.org/10.3334/ORNLDAAC/1952. ↩↩