SpaceLiDAR

A Julia toolbox for ICESat, ICESat-2 and GEDI data. Quickly search, download, and load filtered point data with relevant attributes from the .h5 granules of each data product.

Currently supports the following data products:

mission	data product	User Guide (UG)	Algorithm Theoretical Basis Document (ATBD)
ICESat	GLAH06 v34	UG	ATBD
ICESat	GLAH14 v34	UG	ATBD
ICESat-2	ATL03 v6	UG	ATBD
ICESat-2	ATL06 v5	UG	ATBD
ICESat-2	ATL08 v6	UG	ATBD
ICESat-2	ATL12 v5	UG	ATBD
GEDI	L2A v2	UG	ATBD

For an overview with code examples, see the FOSS4G Pluto notebook here

If you use SpaceLiDAR.jl in your research, please consider citing it.

Install

]add SpaceLiDAR

Usage

Search for data

using SpaceLiDAR
using Extents
# Find all ATL08 granules ever
granules = search(:ICESat2, :ATL08)

# Find only ATL03 granules in a part of Vietnam
vietnam = Extent(X=(102.0, 107.0), Y=(8.0, 12.0))
granules = search(:ICESat2, :ATL08; extent=vietnam, version=6)

# Find GEDI granules in the same way
granules = search(:GEDI, :GEDI02_A; extent=vietnam)

# A granule is pretty simple
granule = granules[1]
granule.id  # filename
granule.url  # download url
granule.info  # derived information from id

# Downloading granules requires a setup .netrc with an NASA EarthData account
# we provide a helper function, that creates/updates a ~/.netrc or ~/_netrc
SpaceLiDAR.netrc!(username, password)  # replace with your credentials

# Afterward you can download the dataset.
# Note: download! updated granule url to local path
granule = SpaceLiDAR.download!(granule)

# You can also load a granule from disk
path2file = granule.url
granule = SpaceLiDAR.granule(path2file)

# Or from a folder
(folder, fn) = splitdir(path2file)
local_granules = SpaceLiDAR.granules(folder)

Derive points

using DataFrames
fn = "GEDI02_A_2019242104318_O04046_01_T02343_02_003_02_V002.h5"
granule = SpaceLiDAR.granule(fn)

df = DataFrame(granule)
760156×15 DataFrame
    Row │ longitude  latitude   height   height_error  datetime                 intensity   sensitivity  surface  quality  nmodes  track     strong_beam  classification  sun_angle  height_reference 
        │ Float64    Float64    Float32  Float32       DateTime                 Float32     Float32      Bool     Bool     UInt8   String    Bool         String          Float32    Float32          
────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
      1 │   26.6923  51.823     169.045      0.313182  2019-04-18T10:22:23.996   -857.388      1.38006      true    false       1  BEAM0000        false  ground           49.0315            169.752
      2 │   26.7006  51.823     165.783      0.31319   2019-04-18T10:22:24.078    853.56       0.694586     true    false       1  BEAM0000        false  ground           49.0312            167.354
      3 │   26.7023  51.823     162.871      0.313192  2019-04-18T10:22:24.095    110.071     -0.480232     true    false       1  BEAM0000        false  ground           49.0311            164.785
   ⋮    │     ⋮          ⋮         ⋮          ⋮                   ⋮                 ⋮            ⋮          ⋮        ⋮       ⋮        ⋮           ⋮             ⋮             ⋮             ⋮
 760155 │  110.661   -0.194184  171.157      0.258848  2019-04-18T10:45:33.900   7702.96       0.945006     true     true       2  BEAM1011         true  ground           -1.94442           176.333
 760156 │  110.662   -0.195451  167.176      0.258852  2019-04-18T10:45:33.925   9595.64       0.981322     true     true       2  BEAM1011         true  ground           -1.94564           173.691

Derive linestrings

using DataFrames
fn = "GEDI02_A_2019108093620_O01965_03_T05338_02_003_01_V002.h5"
granule = SpaceLiDAR.granule(fn)
tlines = DataFrame.(SpaceLiDAR.lines(granule; step=10000))

SpaceLiDAR.GDF.write("lines.gpkg", tlines)