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:

For a 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
# Find all ATL08 granules
granules = find(:ICESat2, "ATL08")

# Find only ATL03 granules in a part of Vietnam
vietnam = (min_x = 102., min_y = 8.0, max_x = 107.0, max_y = 12.0)
granules = find(:ICESat2, "ATL08", vietnam, "004")

# Find GEDI granules in the same way
granules = find(:GEDI, "GEDI02_A")

# 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 (requires curl to be available on PATH)
fn = SpaceLiDAR.download!(granule)

# You can also load a granule from disk
granule = granule_from_file(fn)

# Or from a folder
local_granules = granules_from_folder(<folder>)

# Instantiate search results locally (useful for GEDI location indexing)
local_granules = instantiate(granules, <folder>)

Derive points

using DataFrames
fn = "GEDI02_A_2019242104318_O04046_01_T02343_02_003_02_V002.h5"
g = SpaceLiDAR.granule_from_file(fn)
df = DataFrame(g)
149680×15 DataFrame
    Row │ longitude  latitude  height    height_error  datetime                 intensity  sensitivity  surface  quality  nmo ⋯
        │ Float64    Float64   Float32   Float32       DateTime                 Float32    Float32      Bool     Bool     UIn ⋯
────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
      1 │   153.855  -47.2772  -13.3536      0.307976  2019-08-30T10:48:21.047   393.969   -0.0671094      true    false      ⋯
      2 │   153.855  -47.2769  -11.2522      0.307978  2019-08-30T10:48:21.055   797.26     0.533529       true     true
      3 │   153.856  -47.2767  -13.775       0.307981  2019-08-30T10:48:21.063  1010.39     0.695938       true     true
      4 │   153.857  -47.2765  -11.729       0.307983  2019-08-30T10:48:21.071   852.614    0.544849       true     true
      5 │   153.857  -47.2763  -13.2443      0.307985  2019-08-30T10:48:21.080   980.66     0.620767       true     true      ⋯
      6 │   153.858  -47.2761  -12.1813      0.307987  2019-08-30T10:48:21.088   937.441    0.620531       true     true
      7 │   153.859  -47.2758  -11.9011      0.30799   2019-08-30T10:48:21.096  1235.02     0.73815        true     true
      8 │   153.859  -47.2756  -12.3796      0.307992  2019-08-30T10:48:21.104   854.127    0.545655       true     true

Derive linestrings

using DataFrames
fn = "ATL03_20181110072251_06520101_003_01.h5"
g = SpaceLiDAR.granule_from_file(fn)
tlines = DataFrame(SpaceLiDAR.lines(g, step=10000))
Table with 4 columns and 6 rows:
     geom                       sun_angle  track        datetime
   ┌───────────────────────────────────────────────────────────────────────────
 1 │ wkbLineString25D geometry  38.3864    gt1l_weak    2018-11-10T07:28:01.688
 2 │ wkbLineString25D geometry  38.375     gt1r_strong  2018-11-10T07:28:02.266
 3 │ wkbLineString25D geometry  38.2487    gt2l_weak    2018-11-10T07:28:04.474
 4 │ wkbLineString25D geometry  38.1424    gt2r_strong  2018-11-10T07:28:07.374
 5 │ wkbLineString25D geometry  38.2016    gt3l_weak    2018-11-10T07:28:05.051
 6 │ wkbLineString25D geometry  38.1611    gt3r_strong  2018-11-10T07:28:06.344
SpaceLiDAR.GDF.write("lines.gpkg", tlines)