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.
]add SpaceLiDARSearch 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., 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)
# 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
fn = SpaceLiDAR.download!(granule)
# You can also load a granule from disk
granule = granule(fn)
# Or from a folder
local_granules = granules(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(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 trueDerive linestrings
using DataFrames
fn = "ATL03_20181110072251_06520101_003_01.h5"
g = SpaceLiDAR.granule(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)