The goal of chewie is to make downloading GEDI data as simple as possible. This includes the point-level products: 1B, 2A, 2B and 4A. Here is a quick summary of design choices that enables {chewie} to achieve this:
Data are downloaded and converted to parquet files which can then be
read using {arrow} and
converted to sf objects. This
approach is performant as it only requires each entire granule to be
loaded into memory once (when it is converted from hdf5 to parquet).
From here on we can use dplyr verbs
(or base R) to filter, mutate and select data as required
without needing to load all shots, from a given granule, into memory.
A system-level cache is used to store the data. This means that once a file has been downloaded it will not be downloaded again even if working in a different project (it is also possible to specify a unique cache location for each project).
There is support for spatial filtering of granules that intersect an area of interest and not only by a bounding box; this frequently reduces the amount of irrelevant data that is downloaded.
You can install the development version of chewie like so:
# install.packages("pak")pak::pkg_install("Permian-Global-Research/chewie")First, let’s load in some libraries. {dplyr} isn’t essential but it is recommended as it’s an excellent and highly performative option for working with arrow datasets.
library(chewie) library(dplyr) library(sf)
Here are some useful helper functions to set up your credentials (usingchewie_creds()) and check that those credentials and the cache are set
up correctly (using chewie_health_check()). By default the cache is
set up in the .chewie folder in your home directory. You can change
this by running chewie_cache_set().
chewie_creds() # to set up your credentialschewie_health_check() # to check your credentials and cache setup.
Now, let’s search for GEDI 2A data that intersects with the Prairie
Creek Redwoods State Park, California (the dataset is included with the
package). We then plot the footprints of the granules that intersect
with this area to check out what we’ve got. Note that by default, bothfind_gedi and grab_gedi cache their outputs so, when these functions
are re-run, the data will be loaded from the cache rather than
downloaded again, even in a different R session.
prairie_creek <- sf::read_sf(system.file( "geojson", "prairie-creek.geojson", package = "chewie"))gedi_2a_search <- find_gedi(prairie_creek, gedi_product = "2A", date_start = "2023-01-01", date_end = "2023-01-31")#> ✔ Using cached GEDI find resultprint(gedi_2a_search)#> #> ── chewie.find ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────#> • GEDI-2A#> id time_start time_end url cached#>#> 1: G2754665065-LPCLOUD 2023-01-25 05:14:31 2023-01-25 06:47:21 https://data.lpdaac.earthdatacloud.nasa.gov/lp-pro... TRUE#> 1 variable(s) not shown: [geometry ]#> #> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Whilst there is a plot method for chewie.find objects, a great
alternative is to plot a leaflet map with chewie_show, which can be
static or interactive (this uses the fantastic
{mapview} under the hood).
chewie_show( gedi_2a_search, zoom = 8)
Now we use grab_gedi to download the data - this function internally,
converts the data to parquet format and stores it in the cache. The
returned value is an arrow_dplyr_query object. We can then use {dplyr}
verbs to filter/select the data as we wish before finally usingcollect_gedi to convert the data to a sf object. If no
filtering/selection is carried out then collect_gedi will return all
the available columns/rows for the AOI.
gedi_2a_sf <- grab_gedi(gedi_2a_search) |> filter( quality_flag == 1, degrade_flag == 0 ) |> select( beam, date_time, lat_lowestmode, lon_lowestmode, elev_highestreturn, elev_lowestmode, rh0, rh25, rh50, rh75, rh95, rh100 ) |> collect_gedi(gedi_find = gedi_2a_search)#> ✔ All data found in cacheprint(gedi_2a_sf)#> Simple feature collection with 884 features and 10 fields#> Geometry type: POINT#> Dimension: XY#> Bounding box: xmin: -124.069 ymin: 41.3609 xmax: -123.9959 ymax: 41.43904#> Geodetic CRS: WGS 84#> # A tibble: 884 × 11#> beam date_time elev_highestreturn elev_lowestmode rh0 rh25#> *#> 1 0 2023-01-25 06:09:05 -19.6 -23.8 -3.55 -1.12 #> 2 0 2023-01-25 06:09:05 -20.7 -24.2 -3.89 -1.27 #> 3 0 2023-01-25 06:09:05 -20.7 -24.2 -3.93 -1.27 #> 4 0 2023-01-25 06:09:05 -2.29 -23.3 -3.37 -0.0300#> 5 0 2023-01-25 06:09:05 27.7 -15.0 -2.54 9.70 #> 6 0 2023-01-25 06:09:05 35.8 4.55 -3.74 12.1 #> 7 0 2023-01-25 06:09:05 55.9 12.2 -1.57 16.8 #> 8 0 2023-01-25 06:09:05 94.6 41.0 -1.53 25.8 #> 9 0 2023-01-25 06:09:05 95.3 42.5 -3.78 6.06 #> 10 0 2023-01-25 06:09:05 98.3 33.8 -2.32 29.9 #> # ℹ 874 more rows#> # ℹ 5 more variables: rh50 , rh75 , rh95 , rh100 ,#> # geometry
Finally, we can plot the data. Again we can use the genericchewie_show function.
chewie_show( gedi_2a_sf, zcol = "rh95", zoom = 13, alpha = 0.5, aoi_color = "white")
gedi-subsetter provides a selection of python tools for querying and downloading GEDI data. Its scope is similar to {chewie} but it also provides direct access to the hdf5 files for nasa affiliates with access to the MAAP platform.
{rGEDI} provides the ability download GEDI data but also a great deal of additional functionality for visualisation, post-processing and modelling.
{GEDI4R} which similarly provides a suit of tools for downloading, visualising and modelling GEDI data, but with a focus on the 4A product.
pyGEDI is a Python package for downloading and visualising GEDI data.
GEDI-Data-Resources is a collection of scripts for both python and R that provide examples of how to download and process GEDI data.
These resources have been a great source of inspiration for {chewie}; we would like to thank the authors for their great work!
