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The goal of chewie is to make downloading GEDI data as simple aspossible. This includes the point-level products: 1B, 2A, 2B and 4A.Here is a quick summary of design choices that enables {chewie} toachieve this:

• Data are downloaded and converted to parquet files which can then beread using{arrow} andconverted tosf objects. Thisapproach is performant as it only requires each entire granule to beloaded into memory once (when it is converted from hdf5 to parquet).From here on we can usedplyr verbs(or base R) tofilter,mutate andselect data as requiredwithout 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 afile has been downloaded it will not be downloaded again even ifworking in a different project (it is also possible to specify aunique cache location for each project).

• There is support for spatial filtering of granules that intersect anarea of interest and not only by a bounding box; this frequentlyreduces 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 isrecommended as it’s an excellent and highly performative option forworking 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 setup correctly (usingchewie_health_check()). By default the cache isset up in the.chewie folder in your home directory. You can changethis by runningchewie_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 PrairieCreek Redwoods State Park, California (the dataset is included with thepackage). We then plot the footprints of the granules that intersectwith this area to check out what we’ve got. Note that by default, bothfind_gedi andgrab_gedi cache their outputs so, when these functionsare re-run, the data will be loaded from the cache rather thandownloaded 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#>                 <char>              <POSc>              <POSc>                                                <char> <lgcl>#> 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 <sfc_POLYGON>]#>#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

Whilst there is aplot method forchewie.find objects, a greatalternative is to plot a leaflet map withchewie_show, which can bestatic or interactive (this uses the fantastic{mapview} under the hood).

chewie_show(gedi_2a_search,zoom=8)

Now we usegrab_gedi to download the data - this function internally,converts the data to parquet format and stores it in the cache. Thereturned value is anarrow_dplyr_query object. We can then use {dplyr}verbs tofilter/select the data as we wish before finally usingcollect_gedi to convert the data to a sf object. If nofiltering/selection is carried out thencollect_gedi will return allthe 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#>  * <int> <dttm>                           <dbl>           <dbl> <dbl>   <dbl>#>  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 <dbl>, rh75 <dbl>, rh95 <dbl>, rh100 <dbl>,#> #   geometry <POINT [°]>

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-subsetterprovides a selection of python tools for querying and downloading GEDIdata. Its scope is similar to {chewie} but it also provides directaccess to the hdf5 files for nasa affiliates with access to the MAAPplatform.

• {rGEDI} provides theability download GEDI data but also a great deal of additionalfunctionality for visualisation, post-processing and modelling.

• {GEDI4R} which similarlyprovides a suit of tools for downloading, visualising and modellingGEDI data, but with a focus on the 4A product.

• pyGEDI is a Python packagefor downloading and visualising GEDI data.

• GEDI-Data-Resources isa collection of scripts for both python and R that provide examples ofhow to download and process GEDI data.

These resources have been a great source of inspiration for {chewie}; wewould like to thank the authors for their great work!