gedixr

Extract the variables you need from GEDI L2A/L2B files and start working with them as a geopandas.GeoDataFrame or xarray.Dataset in no time!

Installation

Latest state on GitHub

1. Create and activate an environment with the required dependencies:

conda env create --file https://raw.githubusercontent.com/maawoo/gedixr/main/environment.yml
conda activate gedixr_env

I recommend you to check Mamba/Micromamba as a faster alternative to Conda.

2. Install the gedixr package into the activated environment:

pip install git+https://github.com/maawoo/gedixr.git

Specific version

See the Tags section of the repository for available versions to install:

conda env create --file https://raw.githubusercontent.com/maawoo/gedixr/v0.4.0/environment.yml
conda activate gedixr_env
pip install git+https://github.com/maawoo/gedixr.git@v0.4.0

Usage

After downloading GEDI L2A/L2B v002 files from NASA Earthdata Search¹, you will end up with a bunch of zipped HDF5 files. After unzipping² them, you can use the extract_data function to recursively find all relevant files in a directory and extract biophysical variables (see subsection for defaults) for each shot to further work with them as geopandas.GeoDataFrame in Python or use the created vector file in your favorite GIS software.

Basic example

The NASA Earthdata Search platform mentioned above allows you to already subset the GEDI data to your area of interest during the download process. This saves you space on disk and the extraction process is quite straightforward in this case:

from gedixr.gedi import extract_data

gedi_dir = "directory/containing/gedi/products"
gdf_l2a = extract_data(directory=gedi_dir, gedi_product='L2A')
gdf_l2b = extract_data(directory=gedi_dir, gedi_product='L2B')

The directory you provide will be searched recursively and only files will be considered that match the product provided via the gedi_product parameter.

If you extracted variables from L2A and L2B files of the same spatial and temporal extents, you can then merge both GeoDataFrames:

from gedixr.xr import merge_gdf

gdf = merge_gdf(l2a=gdf_l2a, l2b=gdf_l2b)

If you want to rasterize the GeoDataFrame and use the data as an xarray.Dataset:

from gedixr.xr import gdf_to_xr

ds = gdf_to_xr(gdf=gdf)

If you want to load previously extracted data:

from gedixr.xr import load_to_gdf

gdf = load_to_gdf(l2a="path/to/extracted_l2a.parquet")

Custom subsetting

If your GEDI data is not subsetted (i.e., each file covering an entire orbit), you can provide a vector file (e.g. GeoJSON, GeoPackage, etc.) to extract metrics for your area of interest. You can also provide a list of vector files to extract for multiple areas at the same time:

from gedixr.gedi import extract_data

l2a_dict = extract_data(directory="directory/containing/gedi/products", 
                        gedi_product='L2A', 
                        subset_vector=["path/to/aoi_1.geojson",
                                       "path/to/aoi_2.geojson"])

Please note that if the subset_vector parameter is used, a dictionary with the following key, value pairs is returned:

{'<Vector Basename>': {'geo': Polygon, 'gdf': GeoDataFrame}}

Given the above example, you can access the extracted GeoDataFrame of each area like this:

aoi_1_gdf = l2a_dict['aoi_1']['gdf']
aoi_2_gdf = l2a_dict['aoi_2']['gdf']

Extract from specific beams

The beams parameter can be used to specify which beams to extract data from. By default, data will be extracted from all beams (full power and coverage). You can use beams='full' (or 'coverage') to only extract from one or the other. Alternatively, you can provide a list of beam names, e.g.: beams=['BEAM0101', 'BEAM0110']

Current defaults

Extracted variables

In addition to shot number, acquisition time and geolocation information, the following variables are extracted by default if no custom variables are provided via the variables parameter:

L2A:

• rh98: Relative height metrics at 98% interval

L2B:

• rh100: Height above ground of the received waveform signal start (rh101 from L2A)
• tcc: Total canopy cover
• fhd: Foliage Height Diversity
• pai: Total Plant Area Index

See also the following sources for overviews of the layers contained in each product: L2A and L2B

Quality filtering

The extraction process will automatically apply quality filtering based on the quality_flag, degrade_flag and sensitivity variables using the following default values:

• quality_flag == 1
• degrade_flag == 0
• num_detectedmodes > 0
• abs(ele_lowestmode - digital_elevation_model) < 100

Please note that quality_flag already includes filtering to a sensitivity range of 0.9 - 1.0.

If you want to apply a different quality filtering strategy, you can disable the default filtering by setting apply_quality_filter=False and apply your own filtering after the extraction process.

Notes

¹See #1 for a related issue regarding the download of GEDI data.

²The products need to be unzipped first which can seriously increase the amount of disk space needed (~90 MB compressed -> ~3 GB uncompressed... per file!). A solution is work in progress and being tracked in #2.