Quick Overview#

This section provides brief examples of using gedidb.GEDIProcessor and gedidb.GEDIProvider to process and query GEDI data. For advanced features and detailed use cases, refer to the GediDB Fundamentals.

Start by importing the gedidb package:

import gedidb as gdb
import concurrent.futures

Processing GEDI Data#

To process GEDI data, specify paths to a YAML configuration file (config_file). See Configuration files for more information on the data configuration files.

This setup initiates the download, processing, and storage of GEDI data in your database.

# Paths to configuration files
config_file = 'path/to/config_file.yml'
geometry = 'path/to/test.geojson'

# Initialize a parallel engine
concurrent_engine= concurrent.futures.ThreadPoolExecutor(max_workers=10)

# Initialize the GEDIProcessor and compute
with gdb.GEDIProcessor(
    config_file=config_file,
    geometry=geometry,
    start_date='2020-01-01',
    end_date='2020-12-31',
    earth_data_dir= '/path/to/earthdata_credential/folder',
    parallel_engine=concurrent_engine,
) as processor:
    processor.compute(consolidate=True)

In this example, the gedidb.GEDIProcessor performs:

  • Downloading GEDI L2A-B and L4A-C products.

  • Filtering data by quality.

  • Storing the processed data in the tileDB database.

Querying GEDI Data#

Once the data is processed and stored, use gedidb.GEDIProvider to query it. The results can be returned in either Xarray or Pandas format, providing flexibility for various workflows.

Example query using gedidb.GEDIProvider:

import geopandas as gpd
import gedidb as gdb

# Create GEDIProvider instance
provider = gdb.GEDIProvider(storage_type='local',
                            local_path= "path/to/your/database/")

# Load region of interest
region_of_interest = gpd.read_file('./data/geojson/BR-Sa1.geojson')

# Define the columns to query and additional parameters
vars_selected = ["agbd", 'rh']

# Profile the provider's `get_data` function
gedi_data = provider.get_data(
    variables=vars_selected,
    query_type="bounding_box",
    geometry=region_of_interest,
    start_time="2018-01-01",
    end_time="2024-07-25",
    return_type='xarray'
)

This provider.get_data() function allows you to:

  • Select specific columns (e.g., wsci_z_pi_lower, wsci_z_pi_upper).

  • Apply spatial and temporal filters using geometry, start_time, and end_time.

  • Return data in either xarray or pandas format based on return_type.

This functionality offers a flexible, scalable approach to querying GEDI data, streamlining its integration into your data workflows.

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