85 gedi

How to search and download GEDI L4A dataset interactively

This notebook shows how to search and download the GEDI L4A dataset using leafmap. The source code is adapted from the gedi_tutorials repository but has been greatly simplified. Credit goes to ORNL DAAC and Rupesh Shrestha.

Uncomment the following line to install leafmap if needed.

In [1]:

# %pip install -U "leafmap[vector]"

# %pip install -U "leafmap[vector]"

In [2]:

from leafmap import leafmap

from leafmap import leafmap

Create an interactive map.

In [3]:

m = leafmap.Map(height=600)
m

m = leafmap.Map(height=600) m

Use the draw tool to draw a rectangle on the map. If no rectangle is drawn, the default bounding box will be used.

In [4]:

if m.user_roi is not None:
    roi = m.user_roi_bounds()
else:
    roi = [-73.9872, -33.7683, -34.7299, 5.2444]

if m.user_roi is not None: roi = m.user_roi_bounds() else: roi = [-73.9872, -33.7683, -34.7299, 5.2444]

Specify the start and end date.

In [5]:

start_date = "2020-07-01"
end_date = "2020-07-31"

start_date = "2020-07-01" end_date = "2020-07-31"

Search and download GEDI L4A dataset.

In [6]:

gdf = leafmap.gedi_search(roi, start_date, end_date, add_roi=False, sort_filesize=True)
gdf.head()

gdf = leafmap.gedi_search(roi, start_date, end_date, add_roi=False, sort_filesize=True) gdf.head()

Out[6]:

	id	title	time_start	time_end	granule_size	granule_url	granule_poly
53	G2613724953-ORNL_CLOUD	GEDI_L4A_AGB_Density_V2_1.GEDI04_A_20201881134...	2020-07-06T12:43:14.000Z	2020-07-06T12:46:58.000Z	47.861555	https://data.ornldaac.earthdata.nasa.gov/prote...	MULTIPOLYGON (((-44.97207 2.55617, -42.81717 -...
158	G2613715991-ORNL_CLOUD	GEDI_L4A_AGB_Density_V2_1.GEDI04_A_20202000651...	2020-07-18T08:01:01.000Z	2020-07-18T08:04:33.000Z	54.162131	https://data.ornldaac.earthdata.nasa.gov/prote...	MULTIPOLYGON (((-42.53334 -0.01093, -40.37672 ...
154	G2613746001-ORNL_CLOUD	GEDI_L4A_AGB_Density_V2_1.GEDI04_A_20201992001...	2020-07-17T20:19:34.000Z	2020-07-17T20:24:35.000Z	55.751903	https://data.ornldaac.earthdata.nasa.gov/prote...	MULTIPOLYGON (((-56.59662 -15.01064, -54.30646...
86	G2613716275-ORNL_CLOUD	GEDI_L4A_AGB_Density_V2_1.GEDI04_A_20201921001...	2020-07-10T11:10:20.000Z	2020-07-10T11:14:01.000Z	57.355977	https://data.ornldaac.earthdata.nasa.gov/prote...	MULTIPOLYGON (((-43.26376 0.03918, -41.10744 -...
192	G2613743057-ORNL_CLOUD	GEDI_L4A_AGB_Density_V2_1.GEDI04_A_20202040516...	2020-07-22T06:26:07.000Z	2020-07-22T06:29:35.000Z	60.549672	https://data.ornldaac.earthdata.nasa.gov/prote...	MULTIPOLYGON (((-42.25131 0.03698, -40.09134 -...

Visualize the GEDI L4A dataset footprints.

In [7]:

m.add_gdf(gdf, layer_name="GEDI footprints")
m

m.add_gdf(gdf, layer_name="GEDI footprints") m

Download the GEDI L4A dataset from NASA EarthData website. You need to register an account first if you don't have one. Create an account at https://urs.earthdata.nasa.gov. Then return to this notebook and uncomment the following code cell to set your username and password.

In [8]:

# import os
# os.environ["EARTHDATA_USERNAME"] = "your_username"
# os.environ["EARTHDATA_PASSWORD"] = "your_password"

# import os # os.environ["EARTHDATA_USERNAME"] = "your_username" # os.environ["EARTHDATA_PASSWORD"] = "your_password"

In [9]:

leafmap.gedi_download_files(gdf.head(), outdir="data")

leafmap.gedi_download_files(gdf.head(), outdir="data")

Downloading file 1 of 5...

Downloading file 2 of 5...

Downloading file 3 of 5...

Downloading file 4 of 5...

Downloading file 5 of 5...

Read the downloaded GEDI L4A dataset as a GeoDataFrame.

In [10]:

try:
    gdf = leafmap.h5_to_gdf(
        "data/*.h5", dataset="BEAM0110", columns=["agbd"], nodata=-9999
    )
except:
    # Download the sample data if the above code fails
    gdf = leafmap.geojson_to_gdf(
        "https://github.com/opengeos/data/releases/download/v1.0.0/gedi_sample.geojson"
    )
gdf.head()

try: gdf = leafmap.h5_to_gdf( "data/*.h5", dataset="BEAM0110", columns=["agbd"], nodata=-9999 ) except: # Download the sample data if the above code fails gdf = leafmap.geojson_to_gdf( "https://github.com/opengeos/data/releases/download/v1.0.0/gedi_sample.geojson" ) gdf.head()

Out[10]:

	agbd	lat_lowestmode	lon_lowestmode	category	color	geometry
0	0.294734	-6.611691	-38.467288	1	#f7fcf5	POINT (-38.46729 -6.61169)
1	0.698346	-6.614202	-38.465479	1	#f7fcf5	POINT (-38.46548 -6.6142)
2	0.377637	-6.704764	-38.400342	1	#f7fcf5	POINT (-38.40034 -6.70476)
3	0.655570	-6.705182	-38.400042	1	#f7fcf5	POINT (-38.40004 -6.70518)
4	6.617977	-6.708118	-38.397930	3	#74c476	POINT (-38.39793 -6.70812)

We can subset the GeoDataFrame by a bounding box. First, create an interactive map and add the bounding box to the map.

In [11]:

m = leafmap.Map(height=620)
roi = [-38.8641, -6.8664, -37.2107, -6.359]
bbox = leafmap.bbox_to_gdf(roi)
m.add_gdf(bbox, layer_name="ROI", zoom_to_layer=True, info_mode=None)
m

m = leafmap.Map(height=620) roi = [-38.8641, -6.8664, -37.2107, -6.359] bbox = leafmap.bbox_to_gdf(roi) m.add_gdf(bbox, layer_name="ROI", zoom_to_layer=True, info_mode=None) m

Use the bounding box to subset the GeoDataFrame.

In [12]:

subset = leafmap.filter_bounds(gdf, roi, within=True)

subset = leafmap.filter_bounds(gdf, roi, within=True)

Add the subsetted GeoDataFrame to the map. Note that this is only for visualizing a small subset of the data. If you want to work with the entire dataset, you can skip this step.

In [13]:

m.add_data(subset, column="agbd", cmap="Greens", marker_radius=5)

m.add_data(subset, column="agbd", cmap="Greens", marker_radius=5)

Visualize the GEDI L4A Aboveground Biomass Density (AGBD) data with lonboard.

In [14]:

import leafmap

import leafmap

In [15]:

leafmap.view_vector(
    gdf, color_column="agbd", color_map="Greens", color_k=10, get_radius=25
)

leafmap.view_vector( gdf, color_column="agbd", color_map="Greens", color_k=10, get_radius=25 )