National Wetlands Inventory

The US National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US wetlands. The US Fish and Wildlife Service (FWS) is the principal US Federal agency tasked with providing information to the public on the status and trends of our Nation's wetlands. Wetlands provide a multitude of ecological, economic and social benefits.

Product Details

Visibility: Public
Owner: Qiusheng Wu
Created: 10 Nov 2023
Last Updated: 3 Apr 2025

Product Contents

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README

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National Wetlands Inventory

Description

This dataset is a copy of the National Wetlands Inventory, offering the data in more GIS-friendly and cloud-native geospatial formats. The original dataset is distributed as zipped Geodatabase files, and is available for download from here.

Data download

The script below was used to download the data from the National Wetlands Inventory in Geodatabase format from here. The script uses the leafmap Python package.

First, create a conda environment with the required packages:

1conda create -n gdal python=3.11
2conda activate gdal
3conda install -c conda-forge mamba
4mamba install -c conda-forge libgdal-arrow-parquet gdal leafmap
1conda create -n gdal python=3.11
2conda activate gdal
3conda install -c conda-forge mamba
4mamba install -c conda-forge libgdal-arrow-parquet gdal leafmap

If you are using Google Colab, you can install the packages as follows:

1# %pip install leafmap lonboard==0.3.0
1# %pip install leafmap lonboard==0.3.0

Then, run the script below:

1import leafmap
2import pandas as pd
3
4url = 'https://open.gishub.org/data/us/us_states.csv'
5df = pd.read_csv(url)
6ids = df['id'].tolist()
7ids.sort()
8urls = [f"https://documentst.ecosphere.fws.gov/wetlands/data/State-Downloads/{id}_geodatabase_wetlands.zip" for id in ids]
9leafmap.download_files(urls, out_dir='.', unzip=True)
1import leafmap
2import pandas as pd
3
4url = 'https://open.gishub.org/data/us/us_states.csv'
5df = pd.read_csv(url)
6ids = df['id'].tolist()
7ids.sort()
8urls = [f"https://documentst.ecosphere.fws.gov/wetlands/data/State-Downloads/{id}_geodatabase_wetlands.zip" for id in ids]
9leafmap.download_files(urls, out_dir='.', unzip=True)

Data conversion

The script below was used to convert the data from the original Geodatabase format to Parquet format. The script uses the leafmap Python package.

1import leafmap
2import pandas as pd
3
4url = 'https://open.gishub.org/data/us/us_states.csv'
5df = pd.read_csv(url)
6ids = df['id'].tolist()
7
8for index, state in enumerate(ids):
9    print(f'Processing {state} ({index+1}/{len(ids)})')
10    gdb = f"{state}_geodatabase_wetlands.gdb/"
11    layer_name = f'{state}_Wetlands'
12    leafmap.gdb_to_vector(gdb, ".", gdal_driver="Parquet", layers=[layer_name])
1import leafmap
2import pandas as pd
3
4url = 'https://open.gishub.org/data/us/us_states.csv'
5df = pd.read_csv(url)
6ids = df['id'].tolist()
7
8for index, state in enumerate(ids):
9    print(f'Processing {state} ({index+1}/{len(ids)})')
10    gdb = f"{state}_geodatabase_wetlands.gdb/"
11    layer_name = f'{state}_Wetlands'
12    leafmap.gdb_to_vector(gdb, ".", gdal_driver="Parquet", layers=[layer_name])

The total file size of the Geodatabase files is 32.5 GB. The total file size of the Parquet files is 75.8 GB.

Data access

The script below can be used to access the data using DuckDB. The script uses the duckdb Python package.

1import duckdb
2
3con = duckdb.connect()
4con.install_extension("spatial")
5con.load_extension("spatial")
6
7state = "DC"    # Change to the US State of your choice
8url = f"https://data.source.coop/giswqs/nwi/wetlands/{state}_Wetlands.parquet"
9con.sql(f"SELECT * EXCLUDE geometry, ST_GeomFromWKB(geometry) FROM '{url}'")
1import duckdb
2
3con = duckdb.connect()
4con.install_extension("spatial")
5con.load_extension("spatial")
6
7state = "DC"    # Change to the US State of your choice
8url = f"https://data.source.coop/giswqs/nwi/wetlands/{state}_Wetlands.parquet"
9con.sql(f"SELECT * EXCLUDE geometry, ST_GeomFromWKB(geometry) FROM '{url}'")

Alternatively, you can use the aws cli to access the data directly from the S3 bucket:

1aws s3 ls s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/
1aws s3 ls s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/

Data visualization

To visualize the data, you can use the leafmap Python package with the lonboard backend. The script below shows how to visualize the data.

1import leafmap
2
3state = "DC"   # Change to the US State of your choice
4url = f"https://data.source.coop/giswqs/nwi/wetlands/{state}_Wetlands.parquet"
5gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:5070', dst_crs='EPSG:4326')
6leafmap.view_vector(gdf, get_fill_color=[0, 0, 255, 128])
1import leafmap
2
3state = "DC"   # Change to the US State of your choice
4url = f"https://data.source.coop/giswqs/nwi/wetlands/{state}_Wetlands.parquet"
5gdf = leafmap.read_parquet(url, return_type='gdf', src_crs='EPSG:5070', dst_crs='EPSG:4326')
6leafmap.view_vector(gdf, get_fill_color=[0, 0, 255, 128])

vector

Alternatively, you can specify a color map to visualize the data.

1color_map =  {
2        "Freshwater Forested/Shrub Wetland": (0, 136, 55),
3        "Freshwater Emergent Wetland": (127, 195, 28),
4        "Freshwater Pond": (104, 140, 192),
5        "Estuarine and Marine Wetland": (102, 194, 165),
6        "Riverine": (1, 144, 191),
7        "Lake": (19, 0, 124),
8        "Estuarine and Marine Deepwater": (0, 124, 136),
9        "Other": (178, 134, 86),
10    }
11leafmap.view_vector(gdf, color_column='WETLAND_TYPE', color_map=color_map, opacity=0.5)
1color_map =  {
2        "Freshwater Forested/Shrub Wetland": (0, 136, 55),
3        "Freshwater Emergent Wetland": (127, 195, 28),
4        "Freshwater Pond": (104, 140, 192),
5        "Estuarine and Marine Wetland": (102, 194, 165),
6        "Riverine": (1, 144, 191),
7        "Lake": (19, 0, 124),
8        "Estuarine and Marine Deepwater": (0, 124, 136),
9        "Other": (178, 134, 86),
10    }
11leafmap.view_vector(gdf, color_column='WETLAND_TYPE', color_map=color_map, opacity=0.5)

vector-color

Display a legend for the data.

1leafmap.Legend(title="Wetland Type", legend_dict=color_map)
1leafmap.Legend(title="Wetland Type", legend_dict=color_map)

legend

Data analysis

Find out the total number of wetlands in the United States by aggregating the 51 parquet files.

1con.sql(f"""
2SELECT COUNT(*) AS Count
3FROM 's3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet'
4""")
1con.sql(f"""
2SELECT COUNT(*) AS Count
3FROM 's3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet'
4""")

Find out the number of wetlands in each state. Note that the NWI datasets do not contain a field for state names. The filename argument can be used to add an extra filename column to the result that indicates which row came from which file.

1df = con.sql(f"""
2SELECT filename, COUNT(*) AS Count
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY filename
5ORDER BY COUNT(*) DESC;
6""").df()
7df.head()
1df = con.sql(f"""
2SELECT filename, COUNT(*) AS Count
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY filename
5ORDER BY COUNT(*) DESC;
6""").df()
7df.head()

Inspect the list of filenames.

1df['filename'].tolist()
1df['filename'].tolist()

Create a State column based on the filename column by extracting the state name from the filename.

1count_df = con.sql(f"""
2SELECT SUBSTRING(filename, LENGTH(filename) - 18, 2) AS State, COUNT(*) AS Count
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY State
5ORDER BY COUNT(*) DESC;
6""").df()
7count_df.head(10)
1count_df = con.sql(f"""
2SELECT SUBSTRING(filename, LENGTH(filename) - 18, 2) AS State, COUNT(*) AS Count
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY State
5ORDER BY COUNT(*) DESC;
6""").df()
7count_df.head(10)

Create a wetlands table from the DataFrame above.

1con.sql("CREATE OR REPLACE TABLE wetlands AS FROM count_df")
2con.sql("FROM wetlands")
1con.sql("CREATE OR REPLACE TABLE wetlands AS FROM count_df")
2con.sql("FROM wetlands")

To visualize the data on the map, we need a GeoDataFrame. Let's create a states table from the us_states.parquet file.

1url = 'https://open.gishub.org/data/us/us_states.parquet'
2con.sql(
3    f"""
4CREATE OR REPLACE TABLE states AS
5SELECT * EXCLUDE geometry, ST_GeomFromWKB(geometry)
6AS geometry FROM '{url}'
7"""
8)
9con.sql("FROM states")
1url = 'https://open.gishub.org/data/us/us_states.parquet'
2con.sql(
3    f"""
4CREATE OR REPLACE TABLE states AS
5SELECT * EXCLUDE geometry, ST_GeomFromWKB(geometry)
6AS geometry FROM '{url}'
7"""
8)
9con.sql("FROM states")

Join the wetlands table with the states table.

1con.sql("""
2SELECT * FROM states INNER JOIN wetlands ON states.id = wetlands.State
3""")
1con.sql("""
2SELECT * FROM states INNER JOIN wetlands ON states.id = wetlands.State
3""")

Export the joined table as a Pandas DataFrame.

1df = con.sql("""
2SELECT name, State, Count, ST_AsText(geometry) as geometry
3FROM states INNER JOIN wetlands ON states.id = wetlands.State
4""").df()
5df.head()
1df = con.sql("""
2SELECT name, State, Count, ST_AsText(geometry) as geometry
3FROM states INNER JOIN wetlands ON states.id = wetlands.State
4""").df()
5df.head()

Convert the Pandas DataFrame to a GeoDataFrame.

1gdf = leafmap.df_to_gdf(df, src_crs="EPSG:4326")
1gdf = leafmap.df_to_gdf(df, src_crs="EPSG:4326")

Visualize the data on the map.

1m = leafmap.Map()
2m.add_data(
3    gdf, column='Count', scheme='Quantiles', cmap='Greens', legend_title='Wetland Count'
4)
5m
1m = leafmap.Map()
2m.add_data(
3    gdf, column='Count', scheme='Quantiles', cmap='Greens', legend_title='Wetland Count'
4)
5m

Create a pie chart to show the percentage of wetlands in each state.

1leafmap.pie_chart(count_df, 'State', 'Count', height=800, title='Number of Wetlands by State')
1leafmap.pie_chart(count_df, 'State', 'Count', height=800, title='Number of Wetlands by State')

Create a bar chart to show the number of wetlands in each state.

1leafmap.bar_chart(count_df, 'State', 'Count', title='Number of Wetlands by State')
1leafmap.bar_chart(count_df, 'State', 'Count', title='Number of Wetlands by State')

Calculate the total area of wetlands in the United States. It takes about 3 minutes to run this query. Please be patient.

1con.sql(f"""
2SELECT SUM(Shape_Area) /  1000000 AS Area_SqKm
3FROM 's3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet'
4""")
1con.sql(f"""
2SELECT SUM(Shape_Area) /  1000000 AS Area_SqKm
3FROM 's3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet'
4""")

Calculate the total area of wetlands in each state. It takes about 3 minutes to run this query. Please be patient.

1area_df = con.sql(f"""
2SELECT SUBSTRING(filename, LENGTH(filename) - 18, 2) AS State, SUM(Shape_Area) /  1000000 AS Area_SqKm
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY State
5ORDER BY COUNT(*) DESC;
6""").df()
7area_df.head(10)
1area_df = con.sql(f"""
2SELECT SUBSTRING(filename, LENGTH(filename) - 18, 2) AS State, SUM(Shape_Area) /  1000000 AS Area_SqKm
3FROM read_parquet('s3://us-west-2.opendata.source.coop/giswqs/nwi/wetlands/*.parquet', filename=true)
4GROUP BY State
5ORDER BY COUNT(*) DESC;
6""").df()
7area_df.head(10)

Create a pie chart to show the percentage of wetlands in each state.

1leafmap.pie_chart(area_df, 'State', 'Area_SqKm', height=900, title='Wetland Area by State')
1leafmap.pie_chart(area_df, 'State', 'Area_SqKm', height=900, title='Wetland Area by State')

Create a bar chart to show the wetland area in each state.

1leafmap.bar_chart(area_df, 'State', 'Area_SqKm', title='Wetland Area by State')
1leafmap.bar_chart(area_df, 'State', 'Area_SqKm', title='Wetland Area by State')