Get Fire Perimeters from an OGC API

Explore data available through an OGC API, and how to filter data temporally, spatially, and by property.

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Approach

1. Use OWSLib to determine what data is available and inspect the metadata
2. Use OWSLib to filter and read the data
3. Use geopandas and folium to analyze and plot the data

Note that the default examples environment is missing one requirement: oswlib. We can pip install that before we move on.

%pip install OWSLib==0.28.1 --quiet

Note: you may need to restart the kernel to use updated packages.

import datetime as dt

import geopandas as gpd
from owslib.ogcapi.features import Features
import requests

About the Data

The fire data shown is generated by the FEDs algorithm. The FEDs algorithm tracks fire movement and severity by ingesting observations from the VIIRS thermal sensors on the Suomi NPP and NOAA-20 satellites. This algorithm uses raw VIIRS observations to generate a polygon of the fire, locations of the active fire line, and estimates of fire mean Fire Radiative Power (FRP). The VIIRS sensors overpass at ~1:30 AM and PM local time, and provide estimates of fire evolution ~ every 12 hours. The data produced by this algorithm describe where fires are in space and how fires evolve through time. This CONUS-wide implementation of the FEDs algorithm is based on Chen et al 2020’s algorithm for California.

The data produced by this algorithm is considered experimental.

Look at the data that is availible through the OGC API

The datasets that are distributed throught the OGC API are organized into collections. We can display the collections with the command:

OGC_URL = "https://openveda.cloud/api/features"

w = Features(url=OGC_URL)
w.feature_collections()

['public.eis_fire_lf_perimeter_archive',
 'public.eis_fire_lf_newfirepix_archive',
 'public.eis_fire_lf_fireline_archive',
 'public.eis_fire_lf_fireline_nrt',
 'public.eis_fire_snapshot_fireline_nrt',
 'public.eis_fire_snapshot_newfirepix_nrt',
 'public.eis_fire_lf_newfirepix_nrt',
 'public.eis_fire_lf_perimeter_nrt',
 'public.eis_fire_snapshot_perimeter_nrt',
 'public.st_subdivide',
 'public.st_hexagongrid',
 'public.st_squaregrid']

We will focus on the public.eis_fire_snapshot_fireline_nrt collection, the public.eis_fire_snapshot_perimeter_nrt collection, and the public.eis_fire_lf_perimeter_archive collection here.

Inspect the metatdata for public.eis_fire_snapshot_perimeter_nrt collection

We can access information that describes the public.eis_fire_snapshot_perimeter_nrt table.

perm = w.collection("public.eis_fire_snapshot_perimeter_nrt")

We are interested in accessing the queryable fields. Each of these fields will represent a column in our dataframe.

perm_q = w.collection_queryables("public.eis_fire_snapshot_perimeter_nrt")
perm_q["properties"]

{'geometry': {'$ref': 'https://geojson.org/schema/Geometry.json'},
 'duration': {'name': 'duration', 'type': 'number'},
 'farea': {'name': 'farea', 'type': 'number'},
 'fireid': {'name': 'fireid', 'type': 'number'},
 'flinelen': {'name': 'flinelen', 'type': 'number'},
 'fperim': {'name': 'fperim', 'type': 'number'},
 'geom_counts': {'name': 'geom_counts', 'type': 'string'},
 'isactive': {'name': 'isactive', 'type': 'number'},
 'low_confidence_grouping': {'name': 'low_confidence_grouping',
  'type': 'number'},
 'meanfrp': {'name': 'meanfrp', 'type': 'number'},
 'n_newpixels': {'name': 'n_newpixels', 'type': 'number'},
 'n_pixels': {'name': 'n_pixels', 'type': 'number'},
 'pixden': {'name': 'pixden', 'type': 'number'},
 'primarykey': {'name': 'primarykey', 'type': 'string'},
 'region': {'name': 'region', 'type': 'string'},
 't': {'name': 't', 'type': 'string'}}

We also want to know the most recent data from the dataset. OWSLib doesn’t enable all of the same fields as the API does, so to find the time of the most recent data, we will parse some json from the API.

This is a URL call to the API: https://openveda.cloud/api/features/collections/public.eis_fire_lf_fireline_nrt/items?f=geojson&sortby=-t"

• ‘public.eis_fire_lf_fireline_nrt’ is our collection name (Large fires near real time)
• ‘f=geojson’ is requesting our output in geojson format
• ‘sortby=-t’ is requesting the output to be sorted by column ‘t’, and the ‘-t’ requests the output in reverse order, or newest entry first.

url_example = "https://openveda.cloud/api/features/collections/public.eis_fire_lf_fireline_nrt/items?f=geojson&sortby=-t" 
response = requests.get(url_example)
most_recent_time = response.json()['features'][0]['properties']['t'] # Extracting the most recent time from the json dictionary. 
most_recent_time

Filter the data

It is always a good idea to do any data filtering as early as possible. In this example we know that we want the data for particular spatial and temporal extents. We can apply those and other filters using the OWSLib package.

In the below example we are:

• choosing the public.eis_fire_snapshot_perimeter_nrt collection
• subsetting it by space using the bbox parameter
• subsetting it by time using the datetime parameter
• filtering for fires over 5km^2 and over 2 days long using the filter parameter. The filter parameter lets us filter by the columns in ‘public.eis_fire_snapshot_perimeter_nrt’ using SQL-style queries.

NOTE: The limit parameter desginates the maximum number of objects the query will return. The default limit is 10, so if we want to all of the fire perimeters within certain conditions, we need to make sure that the limit is large.

## Get 7 days before most recent fire perimeter
most_recent_time = most_recent_time + "+00:00"
now = dt.datetime.strptime(most_recent_time, "%Y-%m-%dT%H:%M:%S+00:00")
last_week = now - dt.timedelta(weeks=1)
last_week = dt.datetime.strftime(last_week, "%Y-%m-%dT%H:%M:%S+00:00")
print("Most Recent Time =", most_recent_time)
print("Last week =", last_week)

Most Recent Time = 2025-05-21T12:00:00+00:00
Last week = 2025-05-14T12:00:00+00:00

perm_results = w.collection_items(
    "public.eis_fire_snapshot_perimeter_nrt",  # name of the dataset we want
    bbox=["-106.8", "24.5", "-72.9", "37.3"],  # coodrinates of bounding box,
    datetime=[last_week + "/" + most_recent_time],  # date range
    limit=1000,  # max number of items returned
    filter="farea>5 AND duration>2",  # additional filters based on queryable fields
)

The result is a dictionary containing all of the data and some summary fields. We can look at the keys to see what all is in there.

perm_results.keys()

dict_keys(['type', 'id', 'title', 'description', 'numberMatched', 'numberReturned', 'links', 'features'])

For instance you can check the total number of matched items and make sure that it is equal to the number of returned items. This is how you know that the limit you defined above is high enough.

perm_results["numberMatched"] == perm_results["numberReturned"]

True

You can also access the data directly in the browser or in an HTTP GET call using the constructed link.

perm_results["links"][1]["href"]

'https://openveda.cloud/api/features/collections/public.eis_fire_snapshot_perimeter_nrt/items?bbox=-106.8%2C24.5%2C-72.9%2C37.3&datetime=2025-05-14T12%3A00%3A00%2B00%3A00%2F2025-05-21T12%3A00%3A00%2B00%3A00&limit=1000&filter=farea%3E5+AND+duration%3E2'

Visualize Most Recent Fire Perimeters with Firelines

If we wanted to combine collections to make more informative analyses, we can use some of the same principles.

First we’ll get the queryable fields, and the extents:

fline_q = w.collection_queryables("public.eis_fire_snapshot_fireline_nrt")
fline_collection = w.collection("public.eis_fire_snapshot_fireline_nrt")
fline_q["properties"]

{'geometry': {'$ref': 'https://geojson.org/schema/Geometry.json'},
 'fireid': {'name': 'fireid', 'type': 'number'},
 'mergeid': {'name': 'mergeid', 'type': 'number'},
 'primarykey': {'name': 'primarykey', 'type': 'string'},
 'region': {'name': 'region', 'type': 'string'},
 't': {'name': 't', 'type': 'string'}}

Read

Then we’ll use those fields to get most recent fire perimeters and fire lines.

perm_results = w.collection_items(
    "public.eis_fire_snapshot_perimeter_nrt",
    datetime=most_recent_time,
    limit=1000,
)
perimeters = gpd.GeoDataFrame.from_features(perm_results["features"])

## Get the most recent fire lines
perimeter_ids = perimeters.fireid.unique()
perimeter_ids = ",".join(map(str, perimeter_ids))

fline_results = w.collection_items(
    "public.eis_fire_snapshot_fireline_nrt",
    limit=1000,
    filter="fireid IN ("
    + perimeter_ids
    + ")",  # only the fires from the fire perimeter query above
)
fline = gpd.GeoDataFrame.from_features(fline_results["features"])

Visualize

perimeters = perimeters.set_crs("epsg:4326")
fline = fline.set_crs("epsg:4326")

m = perimeters.explore(zoom_start=5, location=(41, -122))
m = fline.explore(m=m, color="orange")
m

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Visualize the Growth of the Camp Fire

We may be interested in understanding how a fire evolved through time. To do this, we can work with the “Large fire” or “lf” perimeter collections. The public.eis_fire_lf_perimeter_nrt collection has the full spread history of fires from this year. public.eis_fire_lf_perimeter_archive has the full spread history of fires from 2018-2021 that were in the Western United States. The Camp Fire was in 2018, so we will work with the public.eis_fire_lf_perimeter_archive collection.

We can start by querying with information specific to the Camp Fire, like it’s genreal region (Northern California), and when it was active (November 2018). With that information, we can get the fireID associated with the Camp Fire.

perimeters_archive_results = w.collection_items(
    "public.eis_fire_lf_perimeter_archive",
    bbox=["-124.52", "39.2", "-120", "42"],  # North California bounding box,
    datetime=["2018-11-01T00:00:00+00:00/2018-11-30T12:00:00+00:00"],
    limit=3000,
)

perimeters_archive_results

perimeters = gpd.GeoDataFrame.from_features(perimeters_archive_results["features"])
perimeters = perimeters.sort_values(by="t", ascending=False)
perimeters = perimeters.set_crs("epsg:4326")

print(perimeters.fireid.unique())
m = perimeters.explore(
    style_kwds={"fillOpacity": 0}, zoom_start=9, location=(39.7, -121.4)
)
m

['F17028' 'F18493']

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Based on the map, we know that the fireID for the Camp Fire is “F17028”. We can use that to directly query for that particular fire.

perimeters_archive_results = w.collection_items(
    "public.eis_fire_lf_perimeter_archive",
    filter="fireid = 'F17028'",
    datetime=["2018-01-01T00:00:00+00:00/2018-12-31T12:00:00+00:00"],
    limit=3000,
)

perimeters_archive_results

perimeters = gpd.GeoDataFrame.from_features(perimeters_archive_results["features"])
perimeters = perimeters.sort_values(by="t", ascending=False)
perimeters = perimeters.set_crs("epsg:4326")

m = perimeters.explore(
    style_kwds={"fillOpacity": 0}, zoom_start=12, location=(39.7, -121.4)
)
m

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