!pip install OWSLib==0.28.1 --quietGet 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
- Use
OWSLibto determine what data is available and inspect the metadata - Use
OWSLibto filter and read the data - Use
geopandasandfoliumto 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.
from owslib.ogcapi.features import Features
import geopandas as gpd
import datetime as dt
from datetime import datetime, timedeltaAbout 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://firenrt.delta-backend.com"
w = Features(url=OGC_URL)
w.feature_collections()['public.eis_fire_snapshot_fireline_nrt',
'public.eis_fire_lf_fireline_archive',
'public.eis_fire_lf_perimeter_archive',
'public.eis_fire_snapshot_perimeter_nrt',
'public.eis_fire_lf_newfirepix_nrt',
'public.eis_fire_lf_nfplist_nrt',
'public.eis_fire_lf_perimeter_nrt',
'public.eis_fire_lf_nfplist_archive',
'public.eis_fire_lf_newfirepix_archive',
'public.eis_fire_snapshot_newfirepix_nrt',
'public.eis_fire_lf_fireline_nrt',
'public.st_squaregrid',
'public.st_hexagongrid',
'public.st_subdivide']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 particularly interested in the spatial and temporal extents of the data.
perm["extent"]{'spatial': {'bbox': [[-124.63103485107422,
24.069578170776367,
-62.97413635253906,
49.40156555175781]],
'crs': 'http://www.opengis.net/def/crs/OGC/1.3/CRS84'},
'temporal': {'interval': [['2023-06-07T00:00:00+00:00',
'2023-06-27T00:00:00+00:00']],
'trs': 'http://www.opengis.net/def/uom/ISO-8601/0/Gregorian'}}In addition to getting metadata about the data we can access 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"]{'wkb_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'},
'isactive': {'name': 'isactive', 'type': 'number'},
'meanfrp': {'name': 'meanfrp', 'type': 'number'},
'n_newpixels': {'name': 'n_newpixels', 'type': 'number'},
'n_pixels': {'name': 'n_pixels', 'type': 'number'},
'ogc_fid': {'name': 'ogc_fid', 'type': 'number'},
'pixden': {'name': 'pixden', 'type': 'number'},
't': {'name': 't', 'type': 'string'}}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_nrtcollection - subsetting it by space using the
bboxparameter - subsetting it by time using the
datetimeparameter - filtering for fires over 5km^2 and over 2 days long using the
filterparameter. Thefilterparameter 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 the most recent fire perimeters, and 7 days before most recent fire perimeter
most_recent_time = max(*perm["extent"]["temporal"]["interval"])
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 = 2023-06-27T00:00:00+00:00
Last week = 2023-06-20T00:00:00+00:00perm_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"]TrueYou 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://firenrt.delta-backend.com/collections/public.eis_fire_snapshot_perimeter_nrt/items?bbox=-106.8%2C24.5%2C-72.9%2C37.3&datetime=2023-06-20T00%3A00%3A00%2B00%3A00%2F2023-06-27T00%3A00%3A00%2B00%3A00&limit=1000&filter=farea%3E5+AND+duration%3E2'Read data
In addition to all the summary fields, the perm_results dict contains all the data. We can pass the data into geopandas to make it easier to interact with.
df = gpd.GeoDataFrame.from_features(perm_results["features"])
df| geometry | duration | farea | fireid | flinelen | fperim | isactive | meanfrp | n_newpixels | n_pixels | ogc_fid | pixden | t | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | POLYGON ((-95.88101 30.22697, -95.88102 30.227... | 45.0 | 9.811672 | 63986 | 0.0 | 12.124017 | 1 | 0.0 | 0 | 104 | 430 | 10.599620 | 2023-06-26T12:00:00 |
| 1 | POLYGON ((-92.83274 29.81987, -92.83264 29.820... | 4.0 | 8.958987 | 80714 | 0.0 | 12.583987 | 0 | 0.0 | 0 | 63 | 538 | 7.032045 | 2023-06-20T12:00:00 |
| 2 | POLYGON ((-89.29801 36.86458, -89.29801 36.864... | 5.0 | 7.138301 | 80749 | 0.0 | 13.474343 | 0 | 0.0 | 0 | 25 | 1902 | 3.502234 | 2023-06-21T12:00:00 |
| 3 | POLYGON ((-106.33318 36.38887, -106.32771 36.3... | 5.5 | 11.325657 | 82393 | 0.0 | 18.949202 | 1 | 0.0 | 0 | 157 | 5271 | 13.862330 | 2023-06-26T00:00:00 |
| 4 | POLYGON ((-106.00861 36.60666, -106.00859 36.6... | 11.0 | 12.858160 | 79918 | 0.0 | 20.962730 | 1 | 0.0 | 0 | 85 | 5273 | 6.610588 | 2023-06-25T12:00:00 |
| 5 | MULTIPOLYGON (((-101.28638 32.47797, -101.2863... | 14.0 | 9.415797 | 78723 | 0.0 | 36.674171 | 1 | 0.0 | 0 | 114 | 5647 | 12.107313 | 2023-06-26T00:00:00 |
| 6 | MULTIPOLYGON (((-102.69888 31.72183, -102.6987... | 27.0 | 10.484613 | 71936 | 0.0 | 17.485984 | 1 | 0.0 | 0 | 123 | 5736 | 11.731477 | 2023-06-25T12:00:00 |
| 7 | POLYGON ((-103.44609 31.79658, -103.44608 31.7... | 17.0 | 23.515158 | 76919 | 0.0 | 22.167102 | 1 | 0.0 | 0 | 10 | 5774 | 0.425258 | 2023-06-25T00:00:00 |
| 8 | POLYGON ((-103.89600 32.08170, -103.89603 32.0... | 7.0 | 11.917955 | 79765 | 0.0 | 21.145696 | 0 | 0.0 | 0 | 7 | 5806 | 0.587349 | 2023-06-21T00:00:00 |
| 9 | POLYGON ((-106.63614 30.07301, -106.63617 30.0... | 3.5 | 76.317924 | 83254 | 0.0 | 51.647117 | 1 | 0.0 | 0 | 341 | 5881 | 4.468151 | 2023-06-26T00:00:00 |
| 10 | POLYGON ((-106.78326 30.05115, -106.78330 30.0... | 2.5 | 10.102581 | 83084 | 0.0 | 13.674702 | 1 | 0.0 | 0 | 72 | 5901 | 7.126891 | 2023-06-24T12:00:00 |
| 11 | POLYGON ((-102.85583 27.81858, -102.85601 27.8... | 8.0 | 33.159692 | 81431 | 0.0 | 25.731754 | 1 | 0.0 | 0 | 340 | 5969 | 10.253412 | 2023-06-26T00:00:00 |
| 12 | POLYGON ((-97.43908 28.43692, -97.43900 28.436... | 19.0 | 5.312484 | 73340 | 0.0 | 10.166578 | 0 | 0.0 | 0 | 40 | 6187 | 7.529434 | 2023-06-20T12:00:00 |
| 13 | POLYGON ((-100.71656 25.53519, -100.71659 25.5... | 6.5 | 5.131956 | 79873 | 0.0 | 8.995199 | 0 | 0.0 | 0 | 98 | 6423 | 19.096034 | 2023-06-21T00:00:00 |
| 14 | POLYGON ((-102.25288 26.97686, -102.25289 26.9... | 6.0 | 10.666481 | 82098 | 0.0 | 13.479402 | 1 | 0.0 | 0 | 73 | 6470 | 6.843869 | 2023-06-26T00:00:00 |
| 15 | POLYGON ((-106.64036 25.02528, -106.64036 25.0... | 14.5 | 9.843044 | 78547 | 0.0 | 18.376243 | 1 | 0.0 | 0 | 62 | 7118 | 6.298864 | 2023-06-26T00:00:00 |
| 16 | POLYGON ((-106.52000 24.91752, -106.52000 24.9... | 12.5 | 16.358760 | 79217 | 0.0 | 19.469750 | 1 | 0.0 | 0 | 138 | 7129 | 8.435847 | 2023-06-25T12:00:00 |
| 17 | POLYGON ((-106.47703 24.93926, -106.47705 24.9... | 7.0 | 5.051376 | 82013 | 0.0 | 10.648427 | 1 | 0.0 | 0 | 47 | 7143 | 9.304395 | 2023-06-26T12:00:00 |
Explore data
We can quickly explore the data by setting the coordinate reference system (crs) and using .explore()
df = df.set_crs("EPSG:4326")
df.explore()Make this Notebook Trusted to load map: File -> Trust Notebook
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"]{'wkb_geometry': {'$ref': 'https://geojson.org/schema/Geometry.json'},
'fireid': {'name': 'fireid', 'type': 'number'},
'mergeid': {'name': 'mergeid', 'type': 'number'},
'ogc_fid': {'name': 'ogc_fid', 'type': 'number'},
'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()
m = fline.explore(m=m, color="orange")
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