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+Wildfires on the Beach: The Woolsey Fire¶
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+
Over two weeks in November 2018, the Woolsey Fire burned nearly 100,000 acres in the Santa Monica Mountains north of Malibu, California (Fig. 1). Three people were killed in the fire and over 1,600 structures were destroyed. Two other fires that ignited the same day as the Woolsey Fire caused widespread damage throughout California.
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+The Santa Ana season (Fall in Califonia), is prime time large and destructive fires. The Woolsey Fire happened in a location conducive for maximum growth; an area with little recent fire history (but history of fire going back to the 1930s) and only the beach to stop it.
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++Image source: https://www.redzone.co/2019/02/07/woolsey-fire-in-retrospect/
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+In [ ]:
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+import logging
+import os
+import pathlib
+import subprocess
+import warnings
+from glob import glob
+
+import earthpy.appeears as etapp
+import folium
+import geopandas as gpd
+import hvplot.pandas
+import hvplot.xarray as rx
+import pandas as pd
+import rioxarray as rxr
+import xarray as xr
+
+# Set up logging so AppeearsDownloader will log in notebook
+logging.basicConfig(level=logging.INFO)
+
+# Ignore FutureWarnings coming from hvplot
+warnings.simplefilter(action='ignore', category=FutureWarning)
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+In [ ]:
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+# Generating the path to the project directory
+project_direc = os.path.join(
+ pathlib.Path.home(), 'earth-analytics', 'data', 'woolsey-fire')
+
+# Create the project directory
+os.makedirs(project_direc, exist_ok=True)
+project_direc
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+Out[ ]:
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+'/home/jovyan/earth-analytics/data/woolsey-fire'+
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+In [ ]:
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+# Fetching historic fire perimeter data for the "WOOLSEY" incident
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+woolsey_gdf = gpd.read_file(
+ "https://services3.arcgis.com/T4QMspbfLg3qTGWY/arcgis/rest/services/"
+ "Historic_Geomac_Perimeters_2018/FeatureServer/0/query"
+ "?where=incidentname%20%3D%20'WOOLSEY'%20AND%20latest%20%3D%20'Y'"
+ "&outFields=*&outSR=4326&f=json"
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+woolsey_gdf
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+Out[ ]:
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+| + | OBJECTID | +agency | +comments | +active | +mapmethod | +datecurrent | +uniquefireidentifier | +fireyear | +incidentname | +pooownerunit | +... | +incomplex | +complexfirecode | +mergeid | +latest | +modifiedon | +createdon | +temp | +shape__Area | +shape__Length | +geometry | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +1198 | +Local Agency | ++ | N | +Mixed Methods | +1542499200000 | +2018-CAVNC-091023 | +2018 | +WOOLSEY | +CAVNC | +... | +N | ++ | + | Y | +None | +None | +None | +5.738505e+08 | +250299.909321 | +POLYGON ((-118.98273 34.11239, -118.98432 34.1... | +
1 rows × 30 columns
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+In [ ]:
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+# Malibu, CA locations
+# From https://www.latlong.net/place/malibu-ca-usa-32013.html.
+malibu_lat = 34.031246
+malibu_lon = -118.788193
+
+# Create a Folium map object
+m = folium.Map(
+ location=[malibu_lat, malibu_lon],
+ zoom_start=10.49,
+ scrollWheelZoom=False,
+ tiles='Stamen Watercolor')
+
+# Convert the GeoDataFrame to GeoJSON format
+malibu_geojson = woolsey_gdf.to_json()
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+# Add the GeoJSON data to the map as a GeoJson layer
+malibu_style = {"fillColor" : "red",
+ "color": "black",
+ "weight" : 2,
+ "dashArray": "5,5",
+ }
+folium.GeoJson(
+ malibu_geojson,
+ style_function=lambda feature: malibu_style
+ ).add_to(m)
+
+# Add a marker for Malibu, CA
+folium.Marker(location=[malibu_lat, malibu_lon]).add_to(m)
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+# Display the map
+m
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+Out[ ]:
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+Make this Notebook Trusted to load map: File -> Trust Notebook
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+In [ ]:
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+ # Initialize AppeearsDownloader for MODIS NDVI data
+ ndvi_downloader = etapp.AppeearsDownloader(
+ download_key='modis-ndvi',
+ ea_dir=project_direc,
+ product='MYD13Q1.061',
+ layer='_250m_16_days_NDVI',
+ start_date='06-01',
+ end_date='08-31',
+ recurring=True,
+ year_range=[2017, 2022],
+ polygon=woolsey_gdf
+ )
+
+ # Download files if the download directory does not exist
+ if not os.path.exists(ndvi_downloader.data_dir):
+ ndvi_downloader.download_files()
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+In [ ]:
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+ndvi_path_list = glob(os.path.join(
+ ndvi_downloader.data_dir, '*', '*NDVI*.tif'))
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+In [ ]:
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+doy_start = -19
+doy_end = -12
+scale_factor = 10000
+ndvi_da_list = []
+for ndvi_path in ndvi_path_list:
+ # Get date from file name
+ doy = ndvi_path[doy_start:doy_end]
+ date = pd.to_datetime(doy, format='%Y%j')
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+ # Open dataset
+ da = rxr.open_rasterio(ndvi_path, masked=True).squeeze()
+
+ # Prepare to concatenate: Add date dimension and clean up metadata
+ da = da.assign_coords({'date': date})
+ da = da.expand_dims({'date': 1})
+ da.name = 'NDVI'
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+ # Divide by scale factor
+ da = da / scale_factor
+ # Add the DataArray to the end of the accumulator list
+ ndvi_da_list.append(da)
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+In [ ]:
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+ndvi_ds = xr.combine_by_coords(ndvi_da_list, coords=['date'])
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+In [ ]:
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+dndvi_da = (ndvi_ds
+ .sel(date='2020')
+ .mean('date')
+ .NDVI
+ - ndvi_ds
+ .sel(date='2017')
+ .mean('date')
+ .NDVI
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+(dndvi_da.hvplot(cmap='PiYG'))
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+# Get the area outside the fire boundary
+out_gdf = (
+ gpd.GeoDataFrame(geometry=woolsey_gdf.envelope)
+ .overlay(woolsey_gdf, how="difference")
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+out_gdf
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+Out[ ]:
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+| + | geometry | +
|---|---|
| 0 | +MULTIPOLYGON (((-118.99636 34.00540, -118.9963... | +
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+In [ ]:
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+# Clip the data array to the boundaries both inside and outside the fire
+# boundary
+ndvi_fire_ds = ndvi_ds.rio.clip(woolsey_gdf.geometry)
+ndvi_out_ds = ndvi_ds.rio.clip(out_gdf.geometry)
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+In [ ]:
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+ndvi_fire_df = (
+ ndvi_fire_ds
+ .groupby(ndvi_fire_ds.date.dt.year)
+ .mean(...)
+ .NDVI
+ .to_dataframe()[['NDVI']])
+ndvi_out_df = (
+ ndvi_out_ds
+ .groupby(ndvi_out_ds.date.dt.year)
+ .mean(...)
+ .NDVI
+ .to_dataframe()[['NDVI']])
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+ndvi_fire_df, ndvi_out_df
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+Out[ ]:
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+( NDVI + year + 2017 0.458010 + 2018 0.429461 + 2019 0.400919 + 2020 0.406328 + 2021 0.325053 + 2022 0.390789, + NDVI + year + 2017 0.398876 + 2018 0.380254 + 2019 0.416327 + 2020 0.405906 + 2021 0.354659 + 2022 0.381206)+
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+In [ ]:
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+#take the difference of the NDVI outside and inside the fire boundaries
+(ndvi_fire_df-ndvi_out_df).hvplot(
+ xlabel ='Year', ylabel='dNDVI',
+ title='Difference in NDVI between inside and outside Woolsey fire boundary'
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+Vegetation slow to recover after the Woolsey Fire¶
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- The southern coast of California has a relatively low NDVI and the natural landscape of the Santa Monica Mountains is dominated by diverse species of woody shrubs, also known as chaparral. Wildfire is a necessary phase in the life cycle of chaparral plants. Despite the fact that wildfire is necessity for charral, the slow regrowth of vegetation could be in part due to the severe drought with in the state during that time period. +