Back in 2018, California had to face a devastating camp fire that burned approximately 621 square kilometres. As I read through the article in Wikipedia I came to know that  it was the deadliest wildfire in the United States since the Cloquet fire in 1918. I wanted to see how the area looked before, during and after the Camp Fire. So I fired up Earth Engine on my Jupyter Lab notebook and started coding.

And here's how the final output looks like:

Before Camp Fire:

During Camp Fire:

After Camp Fire:

I wanted to make it interactive so I used Folium with it. Which is an amazing library that uses Leaflet to render an HTML file with the Earth Engine output. If you want to learn how I created this map here's my jupyter notebook that you can tinker with. I left some comments and markdown notes as well just in case you wanted know why I did that particular thing.

But if you just want the script here's the whole code:

import ee
import folium


ee.Authenticate()
ee.Initialize()

lat =  39.444012
lon = -121.833619

def add_ee_layer(self, ee_image_object, vis_params, name):
    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
    folium.raster_layers.TileLayer(
        tiles=map_id_dict['tile_fetcher'].url_format,
        name=name,
        attr='Map Data &copy; <a href="https://earthengine.google.com/">Google Earth Engine</a>',
        overlay=True,
        control=True
    ).add_to(self)


folium.Map.add_ee_layer = add_ee_layer

landsat_sequence = [0,2,5]
poi = ee.Geometry.Point(lon,lat)

start_date = '2018-10-01'
end_date = '2019-01-31'

landsat = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterBounds(poi).filterDate(start_date, end_date)

landsat_list = landsat.toList(landsat.size())
roi = poi.buffer(20000)

palette = ['red', 'yellow', 'green']

ndvi_parameters = {'min': 0,
                   'max': 0.4,
                   'dimensions': 512,
                   'palette': palette,
                   'region': roi}

my_map = folium.Map(location=[lat, lon], zoom_start=10)


for i in landsat_sequence:
    date = ee.Image(landsat_list.get(i)).get('DATE_ACQUIRED').getInfo()
    my_map.add_ee_layer(ee.Image(landsat_list.get(i)).normalizedDifference(['SR_B5', 'SR_B4']), 
                        ndvi_parameters, 
                        name=date)
    
folium.LayerControl(collapsed = False).add_to(my_map)

display(my_map)

my_map.save('index.html')

That's it! I hope you understood the way I've made the visualization. If you have any question feel free to reach out to me anytime!