pages/05_California_wildfire.py

import ee
import geemap
import solara

def applyBurnedPopulationMask(image, burnedData):
        popMask = image.select('population').neq(0)  # masking out areas that have 0 population
        burnedMask = burnedData.select('BurnDate').neq(0)  # masking out areas that were not burned
        mask = popMask.And(burnedMask)  # only keep areas that were both burned and have a population
        return image.updateMask(mask).select("population").copyProperties(image, ['population'])



class Map(geemap.Map):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.add_ee_data()
        self.basemap_added = False
        # self.add_plot_gui()
    
         
    def add_ee_data(self):
        california_area = ee.FeatureCollection("TIGER/2018/States").filter(ee.Filter.eq('NAME', 'California'))
        # population grid
        popdataset2020 = ee.ImageCollection("WorldPop/GP/100m/pop")\
            .filterDate("2020-01-01", "2021-01-01")

        pop2020 = popdataset2020.mean().clip(california_area)

        pop_params = {
        'bands': ['population'],
        'min': 1.0,
        'max': 60.0,
        'palette' : ['bdd7e7','6baed6','2171b5']
        }

        popdataset2001 = ee.ImageCollection("WorldPop/GP/100m/pop")\
            .filterDate("2001-01-01", "2002-01-01")

        pop2001 = popdataset2001.mean().clip(california_area)

        # burned
        burnedCollection2020 = ee.ImageCollection('MODIS/006/MCD64A1').filter(ee.Filter.date('2020-01-01', '2021-01-01'))
        burned2020 = burnedCollection2020.select('BurnDate').mean().clip(california_area)
        burned_params = {
        'min': 30.0,
        'max': 341.0,
        'palette':['red']
        }

        burnedCollection2001 = ee.ImageCollection('MODIS/006/MCD64A1').filter(ee.Filter.date('2001-01-01', '2002-01-01'))
        burned2001 = burnedCollection2001.select('BurnDate').mean().clip(california_area)
        burnedpop2020 = popdataset2020.map(lambda img: applyBurnedPopulationMask(img, burned2020))
        burnedpop2001 = popdataset2001.map(lambda img: applyBurnedPopulationMask(img, burned2001))

        impacted_pop_params = {
            'bands': ['population'],
            'min': 0.0,
            'max': 60.0,
            'palette' : ['white','bdd7e7','6baed6','2171b5']
        }

        # plot graph
        roi = ee.Geometry.Point(-119.6, 37.4)
        self.add_basemap("SATELLITE")

        # num_legends = self.getLegendCount()

    
        # legend for population
        legend_dict = {
            'No population': '000000',
            'Low population':'6baed6',
            'High population': '2171b5',
            'Area with population affected by wildfires': 'ffffff',
            'Burn areas': 'ff0000'
        }
        self.centerObject(roi, 4)
        # try:
        #     self.add_legend(legend_title="Population and Burn Scars", legend_elements=legend_dict)
        # except Exception as e:
        #     print(f"Error adding legend: {e}")


        landcover2 = ee.ImageCollection("NOAA/DMSP-OLS/NIGHTTIME_LIGHTS") \
            .filter(ee.Filter.date('1992-01-01', '1992-12-31')).mean()
        curImage = landcover2.select('stable_lights')
        curImage = curImage.where(curImage.neq(1), 1)
        self.add_layer(curImage,{'palette':['white']},'Background')

        landcover = ee.ImageCollection('USGS/NLCD').select('landcover').filterDate("2016-01-01", "2017-01-01").first().clip(california_area)
        landcover3 = ee.ImageCollection("NOAA/DMSP-OLS/NIGHTTIME_LIGHTS") \
            .filter(ee.Filter.date('1992-01-01', '1992-12-31')).mean()
        curImage = landcover3.select('stable_lights').clip(california_area)
        curImage = curImage.updateMask(landcover.neq(11).And(landcover.neq(12)).And(landcover.neq(90)).And(landcover.neq(95)).And(landcover.neq(31)))
        curImage = curImage.where(curImage.neq(1), 1)
        self.add_layer(curImage,{'palette':['a4a7ab']},'California')


        # population
        worldPop2020 = ee.ImageCollection("WorldPop/GP/100m/pop")\
            .filterDate("2020-01-01", "2020-12-01").select('population').mean().clip(california_area)

        worldPop2020 = worldPop2020.updateMask(worldPop2020.gte(5))

        worldPop2001 = ee.ImageCollection("WorldPop/GP/100m/pop")\
            .filterDate("2001-01-01", "2002-12-01").select('population').mean().clip(california_area)

        worldPop2001 = worldPop2001.updateMask(worldPop2001.gte(5))

        pop_params2 = {
        'bands': ['population'],
        'min': 0.0,
        'max': 50.0,
        'palette': ['white','bdd7e7','6baed6','2171b5']
        }

       
        self.addLayer(pop2020, pop_params, "Population 2020", False)
        self.addLayer(burned2020, burned_params, 'Burned Area 2020', True, 0.6)
        self.addLayer(burnedpop2020, impacted_pop_params, "Area of Impacted Population 2020", False)
        self.addLayer(worldPop2020, pop_params2, "Impacted Population Overlay 2020", True)

        self.addLayer(pop2001, pop_params, "Population 2001", False)
        self.addLayer(burned2001, burned_params, 'Burned Area 2001', False, 0.6)
        self.addLayer(burnedpop2001, impacted_pop_params, "Area of Impacted Population 2001", False)
        self.addLayer(worldPop2001, pop_params2, "Impacted Population Overlay 2001", False)

        
        # self.centerObject(roi, 10)
        # self.addLayer(roi, {}, 'ROI')
        # try:
        #     self.add_legend(legend_title="Population and Burn Scars", legend_elements=legend_dict)
        # except Exception as e:
        #     print(f"Error adding legend: {e}")
        
        # time.sleep(20)
        # add_legend_after_overlay(self)     

@solara.component
def Page():
    with solara.Column(align="center"):
        markdown = """
            ## California Wildfire Analysis
            
            ### Estimated California Population Directly Affected by wildfire (2000 to 2020)

            **For this analysis we will be using [WorldPop Global Project Population Data: Estimated Residential Population per 100x100m Grid Square dataset](https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop#bands) for population, [MCD64A1.061 MODIS Burned Area Monthly Global 500m](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1) dataset for burn scars, 
            [TIGER: US Census States 2018](https://developers.google.com/earth-engine/datasets/catalog/TIGER_2018_States) for state geometry, [DMSP OLS: Nighttime Lights Time Series Version 4](https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS) and [USGS/NLCD](https://www.usgs.gov/centers/eros/science/national-land-cover-database) for land cover with geemap. First, we will compute the zonal statistics to identify the countries with the largest forest area, and then plot them. Here the base tree cover imagery is taken from 2000**
            """
        solara.Markdown(markdown)
    
    with solara.Column(align="center",style={"min-width": "500px"}):
            forest_barchart_image_url = "/static/public/wildfire_population.png"
            solara.Image(forest_barchart_image_url)
    
    with solara.Column(align="center"):
        markdown = """

            ### Population Density and wildfire burn area

            **Let's visualize the popluation with wildfire burn area**
            #### Population and Wildfire Color Coding
            - **No population**: `#000000` ![#000000](https://via.placeholder.com/15/000000/000000?text=+)
            - **Low population**: `#6baed6` ![#6baed6](https://via.placeholder.com/15/6baed6/000000?text=+)
            - **High population**: `#2171b5` ![#2171b5](https://via.placeholder.com/15/2171b5/000000?text=+)
            - **Area with population affected by wildfires**: `#ffffff` ![#ffffff](https://via.placeholder.com/15/ffffff/000000?text=+)
            - **Burn areas**: `#ff0000` ![#ff0000](https://via.placeholder.com/15/ff0000/000000?text=+)
            """
        solara.Markdown(markdown)
    
    with solara.Column(style={"min-width": "500px"}):
        Map.element(
            center=[35, -120],
            zoom=4.5,
            height="600px",
        )