add wildfire mapping in home page

pages/05_California_wildfire.py

@@ -0,0 +1,160 @@
+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)
+
+        
+        
+        # time.sleep(20)
+        # add_legend_after_overlay(self)     
+
+@solara.component
+def Page():
+    with solara.Column(align="center"):
+        markdown = """
+            ## California Wildfire Analysis
+            
+            ### Population Density and wildfire burn area
+
+            **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), [MCD64A1.061 MODIS Burned Area Monthly Global 500m (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1) dataset] 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**
+            #### 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=[40, -100],
+            zoom=4,
+            height="600px",
+        )
+
+
+