added gif file to lfs

pages/05_California_wildfire.py

@@ -126,6 +126,12 @@ class Map(geemap.Map):
         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)     
@@ -136,24 +142,36 @@ def Page():
         markdown = """
             ## California Wildfire Analysis
             
-            ### Population Density and wildfire burn area
+            ### 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=[40, -100],
-            zoom=4,
+            center=[35, -120],
+            zoom=4.5,
             height="600px",
         )