added description of each pages

pages/02_inspector.py

@@ -39,6 +39,15 @@ class Map(geemap.Map):
 
 @solara.component
 def Page():
+    with solara.Column(align="center"):
+            markdown = """
+                
+                ### Landsat Inspection
+
+                **Here, we are using [NASA SRTM Digital Elevation 30m](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003) for digital elevation and [LANDSAT_LE7_TOA_5YEAR](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_5YEAR) that contains 5 year composites from all Landsat 7 images in the specified composite period with [TIGER: US Census States 2018](https://developers.google.com/earth-engine/datasets/catalog/TIGER_2018_States) for state geometry clipping**
+                    """
+            solara.Markdown(markdown)
+
     with solara.Column(style={"min-width": "500px"}):
         Map.element(
             center=[40, -100],

pages/03_plotting.py

@@ -32,6 +32,14 @@ class Map(geemap.Map):
 
 @solara.component
 def Page():
+    with solara.Column(align="center"):
+            markdown = """
+                
+                ### Plotting Landsat and Hyperion
+
+                **Here, we are using [LANDSAT_LE7_TOA_5YEAR](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_5YEAR) that contains 5 year composites from all Landsat 7 images in the specified composite period and [EO1_HYPERION](https://developers.google.com/earth-engine/datasets/catalog/EO1_HYPERION) that produced high resolution hyperspectral imager producing 220 unique spectral channels**
+                    """
+            solara.Markdown(markdown)
     with solara.Column(style={"min-width": "500px"}):
         Map.element(
             center=[40, -100],

pages/04_LULC_split_map.py

@@ -47,13 +47,6 @@ class Map(geemap.Map):
 
 @solara.component
 def Page():
-        with solara.Column(style={"min-width": "500px"}):
-            Map.element(
-                center=[40, -100],
-                zoom=4,
-                height="600px",
-            )
-
         with solara.Column(align="center"):
             markdown = """
             ## Land cover change mapping using split-panel map
@@ -62,3 +55,10 @@ def Page():
             """
 
             solara.Markdown(markdown)
+            
+        with solara.Column(style={"min-width": "500px"}):
+            Map.element(
+                center=[40, -100],
+                zoom=4,
+                height="600px",
+            )

pages/05_California_wildfire.py

@@ -138,7 +138,8 @@ def Page():
             
             ### 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**
+            **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**
             #### 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=+)