Update app.py

app.py

@@ -209,6 +209,10 @@ if uploaded_file is not None:
         start_year = 2019
         end_year = 2023
 
+        # Create an empty resultant dataframe
+        columns = ['Date', 'Imagery', 'AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Ratio']
+        combined_df = pd.DataFrame(columns=columns)
+        
         max_ndvi_geoms = []
         max_ndvi_buffered_geoms = []
         for year in range(start_year, end_year+1):
@@ -220,9 +224,28 @@ if uploaded_file is not None:
             # Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
             collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_ddmm, end_ddmm)).select(['B4', 'B8'])
         
-            # Get Zonal NDVI based on collection and geometries (Original KML and Buffered KML)
+            # Get Zonal Max composite NDVI based on collection and geometries (Original KML and Buffered KML)
             max_ndvi_geoms.append(get_zonal_ndviYoY(collection.filterBounds(geom_ee_object), geom_ee_object))
             max_ndvi_buffered_geoms.append(get_zonal_ndviYoY(collection.filterBounds(buffered_ee_object), buffered_ee_object))
+
+            # Get Zonal NDVI
+            df_geom = get_zonal_ndvi(collection.filterBounds(geom_ee_object), geom_ee_object)
+            df_buffered_geom = get_zonal_ndvi(collection.filterBounds(buffered_ee_object), buffered_ee_object)
+
+            # Merge both Zonalstats and create resultant dataframe
+            resultant_df = pd.merge(df_geom, df_buffered_geom, on='Date', how='inner')
+            resultant_df = resultant_df.rename(columns={'NDVI_x': 'AvgNDVI_Inside', 'NDVI_y': 'Avg_NDVI_Buffer', 'Imagery_x': 'Imagery'})
+            resultant_df['Ratio'] = resultant_df['AvgNDVI_Inside'] / resultant_df['Avg_NDVI_Buffer']
+            resultant_df.drop(columns=['Imagery_y'], inplace=True)
+    
+            # Re-order the columns of the resultant dataframe
+            resultant_df = resultant_df[['Date', 'Imagery', 'AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Ratio']]
+
+            # Append to empty dataframe
+            combined_df = pd.concat([combined_df, resultant_df], ignore_index=True)
+
+        # Write the final table
+        st.write(combined_df)
         
         # Create a DataFrame for YoY profile
         yoy_df = pd.DataFrame({'Year': [str(i) for i in range(start_year, end_year+1)], 'NDVI_Inside': max_ndvi_geoms, 'NDVI_Buffer': max_ndvi_buffered_geoms})