Update app.py

app.py

@@ -93,6 +93,10 @@ def reduce_zonal_ndvi(image, ee_object):
   )
   return image.set(reduced)
 
+def calculate_NDVI(image):
+    ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
+    return image.addBands(ndvi)
+
 # Get Zonal NDVI
 def get_zonal_ndvi(collection, geom_ee_object):
   reduced_collection = collection.map(lambda image: reduce_zonal_ndvi(image, ee_object=geom_ee_object))
@@ -102,6 +106,17 @@ def get_zonal_ndvi(collection, geom_ee_object):
   df = pd.DataFrame({'NDVI': stats_list, 'Date': dates, 'Imagery': filenames})
   return df
 
+def get_zonal_ndviYoY(collection, geom_ee_object):
+    collection = collection.map(lambda image: calculate_NDVI)
+    max_ndvi = collection.select('NDVI').max()
+    reduced_max_ndvi = image.reduceRegion(
+        reducer=ee.Reducer.mean(),
+        geometry=ee_object.geometry(),
+        scale=10,
+        maxPixels=1e12)
+    return reduced_max_ndvi
+    
+
 # put title in center
 st.markdown("""
 <style>
@@ -187,6 +202,26 @@ if uploaded_file is not None:
         st.write("Time Series Plot")
         st.line_chart(resultant_df[['AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Date']].set_index('Date'))
 
+        ####### YoY Profile ########
+        start_year = 2017
+        end_year = 2023
+
+        max_ndvi_geoms = []
+        max_ndvi_buffered_geoms = []
+        for year in range(start_year, end_year+1):
+            start_ddmm = start_date.strftime("%d%m") + str(year)
+            end_ddmm = end_date.strftime("%d%m") + str(year)
+            
+            # 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)
+            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))
+
+        st.write(max_ndvi_geoms)
+        st.write(max_ndvi_buffered_geoms)
+        
         # Visualize map on ESRI basemap
         st.write("Map Visualization")