Added Slope

app.py

@@ -13,7 +13,8 @@ from shapely import wkb
 from shapely.ops import transform
 import geemap.foliumap as geemapfolium
 from streamlit_folium import st_folium
-
+import datetime import datetime
+import numpy as np
 
 # Enable fiona driver
 fiona.drvsupport.supported_drivers['LIBKML'] = 'rw'
@@ -208,7 +209,7 @@ if uploaded_file is not None:
 
         ####### YoY Profile ########
         start_year = 2019
-        end_year = 2023
+        end_year = datetime.now().year
 
         # Create an empty resultant dataframe
         columns = ['Date', 'Imagery', 'AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Ratio']
@@ -216,35 +217,40 @@ if uploaded_file is not None:
         
         max_ndvi_geoms = []
         max_ndvi_buffered_geoms = []
+        years=[]
         for year in range(start_year, end_year+1):
+            try:
+                # Construct start and end dates for every year
+                start_ddmm = str(year)+pd.to_datetime(start_date).strftime("-%m-%d")
+                end_ddmm = str(year)+pd.to_datetime(end_date).strftime("-%m-%d")     
             
-            # Construct start and end dates for every year
-            start_ddmm = str(year)+pd.to_datetime(start_date).strftime("-%m-%d")
-            end_ddmm = str(year)+pd.to_datetime(end_date).strftime("-%m-%d")     
-            
-            # 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'])
+                # 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 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)
+                # 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))
+                years.append(year)
+
+                # 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']]
+                # 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)
+                # Append to empty dataframe
+                combined_df = pd.concat([combined_df, resultant_df], ignore_index=True)
+            except:
+                continue
 
+            
         # Write the final table
         st.write(combined_df)
 
@@ -253,12 +259,15 @@ if uploaded_file is not None:
         st.line_chart(combined_df[['AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Date']].set_index('Date'))
         
         # 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})
+        yoy_df = pd.DataFrame({'Year': years, 'NDVI_Inside': max_ndvi_geoms, 'NDVI_Buffer': max_ndvi_buffered_geoms})
         yoy_df['Ratio'] = yoy_df['NDVI_Inside'] / yoy_df['NDVI_Buffer']
+        slope, intercept = np.polyfit(yoy_df['Year'], yoy_df['NDVI_Inside'], 1)
 
         # plot the time series
         st.write("Year on Year Plot using Maximum NDVI Composite (computed for given duration)")
         st.line_chart(yoy_df[['NDVI_Inside', 'NDVI_Buffer', 'Ratio', 'Year']].set_index('Year'))
+        st.write("Slope (trend) and Intercept are {}, {} respectively. ".format(np.round(slope, 4), np.round(intercept, 4)))
+
         
         # Visualize map on ESRI basemap
         st.write("Map Visualization")