upload version

Bhankhara Df 11 he 5 2020-21.kml

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+	<name>Bhankhara Df 11 he 5 2020-21</name>
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Bhankhara_Df_11_he_5_2020-21.geojson

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+"name": "Bhankhara Df 11 he 5 2020-21",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
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+]
+}

Modhari_he_10_2024-25_1.geojson

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+{
+"type": "FeatureCollection",
+"name": "Modhari he 10 2024-25",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
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+]
+}

app.py

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+import os
+import ee
+import geemap
+import json
+import geopandas as gpd
+import streamlit as st
+import pandas as pd
+from fastkml import kml
+import geojson
+
+ee.Initialize()
+
+def convert_3d_to_2d(geometry):
+    """
+    Recursively convert any 3D coordinates in a geometry to 2D.
+    """
+    if geometry.is_empty:
+        return geometry
+
+    if geometry.geom_type == 'Polygon':
+        return geojson.Polygon([[(x, y) for x, y, *_ in ring] for ring in geometry.coordinates])
+
+    elif geometry.geom_type == 'MultiPolygon':
+        return geojson.MultiPolygon([
+            [[(x, y) for x, y, *_ in ring] for ring in poly]
+            for poly in geometry.coordinates
+        ])
+
+    elif geometry.geom_type == 'LineString':
+        return geojson.LineString([(x, y) for x, y, *_ in geometry.coordinates])
+
+    elif geometry.geom_type == 'MultiLineString':
+        return geojson.MultiLineString([
+            [(x, y) for x, y, *_ in line]
+            for line in geometry.coordinates
+        ])
+
+    elif geometry.geom_type == 'Point':
+        x, y, *_ = geometry.coordinates
+        return geojson.Point((x, y))
+
+    elif geometry.geom_type == 'MultiPoint':
+        return geojson.MultiPoint([(x, y) for x, y, *_ in geometry.coordinates])
+
+    return geometry  # Return unchanged if not a supported geometry type
+
+def kml_to_geojson(kml_string):
+    k = kml.KML()
+    k.from_string(kml_string.encode('utf-8'))  # Convert the string to bytes
+    features = list(k.features())
+    
+    geojson_features = []
+    for feature in features:
+        geometry_2d = convert_3d_to_2d(feature.geometry)
+        geojson_features.append(geojson.Feature(geometry=geometry_2d))
+    
+    geojson_data = geojson.FeatureCollection(geojson_features)
+    return geojson_data
+
+def geojson_to_ee(geojson_data):
+  ee_object = geemap.geojson_to_ee(geojson_data)
+  return ee_object
+
+# put title in center
+st.markdown("""
+<style>
+h1 {
+    text-align: center;
+}
+</style>
+""", unsafe_allow_html=True)
+
+st.title("Mean NDVI Calculator")
+
+# get the start and end date from the user
+col = st.columns(2)
+start_date = col[0].date_input("Start Date", value=pd.to_datetime('2021-01-01'))
+end_date = col[1].date_input("End Date", value=pd.to_datetime('2021-01-30'))
+start_date = start_date.strftime("%Y-%m-%d")
+end_date = end_date.strftime("%Y-%m-%d")
+
+max_cloud_cover = st.number_input("Max Cloud Cover", value=20)
+
+# Get the geojson file from the user
+uploaded_file = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml"])
+
+# Read the KML file
+if uploaded_file is None:
+    file_name = "Bhankhara_Df_11_he_5_2020-21.geojson"
+    st.write(f"Using default file: {file_name}")
+    data = gpd.read_file(file_name)
+    with open(file_name) as f:
+      str_data = f.read()
+else:
+    st.write(f"Using uploaded file: {uploaded_file.name}")
+    file_name = uploaded_file.name
+    bytes_data = uploaded_file.getvalue()
+    str_data = bytes_data.decode("utf-8")
+
+
+if file_name.endswith(".geojson"):
+  geojson_data = json.loads(str_data)
+elif file_name.endswith(".kml"):
+  geojson_data = kml_to_geojson(str_data)
+  print(geojson_data)
+
+# Read Geojson File
+ee_object = geojson_to_ee(geojson_data)
+
+# Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
+collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterBounds(ee_object).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_date, end_date)).select(['B4', 'B8'])
+
+# Print Number of Images in collection
+# print("Number of images", collection.size().getInfo())
+st.write(f"Number of images: {collection.size().getInfo()}")
+
+# Calculate NDVI as Normalized Index
+def calculate_ndvi(image):
+  ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
+  return image.addBands(ndvi)
+
+collection = collection.map(calculate_ndvi)
+
+# Write Zonalstats into csv file
+# out_dir = os.path.join("Output")
+# out_NDVI_stats = os.path.join(out_dir, "tmp.csv")
+
+# if not os.path.exists(out_dir):
+#     os.makedirs(out_dir)
+
+geemap.zonal_stats(collection.select(["NDVI"]), ee_object, "tmp.csv", stat_type="mean", scale=10)
+
+# Show the table
+df = pd.read_csv("tmp.csv")
+df = df.T
+df = df.reset_index()
+df = df.iloc[:-2]
+df['index'] = pd.to_datetime(df['index'].apply(lambda x: x.split('_')[1].split('T')[0])).dt.strftime('%Y-%m-%d')
+df.rename(columns={'index': 'Date', 0: 'Mean NDVI'}, inplace=True)
+st.write(df)
+
+# plot the time series
+st.write("Time Series Plot")
+st.line_chart(df.set_index('Date'))
+
+st.write(f"Overall Mean NDVI: {df['Mean NDVI'].mean():.2f}")

credentials

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dezkvf.geojson

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+"name": "dezkvf",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "Name": "kelava 25 he", "Description": "<div><b>kelava irrigation he 25&nbsp;</b><br></div><div><b>Year 2022-23</b><br></div>" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 73.197914709471448, 24.019776408586271, 0.0 ], [ 73.195116560943902, 24.017540941492609, 0.0 ], [ 73.195520536704294, 24.017383406835211, 0.0 ], [ 73.194800334002622, 24.015236193127709, 0.0 ], [ 73.194636950732615, 24.014403118717031, 0.0 ], [ 73.194542860108086, 24.0139003560814, 0.0 ], [ 73.194244339216496, 24.0126807914708, 0.0 ], [ 73.194234194479606, 24.0126863557728, 0.0 ], [ 73.196535112752471, 24.012520238854709, 0.0 ], [ 73.196484322109839, 24.01217871972386, 0.0 ], [ 73.197648612812372, 24.011999301879619, 0.0 ], [ 73.198172869309701, 24.014332547414551, 0.0 ], [ 73.198681981415604, 24.01611200638915, 0.0 ], [ 73.199597451332266, 24.01895266153662, 0.0 ], [ 73.197914709471448, 24.019776408586271, 0.0 ] ] ] } },
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+{ "type": "Feature", "properties": { "Name": "Golvada he 10", "Description": "" }, "geometry": { "type": "Point", "coordinates": [ 73.262362465970142, 23.947286317747501, 348.096590272880121 ] } }
+]
+}

golvada he 10 2023-24.kml

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