increase index

app.py

@@ -277,12 +277,16 @@ def get_dem_slope_maps(ee_geometry, map_bounds, wayback_url, wayback_title, zoom
 
     return dem_map_html, slope_map_html
 
-def add_indices(image, nir_band, red_band, blue_band, green_band, evi_vars):
+def add_indices(image, nir_band, red_band, blue_band, green_band, swir_band, swir2_band, evi_vars):
     """Calculates and adds multiple vegetation indices to an Earth Engine image."""
     nir = image.select(nir_band).divide(10000)
     red = image.select(red_band).divide(10000)
     blue = image.select(blue_band).divide(10000)
     green = image.select(green_band).divide(10000)
+    swir = image.select(swir_band).divide(10000)
+    swir2 = image.select(swir2_band).divide(10000)
+
+    # Previously existing indices
     ndvi = image.normalizedDifference([nir_band, red_band]).rename('NDVI')
     evi = image.expression(
         'G * ((NIR - RED) / (NIR + C1 * RED - C2 * BLUE + L))', {
@@ -312,7 +316,21 @@ def add_indices(image, nir_band, red_band, blue_band, green_band, evi_vars):
         '0.5 * (NIR - RED) / (NIR + 6 * RED - 8.25 * BLUE - 0.01)', {
             'NIR': nir, 'RED': red, 'BLUE': blue
         }).rename('GujVDI')
-    return image.addBands([ndvi, evi, evi2, rf, ci, gujvdi])
+    mndwi = image.normalizedDifference([green_band, swir_band]).rename('MNDWI')
+
+    # Newly added indices
+    savi = image.expression('(1 + L) * (NIR - RED) / (NIR + RED + L)', {
+        'NIR': nir, 'RED': red, 'L': 0.5
+    }).rename('SAVI')
+    mvi = image.expression('(NIR - (GREEN + SWIR)) / (NIR + (GREEN + SWIR))', {
+        'NIR': nir, 'GREEN': green, 'SWIR': swir
+    }).rename('MVI')
+    nbr = image.normalizedDifference([nir_band, swir2_band]).rename('NBR')
+    gci = image.expression('(NIR - GREEN) / GREEN', {
+        'NIR': nir, 'GREEN': green
+    }).rename('GCI')
+
+    return image.addBands([ndvi, evi, evi2, rf, ci, gujvdi, mndwi, savi, mvi, nbr, gci])
 
 
 # --- Gradio App Logic ---
@@ -380,49 +398,6 @@ def process_and_display(file_obj, url_str, buffer_m, progress=gr.Progress()):
     progress(1, desc="Done!")
     return m._repr_html_(), None, stats_df, dem_html, slope_html, geometry_json, buffer_geometry_json
 
-
-@app.get("/api/geometry1")
-def calculate_geometry_metrics(file_url: str):
-    """
-    Accepts a URL to a KML/GeoJSON file, calculates the area and 
-    perimeter of the first valid polygon, and returns the results.
-    """
-    try:
-
-        decoded_url = unquote(file_url)
-        input_gdf = get_gdf_from_url(decoded_url)
-        if input_gdf is None or input_gdf.empty:
-            raise ValueError("Could not read geometry from the provided URL.")
-        input_gdf = preprocess_gdf(input_gdf)
-        geometry_gdf = next((input_gdf.iloc[[i]] for i in range(len(input_gdf)) if is_valid_polygon(input_gdf.iloc[[i]])), None)
-        
-        if geometry_gdf is None:
-            raise ValueError("No valid polygon found in the provided file.")
-        projected_gdf = to_best_crs(geometry_gdf)
-        
-        # Calculate area (in hectares) and perimeter (in meters)
-        area_hectares = projected_gdf.area.item() / 10000
-        perimeter_meters = projected_gdf.length.item()
-
-        # reproject to WGS84 (EPSG:4326)
-        centroid_gdf = projected_gdf.to_crs(epsg=4326)
-        centroid_point = centroid_gdf.centroid.item()
-        
-        return {
-            "area_hectares": round(area_hectares, 4),
-            "perimeter_meters": round(perimeter_meters, 4),
-            "latitude": round(centroid_point.y, 4),
-            "longitude": round(centroid_point.x, 4)
-        }
-        
-    except ValueError as e:
-        # Handle specific errors like bad URLs, no polygons, etc. with a 400 Bad Request
-        raise HTTPException(status_code=400, detail=str(e))
-    except Exception as e:
-        # Handle any other unexpected errors with a 500 Internal Server Error
-        print(f"An unexpected error occurred in /api/geometry: {e}")
-        raise HTTPException(status_code=500, detail="An unexpected server error occurred.")
-
 @app.get("/api/geometry")
 def calculate_geometry_metrics(file_url: str):
     """
@@ -494,10 +469,10 @@ def calculate_indices(
         collection = (
             ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
             .select(
-                ["B2", "B3", "B4", "B8", "MSK_CLDPRB"],
-                ["Blue", "Green", "Red", "NIR", "MSK_CLDPRB"]
+                ["B2", "B3", "B4", "B8", "B11", "B12", "MSK_CLDPRB"],
+                ["Blue", "Green", "Red", "NIR", "SWIR", "SWIR2", "MSK_CLDPRB"]
             )
-            .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', evi_vars))
+            .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', 'SWIR', 'SWIR2', evi_vars))
         )
 
         result_rows = []
@@ -614,8 +589,11 @@ def generate_comparison_maps(geometry_json, selected_index, selected_years, evi_
             ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
             .filterDate(start_date, end_date)
             .filterBounds(ee_geometry)
-            .select(["B2", "B3", "B4", "B8"], ["Blue", "Green", "Red", "NIR"])
-            .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', evi_vars))
+            .select(
+                ["B2", "B3", "B4", "B8", "B11", "B12", "MSK_CLDPRB"],
+                ["Blue", "Green", "Red", "NIR", "SWIR", "SWIR2", "MSK_CLDPRB"]
+            )
+            .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', 'SWIR', 'SWIR2', evi_vars))
         )
 
         if collection.size().getInfo() == 0:
@@ -721,7 +699,7 @@ with gr.Blocks(theme=theme, title="Kamlan: KML Analyzer") as demo:
 
             with gr.Accordion("Advanced Settings", open=False):
                 gr.Markdown("### Select Vegetation Indices")
-                all_veg_indices = ["GujVDI", "NDVI", "EVI", "EVI2", "RandomForest", "CI"]
+                all_veg_indices = ["GujVDI", "NDVI", "EVI", "EVI2", "RandomForest", "CI", "MNDWI", "SAVI", "MVI", "NBR", "GCI"]
                 veg_indices_checkboxes = gr.CheckboxGroup(all_veg_indices, label="Indices", value=["NDVI"])
 
                 gr.Markdown("### EVI/EVI2 Parameters")
@@ -873,8 +851,11 @@ def compute_index_histogram(file_url: str, index_name: str, start_date: str, end
     collection = (
         ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
         .filterDate(start_date, end_date)
-        .select(["B2", "B3", "B4", "B8"], ["Blue", "Green", "Red", "NIR"])
-        .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', evi_vars))
+        .select(
+            ["B2", "B3", "B4", "B8", "B11", "B12", "MSK_CLDPRB"],
+            ["Blue", "Green", "Red", "NIR", "SWIR", "SWIR2", "MSK_CLDPRB"]
+        )
+        .map(lambda img: add_indices(img, 'NIR', 'Red', 'Blue', 'Green', 'SWIR', 'SWIR2', evi_vars))
         .filterBounds(ee_geometry)
     )
 
@@ -921,7 +902,7 @@ def compute_index_histogram(file_url: str, index_name: str, start_date: str, end
     return Response(content=final_string, media_type="text/csv")
 
 # Register endpoints for multiple indices with date range
-for idx in ["NDVI", "EVI", "EVI2", "RandomForest", "CI", "GujVDI"]:
+for idx in ["NDVI", "EVI", "EVI2", "RandomForest", "CI", "GujVDI", "MNDWI", "SAVI", "MVI", "NBR", "GCI"]:
     endpoint_path = f"/api/{idx}"
 
     async def index_hist_endpoint(file_url: str, start_date: str, end_date: str, index_name=idx):