Added ndvi calculation.

download.py

@@ -1,11 +0,0 @@
-from download_s2 import download_s2
-from download_s3 import download_s3
-
-year = 2024
-site_position = (47.116171, 11.320308)
-site_name = "innsbruck"
-
-print(f"Downloading data for {site_name}, {year}")
-download_s2(year, site_position, site_name)
-download_s3(year, site_position, site_name)
-print("All downloads completed")

ndvi.py

@@ -0,0 +1,97 @@
+import os
+import json
+import numpy as np
+import rasterio
+from rasterio.warp import transform as transform_coords
+from pathlib import Path
+from datetime import datetime
+
+
+def generate_ndvi(year, site_position, site_name):
+    for source in ["s2", "s3"]:
+        input_dir = Path(f"data/{site_name}/{year}/{source}/")
+        output_dir = Path(f"data/{site_name}/{year}/ndvi/{source}/")
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"[NDVI-{source.upper()}] Processing {input_dir}...")
+
+        geotiff_files = sorted(input_dir.glob("*.geotiff"))
+        if not geotiff_files:
+            print(f"[NDVI-{source.upper()}] No files found")
+            continue
+
+        for geotiff_file in geotiff_files:
+            output_file = output_dir / geotiff_file.name
+
+            if output_file.exists():
+                print(f"[NDVI-{source.upper()}] Skipping {geotiff_file.name} (exists)")
+                continue
+
+            with rasterio.open(geotiff_file) as src:
+                red = src.read(3).astype(np.float32)
+                nir = src.read(4).astype(np.float32)
+
+                mask = (red > 0) & (nir > 0)
+                ndvi = np.zeros_like(red, dtype=np.float32)
+                ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
+
+                profile = src.profile.copy()
+                profile.update(
+                    {
+                        "count": 1,
+                        "dtype": "float32",
+                        "nodata": 0,
+                        "compress": "lzw",
+                    }
+                )
+
+                with rasterio.open(output_file, "w", **profile) as dst:
+                    dst.write(ndvi, 1)
+                    dst.set_band_description(1, "NDVI")
+
+            print(f"[NDVI-{source.upper()}] Saved: {output_file}")
+        
+        print(f"[NDVI-{source.upper()}] Completed")
+
+
+def create_ndvi_timeseries(year, site_position, site_name):
+    for source in ["s2", "s3"]:
+        output_dir = Path(f"data/{site_name}/{year}/ndvi/{source}/")
+        
+        print(f"[NDVI-{source.upper()}] Creating timeseries.json...")
+        timeseries = []
+        
+        ndvi_files = sorted(output_dir.glob("*.geotiff"))
+        for ndvi_file in ndvi_files:
+            filename = ndvi_file.name
+            date_str = filename.split("_")[0]
+            try:
+                date = datetime.strptime(date_str, "%Y%m%d").isoformat()
+            except ValueError:
+                date = date_str
+            
+            ndvi_value = None
+            try:
+                with rasterio.open(ndvi_file) as src:
+                    lon, lat = site_position[1], site_position[0]
+                    x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+                    samples = list(src.sample([(x[0], y[0])]))
+                    if samples and len(samples) > 0:
+                        value = float(samples[0][0])
+                        if value != 0 and not np.isnan(value):
+                            ndvi_value = value
+            except Exception as e:
+                print(f"[NDVI-{source.upper()}] Warning: Could not sample {filename}: {e}")
+            
+            timeseries.append({
+                "date": date,
+                "filename": filename,
+                "ndvi": ndvi_value
+            })
+        
+        timeseries.sort(key=lambda x: x["date"])
+        timeseries_file = output_dir / "timeseries.json"
+        with open(timeseries_file, "w") as f:
+            json.dump(timeseries, f, indent=2)
+        
+        print(f"[NDVI-{source.upper()}] Saved: {timeseries_file} ({len(timeseries)} entries)")

run.py

@@ -0,0 +1,17 @@
+from download_s2 import download_s2
+from download_s3 import download_s3
+from ndvi import generate_ndvi, create_ndvi_timeseries
+
+year = 2024
+site_position = (47.116171, 11.320308)
+site_name = "innsbruck"
+
+# print(f"Downloading data for {site_name}, {year}")
+# download_s2(year, site_position, site_name)
+# download_s3(year, site_position, site_name)
+# print("All downloads completed")
+
+print(f"Generating NDVI for {site_name}, {year}")
+# generate_ndvi(year, site_position, site_name)
+create_ndvi_timeseries(year, site_position, site_name)
+print("All NDVI generation completed")