225 lines
8.1 KiB
Python
225 lines
8.1 KiB
Python
import json
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import numpy as np
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import rasterio
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from rasterio.warp import transform as transform_coords
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from pathlib import Path
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from datetime import datetime
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RED_BAND = 3
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NIR_BAND = 4
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def _calculate_and_write_ndvi(input_file, output_file):
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with rasterio.open(input_file) as src:
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red = src.read(RED_BAND).astype(np.float32)
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nir = src.read(NIR_BAND).astype(np.float32)
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mask = (red > 0) & (nir > 0)
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ndvi = np.zeros_like(red, dtype=np.float32)
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ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
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profile = src.profile.copy()
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profile.update(
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{
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"count": 1,
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"dtype": "float32",
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"nodata": 0,
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"compress": "lzw",
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}
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)
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with rasterio.open(output_file, "w", **profile) as dst:
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dst.write(ndvi, 1)
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dst.set_band_description(1, "NDVI")
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def _get_ndvi_value(ndvi_file, site_position):
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try:
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with rasterio.open(ndvi_file) as src:
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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# Check if point is within bounds
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if not (
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src.bounds.left <= x[0] <= src.bounds.right
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and src.bounds.bottom <= y[0] <= src.bounds.top
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):
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return None # Point is outside raster bounds
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samples = list(src.sample([(x[0], y[0])]))
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if samples:
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value = float(samples[0][0])
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# Check if it's actually nodata (using raster's nodata value)
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if src.nodata is not None and value == src.nodata:
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return None # This is nodata, not a valid 0 value
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if np.isnan(value):
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return None # NaN is invalid
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# 0 is a valid NDVI value (no vegetation), so return it
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return value
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except Exception as e:
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print(f"Error sampling {ndvi_file.name}: {e}")
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pass
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return None
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def _create_timeseries_for_dir(output_dir, site_position, source_name):
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print(f"[NDVI-{source_name}] Creating timeseries.json...")
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timeseries = []
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for ndvi_file in sorted(output_dir.glob("*.geotiff")):
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filename = ndvi_file.name
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# Extract date from filename
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# Format examples:
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# - YYYYMMDD_ndvi.geotiff -> date is at [0]
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# - YYYYMMDD_0.geotiff -> date is at [0]
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# - composite_YYYYMMDD.geotiff -> date is at [1]
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parts = filename.replace(".geotiff", "").split("_")
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date_str = None
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# Try to find a date pattern (8 digits)
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for part in parts:
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if len(part) == 8 and part.isdigit():
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date_str = part
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break
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if date_str:
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try:
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date = datetime.strptime(date_str, "%Y%m%d").isoformat()
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except ValueError:
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date = date_str
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else:
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# Fallback: use first part (for old MSIL2A_ndvi.geotiff files)
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date_str = parts[0]
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date = date_str
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print(
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f"[NDVI-{source_name}] Warning: Could not extract date from {filename}, using '{date_str}'"
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)
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ndvi_value = _get_ndvi_value(ndvi_file, site_position)
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if ndvi_value is None:
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print(
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f"[NDVI-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
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)
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# Note: 0 is a valid NDVI value (no vegetation), so we keep it
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# The _get_ndvi_value function now properly distinguishes between
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# valid 0 values and nodata values
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timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
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timeseries.sort(key=lambda x: x["date"])
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timeseries_file = output_dir / "timeseries.json"
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with open(timeseries_file, "w") as f:
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json.dump(timeseries, f, indent=2)
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print(f"[NDVI-{source_name}] Saved: {timeseries_file} ({len(timeseries)} entries)")
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def _process_ndvi_files(
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input_dir, output_dir, source_name, pattern="*.geotiff", output_namer=None
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):
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output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[NDVI-{source_name}] Processing {input_dir}...")
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geotiff_files = sorted(input_dir.glob(pattern))
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if not geotiff_files:
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print(f"[NDVI-{source_name}] No files found")
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return
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for geotiff_file in geotiff_files:
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# Skip DIST_CLOUD files silently (single-band distance-to-clouds, not suitable for NDVI)
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if "DIST_CLOUD" in geotiff_file.name:
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continue
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# Check if file has enough bands (need at least 4 for RED and NIR)
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try:
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with rasterio.open(geotiff_file) as src:
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if src.count < 4:
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print(
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f"[NDVI-{source_name}] Skipping {geotiff_file.name} (only {src.count} band(s), need 4+)"
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)
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continue
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except Exception as e:
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print(
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f"[NDVI-{source_name}] Skipping {geotiff_file.name} (error reading: {e})"
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)
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continue
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output_file = output_dir / (
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output_namer(geotiff_file) if output_namer else geotiff_file.name
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)
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_calculate_and_write_ndvi(geotiff_file, output_file)
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print(f"[NDVI-{source_name}] Saved: {output_file}")
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def generate_ndvi_raw(season, site_position, site_name):
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{season}/raw/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
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_process_ndvi_files(input_dir, output_dir, source.upper())
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def create_ndvi_timeseries_raw(season, site_position, site_name):
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for source in ["s2", "s3"]:
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output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
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_create_timeseries_for_dir(output_dir, site_position, source.upper())
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def _get_output_name_prepared(geotiff_file):
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if geotiff_file.suffix == ".tif":
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if "REFL" in geotiff_file.stem:
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# For S2: S2A_MSIL2A_20240101_REFL -> date is at index [2]
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# For S3: composite_20240101.tif -> date is at index [1] after removing .tif
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parts = geotiff_file.stem.split("_")
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if len(parts) >= 3 and parts[0].startswith("S2"):
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# S2 format: S2A_MSIL2A_YYYYMMDD_REFL
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date_str = parts[2]
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elif len(parts) >= 2 and parts[0] == "composite":
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# S3 format: composite_YYYYMMDD
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date_str = parts[1]
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else:
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# Fallback: try index [1] for other formats
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date_str = parts[1] if len(parts) > 1 else parts[0]
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return f"{date_str}_ndvi.geotiff"
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return geotiff_file.name.replace(".tif", ".geotiff")
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return geotiff_file.name
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def _fusion_namer(f):
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date_str = f.stem.split("_")[1]
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return f"{date_str}_ndvi.geotiff"
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def generate_ndvi_prepared(season, site_position, site_name):
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{season}/prepared/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/prepared/ndvi/{source}/")
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for pattern in ["*.geotiff", "*.tif"]:
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_process_ndvi_files(
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input_dir,
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output_dir,
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f"PREPARED-{source.upper()}",
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pattern=pattern,
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output_namer=_get_output_name_prepared,
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)
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input_dir = Path(f"data/{site_name}/{season}/prepared/fusion/")
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output_dir = Path(f"data/{site_name}/{season}/prepared/ndvi/fusion/")
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_process_ndvi_files(
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input_dir,
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output_dir,
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"FUSION",
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pattern="REFL_*.tif",
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output_namer=_fusion_namer,
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)
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def create_ndvi_timeseries_prepared(season, site_position, site_name):
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for source in ["s2", "s3"]:
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output_dir = Path(f"data/{site_name}/{season}/prepared/ndvi/{source}/")
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_create_timeseries_for_dir(
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output_dir, site_position, f"PREPARED-{source.upper()}"
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)
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output_dir = Path(f"data/{site_name}/{season}/prepared/ndvi/fusion/")
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_create_timeseries_for_dir(output_dir, site_position, "FUSION")
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