Renaming.
This commit is contained in:
Felix Delattre
parent
f9da4aef7d
commit
3919b8e871
12 changed files with 953 additions and 203 deletions
|
|
@ -1,3 +1,4 @@
|
|||
"""PhenoCam acquisition from PhenoCam Network API."""
|
||||
import csv
|
||||
import json
|
||||
import requests
|
||||
|
|
@ -13,7 +14,7 @@ def _find_start_offset(site_name, start_dt, total_count):
|
|||
"""Binary search to find approximate offset for start date."""
|
||||
low, high = 0, total_count - 1
|
||||
limit = 1
|
||||
|
||||
|
||||
for _ in range(15):
|
||||
mid = (low + high) // 2
|
||||
response = requests.get(
|
||||
|
|
@ -25,11 +26,11 @@ def _find_start_offset(site_name, start_dt, total_count):
|
|||
results = response.json().get("results", [])
|
||||
if not results:
|
||||
break
|
||||
|
||||
|
||||
mid_date_str = results[0].get("imgdate", "")
|
||||
if not mid_date_str:
|
||||
break
|
||||
|
||||
|
||||
try:
|
||||
mid_date = datetime.strptime(mid_date_str, "%Y-%m-%d")
|
||||
if mid_date < start_dt:
|
||||
|
|
@ -38,7 +39,7 @@ def _find_start_offset(site_name, start_dt, total_count):
|
|||
high = mid
|
||||
except ValueError:
|
||||
break
|
||||
|
||||
|
||||
return max(0, low - 100)
|
||||
|
||||
|
||||
|
|
@ -62,32 +63,32 @@ def download_phenocam(season, site_position, site_name, date_range=None):
|
|||
)
|
||||
response.raise_for_status()
|
||||
total_count = response.json().get("count", 0)
|
||||
|
||||
|
||||
if total_count == 0:
|
||||
print(f"[PhenoCam] No images found for site '{site_name}'")
|
||||
return
|
||||
|
||||
|
||||
print(f"[PhenoCam] Found {total_count} total images, estimating start offset...")
|
||||
start_offset = _find_start_offset(site_name, start_dt, total_count)
|
||||
|
||||
|
||||
url = f"{PHENOCAM_API}/middayimages/"
|
||||
params = {"site": site_name, "offset": start_offset}
|
||||
|
||||
|
||||
print(f"[PhenoCam] Fetching image list from offset {start_offset}...")
|
||||
images = []
|
||||
page = 1
|
||||
max_pages = 500
|
||||
past_end_date = False
|
||||
|
||||
|
||||
while url and page <= max_pages and not past_end_date:
|
||||
response = requests.get(url, params=params, timeout=30)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
results = data.get("results", [])
|
||||
|
||||
|
||||
if not results:
|
||||
break
|
||||
|
||||
|
||||
for img in results:
|
||||
img_date_str = img.get("imgdate", "")
|
||||
if not img_date_str:
|
||||
|
|
@ -101,7 +102,7 @@ def download_phenocam(season, site_position, site_name, date_range=None):
|
|||
images.append(img)
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
|
||||
if url and not past_end_date:
|
||||
url = data.get("next")
|
||||
params = None
|
||||
|
|
@ -120,15 +121,15 @@ def download_phenocam(season, site_position, site_name, date_range=None):
|
|||
date_str = img.get("imgdate", "").replace("-", "")
|
||||
if not date_str:
|
||||
return None
|
||||
|
||||
|
||||
filepath = output_dir / f"{date_str}.jpg"
|
||||
if filepath.exists():
|
||||
return f"Skipped {date_str}.jpg (exists)"
|
||||
|
||||
|
||||
img_path = img.get("imgpath")
|
||||
if not img_path:
|
||||
return None
|
||||
|
||||
|
||||
img_url = f"https://phenocam.nau.edu{img_path}"
|
||||
try:
|
||||
img_response = requests.get(img_url, timeout=30)
|
||||
|
|
@ -153,13 +154,13 @@ def download_phenocam_greenness(season, site_position, site_name, date_range=Non
|
|||
datetime_range = date_range or f"{season}-01-01/{season}-12-31"
|
||||
output_file = Path(f"data/{site_name}/{season}/raw/phenocam/timeseries.json")
|
||||
output_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
start_date, end_date = datetime_range.split("/")
|
||||
start_dt = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
end_dt = datetime.strptime(end_date, "%Y-%m-%d")
|
||||
|
||||
|
||||
print(f"[PhenoCam-GI] Fetching greenness-index time series: {site_name}, {season}")
|
||||
|
||||
|
||||
# Get ROIs for site (paginate through results)
|
||||
try:
|
||||
url = f"{PHENOCAM_API}/roilists/"
|
||||
|
|
@ -184,7 +185,7 @@ def download_phenocam_greenness(season, site_position, site_name, date_range=Non
|
|||
except requests.exceptions.RequestException as e:
|
||||
print(f"[PhenoCam-GI] Error fetching ROIs: {e}")
|
||||
return
|
||||
|
||||
|
||||
# Fetch CSV data
|
||||
try:
|
||||
csv_r = requests.get(csv_url, timeout=30)
|
||||
|
|
@ -207,10 +208,9 @@ def download_phenocam_greenness(season, site_position, site_name, date_range=Non
|
|||
except requests.exceptions.RequestException as e:
|
||||
print(f"[PhenoCam-GI] Error fetching CSV: {e}")
|
||||
return
|
||||
|
||||
|
||||
timeseries.sort(key=lambda x: x["date"])
|
||||
with open(output_file, "w") as f:
|
||||
json.dump(timeseries, f, indent=2)
|
||||
|
||||
print(f"[PhenoCam-GI] Saved: {output_file} ({len(timeseries)} entries)")
|
||||
|
||||
print(f"[PhenoCam-GI] Saved: {output_file} ({len(timeseries)} entries)")
|
||||
|
|
@ -1,12 +1,16 @@
|
|||
"""Sentinel-2-MSI acquisition from AWS Element84 Earth Search (STAC catalog)."""
|
||||
import numpy as np
|
||||
import rasterio
|
||||
import xml.etree.ElementTree as ET
|
||||
import requests
|
||||
from pathlib import Path
|
||||
from rasterio.warp import transform_geom
|
||||
from rasterio.crs import CRS
|
||||
from rasterio.warp import Resampling, calculate_default_transform, reproject, transform_geom
|
||||
from rasterio.windows import from_bounds, transform as window_transform
|
||||
from pystac_client import Client
|
||||
|
||||
BBOX_SIZE = 0.011
|
||||
BBOX_SIZE = 0.016
|
||||
TARGET_CRS = CRS.from_epsg(32632)
|
||||
|
||||
|
||||
def _get_bbox(lon, lat):
|
||||
|
|
@ -128,10 +132,41 @@ def download_s2(season, site_position, site_name, date_range=None):
|
|||
band_data[band_idx] = data[0]
|
||||
|
||||
if profile and len(band_data) == len(bands):
|
||||
stacked = [band_data[i] for i in sorted(band_data.keys())]
|
||||
stacked = np.array([band_data[i] for i in sorted(band_data.keys())])
|
||||
band_names = [list(bands.keys())[i] for i in sorted(band_data.keys())]
|
||||
viewing_angle = _extract_viewing_angle(item)
|
||||
|
||||
if profile["crs"] != TARGET_CRS:
|
||||
src_transform = profile["transform"]
|
||||
src_height, src_width = profile["height"], profile["width"]
|
||||
left, bottom, right, top = rasterio.transform.array_bounds(
|
||||
src_height, src_width, src_transform
|
||||
)
|
||||
dst_transform, dst_width, dst_height = calculate_default_transform(
|
||||
profile["crs"], TARGET_CRS, src_width, src_height,
|
||||
left=left, bottom=bottom, right=right, top=top,
|
||||
)
|
||||
reprojected = np.empty(
|
||||
(len(stacked), dst_height, dst_width), dtype=stacked.dtype
|
||||
)
|
||||
for i in range(len(stacked)):
|
||||
reproject(
|
||||
source=stacked[i],
|
||||
destination=reprojected[i],
|
||||
src_transform=src_transform,
|
||||
src_crs=profile["crs"],
|
||||
dst_transform=dst_transform,
|
||||
dst_crs=TARGET_CRS,
|
||||
resampling=Resampling.bilinear,
|
||||
)
|
||||
stacked = reprojected
|
||||
profile.update({
|
||||
"crs": TARGET_CRS,
|
||||
"transform": dst_transform,
|
||||
"width": dst_width,
|
||||
"height": dst_height,
|
||||
})
|
||||
|
||||
with rasterio.open(filepath, "w", **profile) as dst:
|
||||
for i, data in enumerate(stacked, 1):
|
||||
dst.write(data, i)
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
"""Sentinel-3-OLCI acquisition from Copernicus Data Space OpenEO API."""
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
76
fusion.py
Normal file
76
fusion.py
Normal file
|
|
@ -0,0 +1,76 @@
|
|||
"""EFAST fusion: S2/S3 reflectance fusion for four scenarios."""
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from preselection import detect_clouds
|
||||
from preparation import (
|
||||
prepare_s2,
|
||||
prepare_s3,
|
||||
_get_base_dir,
|
||||
RESOLUTION_RATIO,
|
||||
)
|
||||
|
||||
|
||||
def _import_efast():
|
||||
"""Lazy import of efast to avoid import errors when not using efast functions."""
|
||||
try:
|
||||
import efast
|
||||
return efast
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"efast package not found. Install with: pip install git+https://github.com/DHI-GRAS/efast.git"
|
||||
)
|
||||
|
||||
|
||||
def run_efast(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None, date_range=None):
|
||||
lat, lon = site_position
|
||||
datetime_range = date_range or f"{season}-01-01/{season}-12-31"
|
||||
|
||||
efast_base_dir = _get_base_dir(season, site_name, cleaning_strategy)
|
||||
s2_output_dir = efast_base_dir / "s2"
|
||||
s3_output_dir = efast_base_dir / "s3"
|
||||
fusion_output_dir = efast_base_dir / (f"fusion_sigma{sigma}" if sigma else "fusion")
|
||||
|
||||
fusion_output_dir.mkdir(parents=True, exist_ok=True)
|
||||
print(f"[EFAST] Starting fusion: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
|
||||
|
||||
efast = _import_efast()
|
||||
|
||||
start_str, end_str = datetime_range.split("/")
|
||||
start_date = datetime.strptime(start_str, "%Y-%m-%d")
|
||||
end_date = datetime.strptime(end_str, "%Y-%m-%d")
|
||||
|
||||
current_date = start_date
|
||||
while current_date <= end_date:
|
||||
date_str = current_date.strftime("%Y%m%d")
|
||||
output_file = fusion_output_dir / f"REFL_{date_str}.tif"
|
||||
try:
|
||||
kwargs = {
|
||||
"product": "REFL",
|
||||
"max_days": 30,
|
||||
"date_position": 2,
|
||||
"minimum_acquisition_importance": 0.0,
|
||||
"ratio": RESOLUTION_RATIO,
|
||||
}
|
||||
if sigma is not None:
|
||||
kwargs["sigma"] = sigma
|
||||
efast.fusion(current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs)
|
||||
print(
|
||||
f"[EFAST] Saved: {output_file}"
|
||||
if output_file.exists()
|
||||
else f"[EFAST] No output for {date_str} (insufficient nearby data)"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[EFAST] Error processing {date_str}: {e}")
|
||||
current_date += timedelta(days=1)
|
||||
|
||||
print("[EFAST] Completed")
|
||||
|
||||
|
||||
def run_all_efast_scenarios(season, site_position, site_name, sigma_value=30, date_range=None):
|
||||
for strategy in ["aggressive", "nonaggressive"]:
|
||||
detect_clouds(season, site_name, cleaning_strategy=strategy)
|
||||
prepare_s2(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
|
||||
prepare_s3(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
|
||||
run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=None, date_range=date_range)
|
||||
run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma_value, date_range=date_range)
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
"""Index generation: NDVI and GCC from S2/S3/fusion GeoTIFFs."""
|
||||
import json
|
||||
import numpy as np
|
||||
import rasterio
|
||||
|
|
@ -70,37 +71,37 @@ def _get_ndvi_from_original(input_file, site_position):
|
|||
with rasterio.open(input_file) as src:
|
||||
if src.count < 4:
|
||||
return None
|
||||
|
||||
|
||||
red = src.read(RED_BAND).astype(np.float32)
|
||||
nir = src.read(NIR_BAND).astype(np.float32)
|
||||
|
||||
|
||||
lon, lat = site_position[1], site_position[0]
|
||||
x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
|
||||
|
||||
|
||||
if not (
|
||||
src.bounds.left <= x[0] <= src.bounds.right
|
||||
and src.bounds.bottom <= y[0] <= src.bounds.top
|
||||
):
|
||||
return None
|
||||
|
||||
|
||||
row, col = src.index(x[0], y[0])
|
||||
if row < 0 or row >= src.height or col < 0 or col >= src.width:
|
||||
return None
|
||||
|
||||
|
||||
# Extract 3x3 window with boundary handling
|
||||
r0, r1 = max(0, row - 1), min(src.height, row + 2)
|
||||
c0, c1 = max(0, col - 1), min(src.width, col + 2)
|
||||
red_window = red[r0:r1, c0:c1]
|
||||
nir_window = nir[r0:r1, c0:c1]
|
||||
|
||||
|
||||
# Calculate NDVI for each pixel in window
|
||||
mask = (red_window > 0) & (nir_window > 0) & ~np.isnan(red_window) & ~np.isnan(nir_window)
|
||||
if not np.any(mask):
|
||||
return None
|
||||
|
||||
|
||||
ndvi_window = np.zeros_like(red_window, dtype=np.float32)
|
||||
ndvi_window[mask] = (nir_window[mask] - red_window[mask]) / (nir_window[mask] + red_window[mask])
|
||||
|
||||
|
||||
# Return mean of valid NDVI values
|
||||
valid_ndvi = ndvi_window[mask]
|
||||
return float(np.mean(valid_ndvi)) if len(valid_ndvi) > 0 else None
|
||||
|
|
@ -115,7 +116,7 @@ def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name
|
|||
for input_file in sorted(input_dir.glob(pattern)):
|
||||
if "DIST_CLOUD" in input_file.name:
|
||||
continue
|
||||
|
||||
|
||||
filename = input_file.name
|
||||
parts = filename.replace(".geotiff", "").split("_")
|
||||
date_str = None
|
||||
|
|
@ -307,31 +308,31 @@ def _get_gcc_from_original(input_file, site_position):
|
|||
with rasterio.open(input_file) as src:
|
||||
if src.count < 3:
|
||||
return None
|
||||
|
||||
|
||||
blue = src.read(BLUE_BAND).astype(np.float32)
|
||||
green = src.read(GREEN_BAND).astype(np.float32)
|
||||
red = src.read(RED_BAND).astype(np.float32)
|
||||
|
||||
|
||||
lon, lat = site_position[1], site_position[0]
|
||||
x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
|
||||
|
||||
|
||||
if not (
|
||||
src.bounds.left <= x[0] <= src.bounds.right
|
||||
and src.bounds.bottom <= y[0] <= src.bounds.top
|
||||
):
|
||||
return None
|
||||
|
||||
|
||||
row, col = src.index(x[0], y[0])
|
||||
if row < 0 or row >= src.height or col < 0 or col >= src.width:
|
||||
return None
|
||||
|
||||
|
||||
# Extract 3x3 window with boundary handling
|
||||
r0, r1 = max(0, row - 1), min(src.height, row + 2)
|
||||
c0, c1 = max(0, col - 1), min(src.width, col + 2)
|
||||
blue_window = blue[r0:r1, c0:c1]
|
||||
green_window = green[r0:r1, c0:c1]
|
||||
red_window = red[r0:r1, c0:c1]
|
||||
|
||||
|
||||
# Calculate GCC for each pixel in window
|
||||
total = red_window + green_window + blue_window
|
||||
mask = (total > 0) & ~np.isnan(total) & (blue_window >= 0) & (green_window >= 0) & (red_window >= 0)
|
||||
|
|
@ -340,10 +341,10 @@ def _get_gcc_from_original(input_file, site_position):
|
|||
if negative_pixels > 0:
|
||||
print(f"Warning: {input_file.name} excluded - all pixels have negative band values ({negative_pixels} negative pixels in window)")
|
||||
return None
|
||||
|
||||
|
||||
gcc_window = np.zeros_like(green_window, dtype=np.float32)
|
||||
gcc_window[mask] = green_window[mask] / total[mask]
|
||||
|
||||
|
||||
# Return mean of valid GCC values
|
||||
valid_gcc = gcc_window[mask]
|
||||
return float(np.mean(valid_gcc)) if len(valid_gcc) > 0 else None
|
||||
|
|
@ -358,7 +359,7 @@ def _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, source_
|
|||
for input_file in sorted(input_dir.glob(pattern)):
|
||||
if "DIST_CLOUD" in input_file.name:
|
||||
continue
|
||||
|
||||
|
||||
filename = input_file.name
|
||||
parts = filename.replace(".geotiff", "").split("_")
|
||||
date_str = None
|
||||
|
|
@ -451,3 +452,60 @@ def create_gcc_timeseries_post_process(season, site_position, site_name):
|
|||
input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/fusion/")
|
||||
output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/gcc/fusion/")
|
||||
_create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, f"POST-PROCESS-FUSION-{strategy}-σ{sigma}")
|
||||
|
||||
|
||||
def _get_bands_from_original(input_file, site_position):
|
||||
"""Extract mean B02, B03, B04, B8A from 3x3 window at site. Returns dict or None."""
|
||||
try:
|
||||
with rasterio.open(input_file) as src:
|
||||
if src.count < 4:
|
||||
return None
|
||||
lon, lat = site_position[1], site_position[0]
|
||||
x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
|
||||
if not (
|
||||
src.bounds.left <= x[0] <= src.bounds.right
|
||||
and src.bounds.bottom <= y[0] <= src.bounds.top
|
||||
):
|
||||
return None
|
||||
row, col = src.index(x[0], y[0])
|
||||
r0, r1 = max(0, row - 1), min(src.height, row + 2)
|
||||
c0, c1 = max(0, col - 1), min(src.width, col + 2)
|
||||
bands = [src.read(i + 1, window=((r0, r1), (c0, c1))).astype(np.float32) for i in range(4)]
|
||||
mask = ~np.any([np.isnan(b) for b in bands], axis=0)
|
||||
mask &= np.all([b > 0 for b in bands], axis=0)
|
||||
if not np.any(mask):
|
||||
return None
|
||||
return {
|
||||
"b02": float(np.mean(bands[0][mask])),
|
||||
"b03": float(np.mean(bands[1][mask])),
|
||||
"b04": float(np.mean(bands[2][mask])),
|
||||
"b8a": float(np.mean(bands[3][mask])),
|
||||
}
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _create_s2_bands_timeseries_for_dir(input_dir, output_dir, site_position):
|
||||
print(f"[S2-BANDS] Creating timeseries.json...")
|
||||
timeseries = []
|
||||
for f in sorted(input_dir.glob("*.geotiff")):
|
||||
date_str = f.stem.split("_")[0]
|
||||
if len(date_str) != 8 or not date_str.isdigit():
|
||||
continue
|
||||
date = datetime.strptime(date_str, "%Y%m%d").isoformat()
|
||||
bands = _get_bands_from_original(f, site_position)
|
||||
timeseries.append({"date": date, "filename": f.name, **(bands or {})})
|
||||
timeseries.sort(key=lambda x: x["date"])
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
(output_dir / "timeseries.json").write_text(json.dumps(timeseries, indent=2))
|
||||
print(f"[S2-BANDS] Saved: {output_dir / 'timeseries.json'} ({len(timeseries)} entries)")
|
||||
|
||||
|
||||
def create_s2_bands_timeseries_post_process(season, site_position, site_name):
|
||||
for strategy in ["aggressive", "nonaggressive"]:
|
||||
for sigma in [20, 30]:
|
||||
processed_dir = f"processed_{strategy}_sigma{sigma}"
|
||||
input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/s2/")
|
||||
output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/s2_bands/")
|
||||
if input_dir.exists():
|
||||
_create_s2_bands_timeseries_for_dir(input_dir, output_dir, site_position)
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
"""Calculate metrics comparing fusion-derived GCC with phenocam GCC ground truth."""
|
||||
"""Metrics and statistics: temporal/spatial metrics and PhenoCam stats."""
|
||||
import json
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
|
@ -7,7 +7,7 @@ from scipy.stats import pearsonr
|
|||
import rasterio
|
||||
from rasterio.warp import transform as transform_coords
|
||||
|
||||
from generate_indexes import BLUE_BAND, GREEN_BAND, RED_BAND
|
||||
from metrics_indices import BLUE_BAND, GREEN_BAND, RED_BAND
|
||||
|
||||
|
||||
def load_timeseries(filepath):
|
||||
|
|
@ -25,7 +25,7 @@ def match_dates(fusion_ts, phenocam_ts):
|
|||
fusion_vals = []
|
||||
phenocam_vals = []
|
||||
dates = []
|
||||
|
||||
|
||||
for date in sorted(common_dates):
|
||||
fusion_val = fusion_ts[date]
|
||||
phenocam_val = phenocam_ts[date]
|
||||
|
|
@ -33,7 +33,7 @@ def match_dates(fusion_ts, phenocam_ts):
|
|||
fusion_vals.append(fusion_val)
|
||||
phenocam_vals.append(phenocam_val)
|
||||
dates.append(date)
|
||||
|
||||
|
||||
return np.array(fusion_vals), np.array(phenocam_vals), dates
|
||||
|
||||
|
||||
|
|
@ -95,10 +95,10 @@ def nse(y_true, y_pred):
|
|||
def calculate_temporal_metrics(fusion_ts, phenocam_ts):
|
||||
"""Calculate all 6 temporal metrics."""
|
||||
fusion_vals, phenocam_vals, dates = match_dates(fusion_ts, phenocam_ts)
|
||||
|
||||
|
||||
if len(fusion_vals) < 2:
|
||||
return None
|
||||
|
||||
|
||||
metrics = {
|
||||
"pearson_r": pearson_correlation(phenocam_vals, fusion_vals),
|
||||
"r_squared": r_squared(phenocam_vals, fusion_vals),
|
||||
|
|
@ -117,7 +117,7 @@ def calculate_phenocam_stats(phenocam_ts):
|
|||
values = [v for v in phenocam_ts.values() if v is not None]
|
||||
if len(values) == 0:
|
||||
return None
|
||||
|
||||
|
||||
vals = np.array(values)
|
||||
return {
|
||||
"mean": float(np.mean(vals)),
|
||||
|
|
@ -134,44 +134,44 @@ def _get_spatial_stats_from_raster(raster_file, site_position):
|
|||
with rasterio.open(raster_file) as src:
|
||||
if src.count < 3:
|
||||
return None
|
||||
|
||||
|
||||
blue = src.read(BLUE_BAND).astype(np.float32)
|
||||
green = src.read(GREEN_BAND).astype(np.float32)
|
||||
red = src.read(RED_BAND).astype(np.float32)
|
||||
|
||||
|
||||
lon, lat = site_position[1], site_position[0]
|
||||
x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
|
||||
|
||||
|
||||
if not (
|
||||
src.bounds.left <= x[0] <= src.bounds.right
|
||||
and src.bounds.bottom <= y[0] <= src.bounds.top
|
||||
):
|
||||
return None
|
||||
|
||||
|
||||
row, col = src.index(x[0], y[0])
|
||||
if row < 0 or row >= src.height or col < 0 or col >= src.width:
|
||||
return None
|
||||
|
||||
|
||||
# Extract 3x3 window with boundary handling
|
||||
r0, r1 = max(0, row - 1), min(src.height, row + 2)
|
||||
c0, c1 = max(0, col - 1), min(src.width, col + 2)
|
||||
blue_window = blue[r0:r1, c0:c1]
|
||||
green_window = green[r0:r1, c0:c1]
|
||||
red_window = red[r0:r1, c0:c1]
|
||||
|
||||
|
||||
# Calculate GCC for each pixel in window
|
||||
total = red_window + green_window + blue_window
|
||||
mask = (total > 0) & ~np.isnan(total) & (blue_window >= 0) & (green_window >= 0) & (red_window >= 0)
|
||||
if not np.any(mask):
|
||||
return None
|
||||
|
||||
|
||||
gcc_window = np.zeros_like(green_window, dtype=np.float32)
|
||||
gcc_window[mask] = green_window[mask] / total[mask]
|
||||
valid_gcc = gcc_window[mask]
|
||||
|
||||
|
||||
if len(valid_gcc) == 0:
|
||||
return None
|
||||
|
||||
|
||||
return {
|
||||
"mean": float(np.mean(valid_gcc)),
|
||||
"std": float(np.std(valid_gcc)),
|
||||
|
|
@ -187,15 +187,15 @@ def calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position):
|
|||
fusion_raster_dir = Path(fusion_raster_dir)
|
||||
if not fusion_raster_dir.exists():
|
||||
return None
|
||||
|
||||
|
||||
spatial_means = []
|
||||
phenocam_vals = []
|
||||
|
||||
|
||||
# Process each fusion raster file
|
||||
for raster_file in sorted(fusion_raster_dir.glob("*.geotiff")):
|
||||
if "DIST_CLOUD" in raster_file.name:
|
||||
continue
|
||||
|
||||
|
||||
# Extract date from filename
|
||||
parts = raster_file.stem.split("_")
|
||||
date_str = None
|
||||
|
|
@ -203,35 +203,35 @@ def calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position):
|
|||
if len(part) == 8 and part.isdigit():
|
||||
date_str = part
|
||||
break
|
||||
|
||||
|
||||
if not date_str:
|
||||
continue
|
||||
|
||||
|
||||
# Convert to ISO format for matching
|
||||
try:
|
||||
date = datetime.strptime(date_str, "%Y%m%d").isoformat()
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
|
||||
# Get phenocam value for this date
|
||||
phenocam_val = phenocam_ts.get(date)
|
||||
if phenocam_val is None:
|
||||
continue
|
||||
|
||||
|
||||
# Extract spatial statistics
|
||||
stats = _get_spatial_stats_from_raster(raster_file, site_position)
|
||||
if stats is None:
|
||||
continue
|
||||
|
||||
|
||||
spatial_means.append(stats["mean"])
|
||||
phenocam_vals.append(phenocam_val)
|
||||
|
||||
|
||||
if len(spatial_means) < 2:
|
||||
return None
|
||||
|
||||
|
||||
spatial_means = np.array(spatial_means)
|
||||
phenocam_vals = np.array(phenocam_vals)
|
||||
|
||||
|
||||
return {
|
||||
"pearson_r": pearson_correlation(phenocam_vals, spatial_means),
|
||||
"r_squared": r_squared(phenocam_vals, spatial_means),
|
||||
|
|
@ -243,24 +243,24 @@ def calculate_scenario_metrics(season, site_name, strategy, sigma, site_position
|
|||
"""Calculate metrics for one scenario."""
|
||||
base = Path(f"data/{site_name}/{season}")
|
||||
processed_dir = f"processed_{strategy}_sigma{sigma}"
|
||||
|
||||
|
||||
# Load timeseries
|
||||
fusion_ts_path = base / processed_dir / "gcc" / "fusion" / "timeseries.json"
|
||||
phenocam_ts_path = base / "raw" / "phenocam" / "timeseries.json"
|
||||
|
||||
|
||||
fusion_ts = load_timeseries(fusion_ts_path)
|
||||
phenocam_ts = load_timeseries(phenocam_ts_path)
|
||||
|
||||
|
||||
if not fusion_ts or not phenocam_ts:
|
||||
return None, None
|
||||
|
||||
|
||||
# Calculate temporal metrics
|
||||
temporal_metrics = calculate_temporal_metrics(fusion_ts, phenocam_ts)
|
||||
|
||||
|
||||
# Calculate spatial metrics
|
||||
fusion_raster_dir = base / processed_dir / "fusion"
|
||||
spatial_metrics = calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position)
|
||||
|
||||
|
||||
return temporal_metrics, spatial_metrics
|
||||
|
||||
|
||||
|
|
@ -268,20 +268,20 @@ def calculate_all_metrics(season, site_name, site_position):
|
|||
"""Calculate metrics for all 4 scenarios and save to JSON."""
|
||||
results = {"temporal": {}, "spatial": {}}
|
||||
base = Path(f"data/{site_name}/{season}")
|
||||
|
||||
|
||||
# Load phenocam timeseries once (same for all scenarios)
|
||||
phenocam_ts_path = base / "raw" / "phenocam" / "timeseries.json"
|
||||
phenocam_ts = load_timeseries(phenocam_ts_path)
|
||||
|
||||
|
||||
if not phenocam_ts:
|
||||
print("[METRICS] Warning: No phenocam data found")
|
||||
return results
|
||||
|
||||
|
||||
# Calculate phenocam stats
|
||||
phenocam_stats = calculate_phenocam_stats(phenocam_ts)
|
||||
if phenocam_stats:
|
||||
results["phenocam_stats"] = phenocam_stats
|
||||
|
||||
|
||||
# Calculate S2 baseline metrics once (S2 data is identical across scenarios)
|
||||
s2_ts_path = base / "processed_aggressive_sigma20" / "gcc" / "s2" / "timeseries.json"
|
||||
s2_ts = load_timeseries(s2_ts_path)
|
||||
|
|
@ -289,34 +289,34 @@ def calculate_all_metrics(season, site_name, site_position):
|
|||
s2_metrics = calculate_temporal_metrics(s2_ts, phenocam_ts)
|
||||
if s2_metrics:
|
||||
results["baseline"] = {"s2": s2_metrics}
|
||||
|
||||
|
||||
# Calculate fusion metrics for each scenario
|
||||
for strategy in ["aggressive", "nonaggressive"]:
|
||||
for sigma in [20, 30]:
|
||||
scenario_name = f"{strategy}_sigma{sigma}"
|
||||
print(f"[METRICS] Calculating metrics for {scenario_name}...")
|
||||
|
||||
|
||||
processed_dir = f"processed_{strategy}_sigma{sigma}"
|
||||
|
||||
|
||||
# Load fusion timeseries
|
||||
fusion_ts_path = base / processed_dir / "gcc" / "fusion" / "timeseries.json"
|
||||
fusion_ts = load_timeseries(fusion_ts_path)
|
||||
|
||||
|
||||
if not fusion_ts:
|
||||
print(f"[METRICS] Warning: Missing fusion data for {scenario_name}, skipping")
|
||||
continue
|
||||
|
||||
|
||||
# Calculate temporal metrics
|
||||
temporal_metrics = calculate_temporal_metrics(fusion_ts, phenocam_ts)
|
||||
if temporal_metrics:
|
||||
results["temporal"][scenario_name] = temporal_metrics
|
||||
|
||||
|
||||
# Calculate spatial metrics
|
||||
fusion_raster_dir = base / processed_dir / "fusion"
|
||||
spatial_metrics = calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position)
|
||||
if spatial_metrics:
|
||||
results["spatial"][scenario_name] = spatial_metrics
|
||||
|
||||
|
||||
# Add summary
|
||||
if results["temporal"]:
|
||||
best_temporal = max(
|
||||
|
|
@ -324,7 +324,7 @@ def calculate_all_metrics(season, site_name, site_position):
|
|||
key=lambda x: x[1].get("r_squared", -1) if x[1].get("r_squared") is not None else -1
|
||||
)
|
||||
results["summary"] = {"best_temporal_scenario": best_temporal[0]}
|
||||
|
||||
|
||||
if results["spatial"]:
|
||||
best_spatial = max(
|
||||
results["spatial"].items(),
|
||||
|
|
@ -333,38 +333,38 @@ def calculate_all_metrics(season, site_name, site_position):
|
|||
if "summary" not in results:
|
||||
results["summary"] = {}
|
||||
results["summary"]["best_spatial_scenario"] = best_spatial[0]
|
||||
|
||||
|
||||
# Save results
|
||||
output_path = Path(f"data/{site_name}/{season}/metrics.json")
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print(f"[METRICS] Saved results to {output_path}")
|
||||
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
"""Standalone script entry point."""
|
||||
import sys
|
||||
|
||||
|
||||
if len(sys.argv) < 4:
|
||||
print("Usage: calculate_metrics.py <season> <site_name> <lat> <lon>")
|
||||
print("Example: calculate_metrics.py 2024 innsbruck 47.116171 11.320308")
|
||||
print("Usage: metrics_stats.py <season> <site_name> <lat> <lon>")
|
||||
print("Example: metrics_stats.py 2024 innsbruck 47.116171 11.320308")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
season = int(sys.argv[1])
|
||||
site_name = sys.argv[2]
|
||||
site_position = (float(sys.argv[3]), float(sys.argv[4]))
|
||||
|
||||
|
||||
results = calculate_all_metrics(season, site_name, site_position)
|
||||
|
||||
|
||||
# Save results
|
||||
output_path = Path(f"data/{site_name}/{season}/metrics.json")
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
|
||||
|
||||
print(f"[METRICS] Saved results to {output_path}")
|
||||
|
||||
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
"""Post-processing: crop fusion/S2/S3 to valid pixels."""
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import rasterio
|
||||
|
|
@ -12,16 +13,16 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
|
|||
prepared = base / f"prepared_{cleaning_strategy}"
|
||||
processed_dir = f"processed_{cleaning_strategy}_sigma{sigma}" if sigma else f"processed_{cleaning_strategy}_sigma20"
|
||||
processed = base / processed_dir
|
||||
|
||||
|
||||
s2_prep = prepared / "s2"
|
||||
s3_prep = prepared / "s3"
|
||||
fusion_prep = prepared / (f"fusion_sigma{sigma}" if sigma else "fusion")
|
||||
|
||||
|
||||
for output_dir in [processed / "s2", processed / "s3", processed / "fusion"]:
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
print(f"[PROCESS] Processing files: {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}")
|
||||
|
||||
|
||||
# Crop fusion to valid data and get dimensions
|
||||
fusion_dims = {}
|
||||
for fusion_file in fusion_prep.glob("REFL_*.tif"):
|
||||
|
|
@ -49,7 +50,7 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
|
|||
dst.write(data_crop)
|
||||
fusion_dims[date_str] = (c0, r0, w, h, transform, src.transform, src.crs, src.profile)
|
||||
print(f"[PROCESS] Cropped fusion: {output_file}")
|
||||
|
||||
|
||||
# Crop S2 and S3 to fusion size
|
||||
for date_str, (c0, r0, w, h, transform, fusion_transform, crs, fusion_profile) in fusion_dims.items():
|
||||
window = windows.Window(c0, r0, w, h)
|
||||
|
|
@ -91,7 +92,7 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
|
|||
with rasterio.open(output_file, "w", **p2) as dst:
|
||||
dst.write(data)
|
||||
print(f"[PROCESS] Cropped: {output_file}")
|
||||
|
||||
|
||||
print("[PROCESS] Completed")
|
||||
|
||||
|
||||
|
|
@ -100,3 +101,8 @@ def process_all_scenarios(season, site_position, site_name):
|
|||
for strategy in ["aggressive", "nonaggressive"]:
|
||||
for sigma in [None, 30]:
|
||||
process_cropped(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma)
|
||||
|
||||
|
||||
# Aliases
|
||||
postprocess = process_cropped
|
||||
postprocess_all_scenarios = process_all_scenarios
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
"""Data preparation: S2/S3 preprocessing for fusion."""
|
||||
import json
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
from collections import defaultdict
|
||||
import numpy as np
|
||||
import rasterio
|
||||
|
|
@ -9,24 +9,20 @@ from rasterio.warp import Resampling
|
|||
from rasterio.vrt import WarpedVRT
|
||||
from rasterio import shutil as rio_shutil
|
||||
|
||||
RESOLUTION_RATIO = 21
|
||||
|
||||
def _import_efast():
|
||||
"""Lazy import of efast to avoid import errors when not using efast functions."""
|
||||
|
||||
def _import_distance_to_clouds():
|
||||
"""Lazy import of efast.distance_to_clouds."""
|
||||
try:
|
||||
import efast
|
||||
from efast.s2_processing import distance_to_clouds
|
||||
from efast.s3_processing import reproject_and_crop_s3
|
||||
|
||||
return efast, distance_to_clouds, reproject_and_crop_s3
|
||||
return distance_to_clouds
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"efast package not found. Install with: pip install git+https://github.com/DHI-GRAS/efast.git"
|
||||
)
|
||||
|
||||
|
||||
RESOLUTION_RATIO = 21
|
||||
|
||||
|
||||
def _load_clouds(clouds_file):
|
||||
clouds = {"s2": set(), "s3": set()}
|
||||
if clouds_file.exists():
|
||||
|
|
@ -100,12 +96,7 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
|
|||
|
||||
temp_normalized = s2_output_dir / f"temp_{s2_file.name}"
|
||||
with rasterio.open(s2_file) as src:
|
||||
pb = src.tags().get("PROCESSING_BASELINE", "")
|
||||
data = src.read().astype("float32")
|
||||
mask_nodata = data == 0
|
||||
data = (data - 1000) / 10000.0 if pb >= "04.00" else data / 10000.0
|
||||
data = np.maximum(data, 0)
|
||||
data[mask_nodata] = 0
|
||||
data = src.read().astype("float32") / 10000.0
|
||||
profile = src.profile.copy()
|
||||
profile.update({"dtype": "float32", "nodata": 0})
|
||||
with rasterio.open(temp_normalized, "w", **profile) as dst:
|
||||
|
|
@ -116,7 +107,7 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
|
|||
)
|
||||
temp_normalized.unlink()
|
||||
|
||||
_, distance_to_clouds, _ = _import_efast()
|
||||
distance_to_clouds = _import_distance_to_clouds()
|
||||
distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
|
||||
|
||||
|
||||
|
|
@ -200,59 +191,3 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
|
|||
rio_shutil.copy(vrt, outfile, **profile)
|
||||
|
||||
shutil.rmtree(temp_composite_dir)
|
||||
|
||||
|
||||
def run_efast(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None, date_range=None):
|
||||
lat, lon = site_position
|
||||
datetime_range = date_range or f"{season}-01-01/{season}-12-31"
|
||||
|
||||
efast_base_dir = _get_base_dir(season, site_name, cleaning_strategy)
|
||||
s2_output_dir = efast_base_dir / "s2"
|
||||
s3_output_dir = efast_base_dir / "s3"
|
||||
fusion_output_dir = efast_base_dir / (f"fusion_sigma{sigma}" if sigma else "fusion")
|
||||
|
||||
fusion_output_dir.mkdir(parents=True, exist_ok=True)
|
||||
print(f"[EFAST] Starting fusion: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
|
||||
|
||||
efast, _, _ = _import_efast()
|
||||
|
||||
start_str, end_str = datetime_range.split("/")
|
||||
start_date = datetime.strptime(start_str, "%Y-%m-%d")
|
||||
end_date = datetime.strptime(end_str, "%Y-%m-%d")
|
||||
|
||||
current_date = start_date
|
||||
while current_date <= end_date:
|
||||
date_str = current_date.strftime("%Y%m%d")
|
||||
output_file = fusion_output_dir / f"REFL_{date_str}.tif"
|
||||
try:
|
||||
kwargs = {
|
||||
"product": "REFL",
|
||||
"max_days": 30,
|
||||
"date_position": 2,
|
||||
"minimum_acquisition_importance": 0.0,
|
||||
"ratio": RESOLUTION_RATIO,
|
||||
}
|
||||
if sigma is not None:
|
||||
kwargs["sigma"] = sigma
|
||||
efast.fusion(current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs)
|
||||
print(
|
||||
f"[EFAST] Saved: {output_file}"
|
||||
if output_file.exists()
|
||||
else f"[EFAST] No output for {date_str} (insufficient nearby data)"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[EFAST] Error processing {date_str}: {e}")
|
||||
current_date += timedelta(days=1)
|
||||
|
||||
print("[EFAST] Completed")
|
||||
|
||||
|
||||
def run_all_efast_scenarios(season, site_position, site_name, sigma_value=30, date_range=None):
|
||||
from clouds import detect_clouds
|
||||
|
||||
for strategy in ["aggressive", "nonaggressive"]:
|
||||
detect_clouds(season, site_name, cleaning_strategy=strategy)
|
||||
prepare_s2(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
|
||||
prepare_s3(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
|
||||
run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=None, date_range=date_range)
|
||||
run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma_value, date_range=date_range)
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
"""Pre-selection: NDVI-based cloud/flaw filtering for S2 and S3 data."""
|
||||
import json
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
|
@ -8,6 +9,7 @@ THRESHOLDS = {"aggressive": {"threshold": 0.3, "delta": 0.15}, "nonaggressive":
|
|||
|
||||
|
||||
def detect_clouds(season, site_name, cleaning_strategy="aggressive"):
|
||||
"""Filter cloud-covered/flawed S2 and S3 files using NDVI thresholds."""
|
||||
output_file = Path(f"data/{site_name}/{season}/clouds_{cleaning_strategy}.json")
|
||||
clouds = {"s2": [], "s3": []}
|
||||
thresholds = THRESHOLDS[cleaning_strategy]
|
||||
|
|
@ -61,3 +63,7 @@ def detect_clouds(season, site_name, cleaning_strategy="aggressive"):
|
|||
json.dump(clouds, f, indent=2)
|
||||
|
||||
print(f"[CLOUDS] Saved: {output_file}")
|
||||
|
||||
|
||||
# Alias for backward compatibility
|
||||
preselect = detect_clouds
|
||||
38
run.py
38
run.py
|
|
@ -1,31 +1,28 @@
|
|||
from call_efast import run_all_efast_scenarios
|
||||
from post_process import process_all_scenarios
|
||||
from generate_indexes import (
|
||||
generate_ndvi_raw,
|
||||
from fusion import run_all_efast_scenarios
|
||||
from postprocessing import process_all_scenarios
|
||||
from metrics_indices import (
|
||||
create_ndvi_timeseries_raw,
|
||||
generate_ndvi_post_process,
|
||||
create_ndvi_timeseries_post_process,
|
||||
generate_gcc_post_process,
|
||||
create_gcc_timeseries_post_process,
|
||||
create_s2_bands_timeseries_post_process,
|
||||
)
|
||||
from download_s2 import download_s2
|
||||
from download_s3 import download_s3
|
||||
from download_phenocam import download_phenocam, download_phenocam_greenness
|
||||
from clouds import detect_clouds
|
||||
from calculate_metrics import calculate_all_metrics
|
||||
from acquisition_s2 import download_s2
|
||||
from acquisition_s3 import download_s3
|
||||
from acquisition_phenocam import download_phenocam, download_phenocam_greenness
|
||||
from metrics_stats import calculate_all_metrics
|
||||
|
||||
|
||||
def run_pipeline(season, site_position, site_name):
|
||||
"""Run pipeline (downloads + EFAST fusion + post-process + metrics)."""
|
||||
try:
|
||||
# Download steps (needed for new site/season)
|
||||
download_s2(season, site_position, site_name)
|
||||
download_s3(season, site_position, site_name)
|
||||
download_phenocam(season, site_position, site_name)
|
||||
download_phenocam_greenness(season, site_position, site_name)
|
||||
#download_s2(season, site_position, site_name)
|
||||
#download_s3(season, site_position, site_name)
|
||||
#download_phenocam(season, site_position, site_name)
|
||||
#download_phenocam_greenness(season, site_position, site_name)
|
||||
|
||||
print(f"Generating NDVI for raw data: {site_name}, {season}")
|
||||
create_ndvi_timeseries_raw(season, site_position, site_name)
|
||||
#print(f"Generating NDVI for raw data: {site_name}, {season}")
|
||||
#create_ndvi_timeseries_raw(season, site_position, site_name)
|
||||
|
||||
print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
|
||||
run_all_efast_scenarios(season, site_position, site_name)
|
||||
|
|
@ -39,6 +36,9 @@ def run_pipeline(season, site_position, site_name):
|
|||
print(f"Generating GCC for final outputs: {site_name}, {season}")
|
||||
create_gcc_timeseries_post_process(season, site_position, site_name)
|
||||
|
||||
print(f"Generating S2 band timeseries: {site_name}, {season}")
|
||||
create_s2_bands_timeseries_post_process(season, site_position, site_name)
|
||||
|
||||
print(f"Calculating metrics: {site_name}, {season}")
|
||||
calculate_all_metrics(season, site_name, site_position)
|
||||
|
||||
|
|
@ -48,6 +48,8 @@ def run_pipeline(season, site_position, site_name):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
run_pipeline(2024, (47.116171, 11.320308), "innsbruck")
|
||||
# forthgr - FORTH Heraklion Greece, Agriculture, 2024
|
||||
# sites.geojson: lon=25.0743, lat=35.3045
|
||||
run_pipeline(2024, (35.3045, 25.0743), "forthgr")
|
||||
#run_pipeline(2024, (35.3045, 25.0743), "forthgr")
|
||||
|
|
|
|||
|
|
@ -11,6 +11,9 @@
|
|||
.slider-container { position: sticky; top: 0; background: white; padding: 20px; z-index: 1000; border-bottom: 1px solid #ccc; }
|
||||
.scenario-selector { margin-bottom: 10px; }
|
||||
.scenario-selector select { padding: 5px 10px; font-size: 14px; }
|
||||
.site-selector { margin-bottom: 10px; }
|
||||
.site-selector select { padding: 5px 10px; font-size: 14px; }
|
||||
.site-selector label { margin-right: 5px; }
|
||||
.container { max-width: 1400px; margin: 0 auto; padding: 20px; }
|
||||
.header { display: flex; gap: 20px; margin-bottom: 20px; border-bottom: 1px solid #ccc; padding-top: 10px;padding-bottom: 20px;}
|
||||
.header-col { flex: 1; }
|
||||
|
|
@ -60,6 +63,12 @@
|
|||
<div id="sitemap" class="sitemap"></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="site-selector">
|
||||
<label for="siteSelect">Site: </label>
|
||||
<select id="siteSelect"></select>
|
||||
<label for="seasonSelect">Season: </label>
|
||||
<select id="seasonSelect"></select>
|
||||
</div>
|
||||
<div class="scenario-selector">
|
||||
<label for="scenarioSelect">Scenario: </label>
|
||||
<select id="scenarioSelect">
|
||||
|
|
@ -118,15 +127,16 @@
|
|||
proj4.defs("EPSG:32632", "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs");
|
||||
proj4.defs("EPSG:4326", "+proj=longlat +datum=WGS84 +no_defs");
|
||||
|
||||
const start = new Date(2024, 0, 1);
|
||||
let start = new Date(2024, 0, 1);
|
||||
const slider = document.getElementById("dateSlider");
|
||||
const dateDisplay = document.getElementById("dateDisplay");
|
||||
const osmUrl = "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png";
|
||||
const osmOpts = { attribution: "OpenStreetMap", opacity: 0.4 };
|
||||
const mapOpts = { zoomControl: false };
|
||||
const sitePosition = [47.116171, 11.320308];
|
||||
const siteName = "innsbruck";
|
||||
const season = "2024";
|
||||
let sitePosition = [47.116171, 11.320308];
|
||||
let siteName = "innsbruck";
|
||||
let season = "2024";
|
||||
let sitesData = null;
|
||||
|
||||
const urlParams = new URLSearchParams(location.search);
|
||||
const strategy = urlParams.get("strategy") || "aggressive";
|
||||
|
|
@ -139,7 +149,7 @@
|
|||
let allScenariosGCC = {};
|
||||
let metricsData = null;
|
||||
const siteMap = L.map("sitemap", { zoomControl: false }).setView(sitePosition, 4).addLayer(L.tileLayer(osmUrl, { attribution: "OpenStreetMap", opacity: 1 }));
|
||||
L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:8px;height:8px;background:red;border:2px solid white;border-radius:50%;box-shadow:0 0 2px rgba(0,0,0,0.5);'></div>", iconSize: [8, 8] }) }).addTo(siteMap);
|
||||
const siteMarker = L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:8px;height:8px;background:red;border:2px solid white;border-radius:50%;box-shadow:0 0 2px rgba(0,0,0,0.5);'></div>", iconSize: [8, 8] }) }).addTo(siteMap);
|
||||
const maps = {
|
||||
s2: L.map("s2map", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts)),
|
||||
fusion: L.map("fusionmap", mapOpts).setView(sitePosition, 12).addLayer(L.tileLayer(osmUrl, osmOpts)),
|
||||
|
|
@ -173,6 +183,7 @@
|
|||
});
|
||||
|
||||
async function loadTimeseries() {
|
||||
metricsData = null;
|
||||
const fusionPath = getFusionPath();
|
||||
const [s2, fusion, s3, s2gcc, fusiongcc, s3gcc, phenocam] = await Promise.all([
|
||||
fetch(`../data/${siteName}/${season}/processed_${strategy}_sigma${sigma}/ndvi/s2/timeseries.json`).then(r => r.json()).catch(() => []),
|
||||
|
|
@ -526,8 +537,13 @@
|
|||
|
||||
function drawMetricsTable() {
|
||||
const container = document.getElementById("metricsTable");
|
||||
const hasAnyData = timeseries.s2.length || timeseries.fusion.length || timeseries.s3.length || phenocamGreennessTimeseries.length;
|
||||
if (!hasAnyData) {
|
||||
container.innerHTML = "<p style='color:#666; font-size:12px;'>No data for this site/season.</p>";
|
||||
return;
|
||||
}
|
||||
if (!metricsData || !metricsData.temporal) {
|
||||
container.innerHTML = "<p style='color:#666; font-size:12px;'>Metrics not available. Run calculate_metrics.py to generate.</p>";
|
||||
container.innerHTML = "<p style='color:#666; font-size:12px;'>Metrics not available. Run the pipeline (run.py) or metrics_stats.py to generate.</p>";
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -789,6 +805,8 @@
|
|||
const date = dateFromDays(parseInt(slider.value));
|
||||
dateDisplay.textContent = date;
|
||||
const params = new URLSearchParams();
|
||||
params.set("site", siteName);
|
||||
params.set("season", season);
|
||||
params.set("date", date);
|
||||
params.set("strategy", strategy);
|
||||
if (sigma !== "20") params.set("sigma", sigma);
|
||||
|
|
@ -822,10 +840,131 @@
|
|||
window.location.search = params.toString();
|
||||
});
|
||||
|
||||
const urlDate = urlParams.get("date");
|
||||
if (urlDate) slider.value = daysFromDate(urlDate);
|
||||
const siteSelect = document.getElementById("siteSelect");
|
||||
const seasonSelect = document.getElementById("seasonSelect");
|
||||
|
||||
function getSiteBySitename(sitename) {
|
||||
return sitesData?.features?.find(f => f.properties?.sitename === sitename);
|
||||
}
|
||||
|
||||
let availableSiteSeasons = {}; // { sitename: [season, ...] }
|
||||
|
||||
function populateSeasonOptions(sitename) {
|
||||
seasonSelect.innerHTML = "";
|
||||
const seasons = availableSiteSeasons[sitename] || [];
|
||||
for (const s of seasons) {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = s;
|
||||
opt.textContent = s;
|
||||
seasonSelect.appendChild(opt);
|
||||
}
|
||||
}
|
||||
|
||||
async function probeDataExists(sitename, season) {
|
||||
try {
|
||||
const res = await fetch(`../data/${sitename}/${season}/metrics.json`, { method: "HEAD" });
|
||||
return res.ok;
|
||||
} catch { return false; }
|
||||
}
|
||||
|
||||
async function setSiteSeason(newSiteName, newSeason) {
|
||||
const site = getSiteBySitename(newSiteName);
|
||||
let pos;
|
||||
let description;
|
||||
if (site) {
|
||||
const [lon, lat] = site.geometry.coordinates;
|
||||
pos = [lat, lon];
|
||||
description = site.properties.description || newSiteName;
|
||||
} else {
|
||||
pos = [47.116171, 11.320308];
|
||||
description = newSiteName;
|
||||
}
|
||||
siteName = newSiteName;
|
||||
season = newSeason;
|
||||
sitePosition = pos;
|
||||
start = new Date(parseInt(season), 0, 1);
|
||||
const yearEnd = new Date(parseInt(season), 11, 31);
|
||||
slider.max = Math.ceil((yearEnd - start) / 86400000);
|
||||
document.getElementById("siteName").textContent = description;
|
||||
document.getElementById("season").textContent = season;
|
||||
siteMap.setView(sitePosition, 4);
|
||||
siteMarker.setLatLng(sitePosition);
|
||||
for (const source of ["s2", "fusion", "s3"]) {
|
||||
maps[source].setView(sitePosition, 12);
|
||||
markers[source].setLatLng(sitePosition);
|
||||
}
|
||||
const params = new URLSearchParams(location.search);
|
||||
params.set("site", siteName);
|
||||
params.set("season", season);
|
||||
history.replaceState({}, "", `?${params}`);
|
||||
await loadTimeseries();
|
||||
const urlDate = params.get("date");
|
||||
if (urlDate) slider.value = daysFromDate(urlDate);
|
||||
await updateImages();
|
||||
}
|
||||
|
||||
async function init() {
|
||||
try {
|
||||
const res = await fetch("../data/sites.geojson");
|
||||
if (!res.ok) throw new Error("Could not load sites");
|
||||
sitesData = await res.json();
|
||||
} catch (e) {
|
||||
console.error("Failed to load sites.geojson:", e);
|
||||
sitesData = { features: [] };
|
||||
}
|
||||
const features = sitesData.features || [];
|
||||
availableSiteSeasons = {};
|
||||
for (const f of features) {
|
||||
const sn = f.properties?.sitename;
|
||||
if (!sn) continue;
|
||||
const seasonsFromGeo = f.properties?.seasons ? Object.keys(f.properties.seasons).sort() : [];
|
||||
const withData = [];
|
||||
for (const s of seasonsFromGeo) {
|
||||
if (await probeDataExists(sn, s)) withData.push(s);
|
||||
}
|
||||
if (withData.length) availableSiteSeasons[sn] = withData;
|
||||
}
|
||||
const availableSites = Object.keys(availableSiteSeasons);
|
||||
siteSelect.innerHTML = "";
|
||||
if (availableSites.length === 0) {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = "innsbruck";
|
||||
opt.textContent = "innsbruck";
|
||||
siteSelect.appendChild(opt);
|
||||
availableSiteSeasons.innsbruck = ["2024"];
|
||||
} else {
|
||||
for (const sn of availableSites.sort()) {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = sn;
|
||||
opt.textContent = sn;
|
||||
siteSelect.appendChild(opt);
|
||||
}
|
||||
}
|
||||
const urlSite = urlParams.get("site");
|
||||
const urlSeason = urlParams.get("season");
|
||||
const initialSite = urlSite && availableSites.includes(urlSite) ? urlSite : availableSites[0] || "innsbruck";
|
||||
siteName = initialSite;
|
||||
siteSelect.value = initialSite;
|
||||
populateSeasonOptions(initialSite);
|
||||
const seasons = availableSiteSeasons[initialSite] || [];
|
||||
const initialSeason = urlSeason && seasons.includes(urlSeason) ? urlSeason : (seasons[0] || "2024");
|
||||
season = initialSeason;
|
||||
seasonSelect.value = initialSeason;
|
||||
siteSelect.addEventListener("change", function() {
|
||||
const sn = this.value;
|
||||
populateSeasonOptions(sn);
|
||||
const seas = availableSiteSeasons[sn] || [];
|
||||
seasonSelect.value = seas[0] || season;
|
||||
setSiteSeason(sn, seasonSelect.value);
|
||||
});
|
||||
seasonSelect.addEventListener("change", function() {
|
||||
setSiteSeason(siteSelect.value, this.value);
|
||||
});
|
||||
await setSiteSeason(initialSite, initialSeason);
|
||||
}
|
||||
|
||||
slider.addEventListener("input", updateImages);
|
||||
loadTimeseries().then(updateImages);
|
||||
init();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
|
|||
492
webapp/s2-timeseries.html
Normal file
492
webapp/s2-timeseries.html
Normal file
|
|
@ -0,0 +1,492 @@
|
|||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>S2 Band Reflectance Timeseries</title>
|
||||
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
|
||||
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/geotiff@2.0.7/dist-browser/geotiff.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/proj4@2.9.0/dist/proj4.js"></script>
|
||||
<style>
|
||||
body { margin: 0; font-family: sans-serif; }
|
||||
.container { max-width: 900px; margin: 0 auto; padding: 20px; }
|
||||
.selectors { margin-bottom: 20px; }
|
||||
.selectors select { padding: 5px 10px; font-size: 14px; margin-right: 15px; }
|
||||
h1 { margin: 0 0 5px 0; font-size: 22px; }
|
||||
h2 { margin: 0 0 15px 0; font-size: 16px; color: #666; }
|
||||
.plot { width: 100%; height: 100px; border: 1px solid #ccc; margin-bottom: 15px; }
|
||||
.plot-label { font-size: 12px; margin-bottom: 3px; color: #666; }
|
||||
#dateSlider { width: 100%; margin: 15px 0; }
|
||||
#dateDisplay { text-align: center; font-size: 14px; color: #666; }
|
||||
.map-label { font-size: 12px; margin-bottom: 3px; color: #666; }
|
||||
.map-date { font-size: 11px; margin-top: 3px; color: #999; }
|
||||
#s2map { height: 400px; border: 1px solid #ccc; margin-top: 10px; }
|
||||
.leaflet-image-layer { image-rendering: pixelated; }
|
||||
.leaflet-control-attribution { display: none; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1 id="siteName">Innsbruck</h1>
|
||||
<h2 id="season">2024</h2>
|
||||
<div class="selectors">
|
||||
<label>Site:</label>
|
||||
<select id="siteSelect"></select>
|
||||
<label>Season:</label>
|
||||
<select id="seasonSelect"></select>
|
||||
<label>Scenario:</label>
|
||||
<select id="scenarioSelect">
|
||||
<option value="aggressive_20">Aggressive σ20</option>
|
||||
<option value="aggressive_30">Aggressive σ30</option>
|
||||
<option value="nonaggressive_20">Non-aggressive σ20</option>
|
||||
<option value="nonaggressive_30">Non-aggressive σ30</option>
|
||||
</select>
|
||||
</div>
|
||||
<input type="range" id="dateSlider" min="0" max="365" value="0">
|
||||
<div id="dateDisplay">2024-01-01</div>
|
||||
<div class="map-label">S2 RGB (closest available)</div>
|
||||
<div id="s2rgbdate" class="map-date"></div>
|
||||
<div id="s2map"></div>
|
||||
<div id="bandPlots"></div>
|
||||
</div>
|
||||
<script>
|
||||
proj4.defs("EPSG:32632", "+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs");
|
||||
proj4.defs("EPSG:4326", "+proj=longlat +datum=WGS84 +no_defs");
|
||||
|
||||
const BANDS = [
|
||||
{ key: "b02", label: "B02 (Blue)", color: "#0066ff" },
|
||||
{ key: "b03", label: "B03 (Green)", color: "#00aa00" },
|
||||
{ key: "b04", label: "B04 (Red)", color: "#cc0000" },
|
||||
{ key: "b8a", label: "B8A (NIR)", color: "#9900cc" }
|
||||
];
|
||||
let siteName = "innsbruck", season = "2024";
|
||||
let sitePosition = [47.116171, 11.320308];
|
||||
let start = new Date(2024, 0, 1);
|
||||
let timeseries = [];
|
||||
let gccTimeseries = [];
|
||||
let ndviTimeseries = [];
|
||||
let availableSiteSeasons = {};
|
||||
let s2Map = null, s2Overlay = null, s2Marker = null;
|
||||
|
||||
const urlParams = new URLSearchParams(location.search);
|
||||
const [strategy, sigma] = (urlParams.get("scenario") || "aggressive_20").split("_");
|
||||
|
||||
function getBasePath() {
|
||||
return `processed_${strategy}_sigma${sigma || "20"}`;
|
||||
}
|
||||
|
||||
function fmtDate(d) {
|
||||
return `${d.getFullYear()}-${String(d.getMonth() + 1).padStart(2, "0")}-${String(d.getDate()).padStart(2, "0")}`;
|
||||
}
|
||||
const dateFromDays = (days) => fmtDate(new Date(start.getTime() + days * 86400000));
|
||||
const daysFromDate = (dateStr) => {
|
||||
const [y, m, d] = dateStr.split("-").map(Number);
|
||||
return Math.floor((new Date(y, m - 1, d) - start) / 86400000);
|
||||
};
|
||||
|
||||
function drawBandPlot(canvasId, bandKey, bandLabel, color) {
|
||||
const canvas = document.getElementById(canvasId);
|
||||
if (!canvas) return;
|
||||
const ctx = canvas.getContext("2d");
|
||||
canvas.width = canvas.offsetWidth;
|
||||
canvas.height = 100;
|
||||
const w = canvas.width, h = canvas.height, pad = 30;
|
||||
const plotW = w - pad * 2, plotH = h - pad * 2;
|
||||
|
||||
const data = timeseries.filter(t => t[bandKey] != null);
|
||||
if (!data.length) return;
|
||||
|
||||
const dates = data.map(t => new Date(t.date));
|
||||
const values = data.map(t => t[bandKey]);
|
||||
const minDate = new Date(Math.min(...dates)), maxDate = new Date(Math.max(...dates));
|
||||
const dateRange = maxDate - minDate || 1;
|
||||
const minVal = Math.min(...values), maxVal = Math.max(...values);
|
||||
const valRange = maxVal - minVal || 1;
|
||||
|
||||
const x = (d) => pad + ((new Date(d) - minDate) / dateRange) * plotW;
|
||||
const y = (v) => pad + plotH - ((v - minVal) / valRange) * plotH;
|
||||
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
ctx.strokeStyle = "#ccc";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(pad, pad);
|
||||
ctx.lineTo(pad, pad + plotH);
|
||||
ctx.lineTo(pad + plotW, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#000";
|
||||
ctx.font = "9px sans-serif";
|
||||
ctx.fillText(minVal.toFixed(4), 2, pad + plotH + 10);
|
||||
ctx.fillText(maxVal.toFixed(4), 2, pad + 3);
|
||||
|
||||
ctx.strokeStyle = color;
|
||||
ctx.beginPath();
|
||||
data.forEach((t, i) => {
|
||||
const px = x(t.date), py = y(t[bandKey]);
|
||||
i === 0 ? ctx.moveTo(px, py) : ctx.lineTo(px, py);
|
||||
});
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#888";
|
||||
const axisY = pad + plotH;
|
||||
for (const t of data) ctx.fillRect(x(t.date) - 1, axisY - 1, 2, 2);
|
||||
|
||||
const currentDate = dateFromDays(parseInt(document.getElementById("dateSlider").value));
|
||||
const xPos = x(currentDate);
|
||||
ctx.strokeStyle = "#f00";
|
||||
ctx.lineWidth = 2;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(xPos, pad);
|
||||
ctx.lineTo(xPos, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
const closest = data.reduce((c, t) =>
|
||||
Math.abs(new Date(t.date) - new Date(currentDate)) < Math.abs(new Date(c.date) - new Date(currentDate)) ? t : c
|
||||
);
|
||||
if (closest) {
|
||||
ctx.fillStyle = "#f00";
|
||||
ctx.font = "bold 10px sans-serif";
|
||||
ctx.fillText(closest[bandKey].toFixed(4), xPos + 5, y(closest[bandKey]) - 5);
|
||||
}
|
||||
}
|
||||
|
||||
function drawNdviPlot() {
|
||||
const canvas = document.getElementById("plot_ndvi");
|
||||
if (!canvas) return;
|
||||
const ctx = canvas.getContext("2d");
|
||||
canvas.width = canvas.offsetWidth;
|
||||
canvas.height = 100;
|
||||
const w = canvas.width, h = canvas.height, pad = 30;
|
||||
const plotW = w - pad * 2, plotH = h - pad * 2;
|
||||
const data = ndviTimeseries.filter(t => t.ndvi != null);
|
||||
if (!data.length) return;
|
||||
|
||||
const dates = data.map(t => new Date(t.date));
|
||||
const values = data.map(t => t.ndvi);
|
||||
const minDate = new Date(Math.min(...dates)), maxDate = new Date(Math.max(...dates));
|
||||
const dateRange = maxDate - minDate || 1;
|
||||
const minVal = Math.min(...values), maxVal = Math.max(...values);
|
||||
const valRange = maxVal - minVal || 1;
|
||||
const x = (d) => pad + ((new Date(d) - minDate) / dateRange) * plotW;
|
||||
const y = (v) => pad + plotH - ((v - minVal) / valRange) * plotH;
|
||||
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
ctx.strokeStyle = "#ccc";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(pad, pad);
|
||||
ctx.lineTo(pad, pad + plotH);
|
||||
ctx.lineTo(pad + plotW, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#000";
|
||||
ctx.font = "9px sans-serif";
|
||||
ctx.fillText(minVal.toFixed(3), 2, pad + plotH + 10);
|
||||
ctx.fillText(maxVal.toFixed(3), 2, pad + 3);
|
||||
|
||||
ctx.strokeStyle = "#2d7a3e";
|
||||
ctx.beginPath();
|
||||
data.forEach((t, i) => {
|
||||
const px = x(t.date), py = y(t.ndvi);
|
||||
i === 0 ? ctx.moveTo(px, py) : ctx.lineTo(px, py);
|
||||
});
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#888";
|
||||
const axisY = pad + plotH;
|
||||
for (const t of data) ctx.fillRect(x(t.date) - 1, axisY - 1, 2, 2);
|
||||
|
||||
const currentDate = dateFromDays(parseInt(document.getElementById("dateSlider").value));
|
||||
const xPos = x(currentDate);
|
||||
ctx.strokeStyle = "#f00";
|
||||
ctx.lineWidth = 2;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(xPos, pad);
|
||||
ctx.lineTo(xPos, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
const closest = data.reduce((c, t) =>
|
||||
Math.abs(new Date(t.date) - new Date(currentDate)) < Math.abs(new Date(c.date) - new Date(currentDate)) ? t : c
|
||||
);
|
||||
if (closest) {
|
||||
ctx.fillStyle = "#f00";
|
||||
ctx.font = "bold 10px sans-serif";
|
||||
ctx.fillText(closest.ndvi.toFixed(3), xPos + 5, y(closest.ndvi) - 5);
|
||||
}
|
||||
}
|
||||
|
||||
function drawGccPlot() {
|
||||
const canvas = document.getElementById("plot_gcc");
|
||||
if (!canvas) return;
|
||||
const ctx = canvas.getContext("2d");
|
||||
canvas.width = canvas.offsetWidth;
|
||||
canvas.height = 100;
|
||||
const w = canvas.width, h = canvas.height, pad = 30;
|
||||
const plotW = w - pad * 2, plotH = h - pad * 2;
|
||||
const data = gccTimeseries.filter(t => t.greenness_index != null);
|
||||
if (!data.length) return;
|
||||
|
||||
const dates = data.map(t => new Date(t.date));
|
||||
const values = data.map(t => t.greenness_index);
|
||||
const minDate = new Date(Math.min(...dates)), maxDate = new Date(Math.max(...dates));
|
||||
const dateRange = maxDate - minDate || 1;
|
||||
const minVal = Math.min(...values), maxVal = Math.max(...values);
|
||||
const valRange = maxVal - minVal || 1;
|
||||
const x = (d) => pad + ((new Date(d) - minDate) / dateRange) * plotW;
|
||||
const y = (v) => pad + plotH - ((v - minVal) / valRange) * plotH;
|
||||
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
ctx.strokeStyle = "#ccc";
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(pad, pad);
|
||||
ctx.lineTo(pad, pad + plotH);
|
||||
ctx.lineTo(pad + plotW, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#000";
|
||||
ctx.font = "9px sans-serif";
|
||||
ctx.fillText(minVal.toFixed(3), 2, pad + plotH + 10);
|
||||
ctx.fillText(maxVal.toFixed(3), 2, pad + 3);
|
||||
|
||||
ctx.strokeStyle = "#00aa00";
|
||||
ctx.beginPath();
|
||||
data.forEach((t, i) => {
|
||||
const px = x(t.date), py = y(t.greenness_index);
|
||||
i === 0 ? ctx.moveTo(px, py) : ctx.lineTo(px, py);
|
||||
});
|
||||
ctx.stroke();
|
||||
|
||||
ctx.fillStyle = "#888";
|
||||
const axisY = pad + plotH;
|
||||
for (const t of data) ctx.fillRect(x(t.date) - 1, axisY - 1, 2, 2);
|
||||
|
||||
const currentDate = dateFromDays(parseInt(document.getElementById("dateSlider").value));
|
||||
const xPos = x(currentDate);
|
||||
ctx.strokeStyle = "#f00";
|
||||
ctx.lineWidth = 2;
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(xPos, pad);
|
||||
ctx.lineTo(xPos, pad + plotH);
|
||||
ctx.stroke();
|
||||
|
||||
const closest = data.reduce((c, t) =>
|
||||
Math.abs(new Date(t.date) - new Date(currentDate)) < Math.abs(new Date(c.date) - new Date(currentDate)) ? t : c
|
||||
);
|
||||
if (closest) {
|
||||
ctx.fillStyle = "#f00";
|
||||
ctx.font = "bold 10px sans-serif";
|
||||
ctx.fillText(closest.greenness_index.toFixed(3), xPos + 5, y(closest.greenness_index) - 5);
|
||||
}
|
||||
}
|
||||
|
||||
function drawAllPlots() {
|
||||
drawNdviPlot();
|
||||
drawGccPlot();
|
||||
BANDS.forEach(b => drawBandPlot(`plot_${b.key}`, b.key, b.label, b.color));
|
||||
}
|
||||
|
||||
async function loadTimeseries() {
|
||||
const base = `../data/${siteName}/${season}/${getBasePath()}`;
|
||||
try {
|
||||
const [bandsRes, gccRes, ndviRes] = await Promise.all([
|
||||
fetch(`${base}/s2_bands/timeseries.json`),
|
||||
fetch(`${base}/gcc/s2/timeseries.json`),
|
||||
fetch(`${base}/ndvi/s2/timeseries.json`)
|
||||
]);
|
||||
timeseries = bandsRes.ok ? await bandsRes.json() : [];
|
||||
gccTimeseries = gccRes.ok ? await gccRes.json() : [];
|
||||
ndviTimeseries = ndviRes.ok ? await ndviRes.json() : [];
|
||||
} catch {
|
||||
timeseries = [];
|
||||
gccTimeseries = [];
|
||||
ndviTimeseries = [];
|
||||
}
|
||||
document.getElementById("bandPlots").innerHTML =
|
||||
`<div class="plot-label">S2 NDVI</div><canvas id="plot_ndvi" class="plot"></canvas>` +
|
||||
`<div class="plot-label">S2 GCC (Greenness Index)</div><canvas id="plot_gcc" class="plot"></canvas>` +
|
||||
BANDS.map(b => `<div class="plot-label">${b.label}</div><canvas id="plot_${b.key}" class="plot"></canvas>`).join("");
|
||||
const yearEnd = new Date(parseInt(season), 11, 31);
|
||||
document.getElementById("dateSlider").max = Math.ceil((yearEnd - start) / 86400000);
|
||||
drawAllPlots();
|
||||
document.getElementById("dateDisplay").textContent = dateFromDays(parseInt(document.getElementById("dateSlider").value));
|
||||
updateS2Imagery();
|
||||
}
|
||||
|
||||
async function probeDataExists(sitename, s) {
|
||||
try {
|
||||
const res = await fetch(`../data/${sitename}/${s}/processed_aggressive_sigma20/s2_bands/timeseries.json`, { method: "HEAD" });
|
||||
return res.ok;
|
||||
} catch { return false; }
|
||||
}
|
||||
|
||||
function getSiteBySitename(sitename) {
|
||||
return window.sitesData?.features?.find(f => f.properties?.sitename === sitename);
|
||||
}
|
||||
|
||||
async function setSiteSeason(newSite, newSeason) {
|
||||
siteName = newSite;
|
||||
season = newSeason;
|
||||
start = new Date(parseInt(season), 0, 1);
|
||||
const site = getSiteBySitename(newSite);
|
||||
if (site?.geometry?.coordinates) {
|
||||
const [lon, lat] = site.geometry.coordinates;
|
||||
sitePosition = [lat, lon];
|
||||
}
|
||||
if (s2Map) { s2Map.setView(sitePosition, 12); if (s2Marker) s2Marker.setLatLng(sitePosition); }
|
||||
document.getElementById("siteName").textContent = (site?.properties?.description || newSite);
|
||||
document.getElementById("season").textContent = season;
|
||||
const params = new URLSearchParams(location.search);
|
||||
params.set("site", siteName);
|
||||
params.set("season", season);
|
||||
history.replaceState({}, "", `?${params}`);
|
||||
await loadTimeseries();
|
||||
const urlDate = params.get("date");
|
||||
if (urlDate) document.getElementById("dateSlider").value = daysFromDate(urlDate);
|
||||
}
|
||||
|
||||
async function init() {
|
||||
try {
|
||||
const res = await fetch("../data/sites.geojson");
|
||||
window.sitesData = res.ok ? await res.json() : { features: [] };
|
||||
} catch {
|
||||
window.sitesData = { features: [] };
|
||||
}
|
||||
const features = window.sitesData.features || [];
|
||||
for (const f of features) {
|
||||
const sn = f.properties?.sitename;
|
||||
if (!sn) continue;
|
||||
const seasonsFromGeo = f.properties?.seasons ? Object.keys(f.properties.seasons).sort() : [];
|
||||
const withData = [];
|
||||
for (const s of seasonsFromGeo) {
|
||||
if (await probeDataExists(sn, s)) withData.push(s);
|
||||
}
|
||||
if (withData.length) availableSiteSeasons[sn] = withData;
|
||||
}
|
||||
const availableSites = Object.keys(availableSiteSeasons);
|
||||
const siteSelect = document.getElementById("siteSelect");
|
||||
siteSelect.innerHTML = "";
|
||||
(availableSites.length ? availableSites.sort() : ["innsbruck"]).forEach(sn => {
|
||||
const opt = document.createElement("option");
|
||||
opt.value = sn;
|
||||
opt.textContent = sn;
|
||||
siteSelect.appendChild(opt);
|
||||
if (!availableSiteSeasons[sn]) availableSiteSeasons[sn] = ["2024"];
|
||||
});
|
||||
|
||||
const urlSite = urlParams.get("site");
|
||||
const urlSeason = urlParams.get("season");
|
||||
const initialSite = (urlSite && availableSiteSeasons[urlSite]) ? urlSite : (availableSites[0] || "innsbruck");
|
||||
const initialSeason = (urlSeason && (availableSiteSeasons[initialSite] || []).includes(urlSeason)) ? urlSeason : ((availableSiteSeasons[initialSite] || [])[0] || "2024");
|
||||
|
||||
siteSelect.value = initialSite;
|
||||
document.getElementById("seasonSelect").innerHTML = (availableSiteSeasons[initialSite] || []).map(s =>
|
||||
`<option value="${s}">${s}</option>`
|
||||
).join("");
|
||||
document.getElementById("seasonSelect").value = initialSeason;
|
||||
document.getElementById("scenarioSelect").value = `${strategy}_${sigma || "20"}`;
|
||||
|
||||
const initSite = getSiteBySitename(initialSite);
|
||||
if (initSite?.geometry?.coordinates) {
|
||||
const [lon, lat] = initSite.geometry.coordinates;
|
||||
sitePosition = [lat, lon];
|
||||
}
|
||||
const osmUrl = "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png";
|
||||
s2Map = L.map("s2map", { zoomControl: false }).setView(sitePosition, 12)
|
||||
.addLayer(L.tileLayer(osmUrl, { attribution: "OpenStreetMap", opacity: 0.4 }));
|
||||
s2Marker = L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:8px;height:8px;background:red;border:2px solid white;border-radius:50%;box-shadow:0 0 2px rgba(0,0,0,0.5);'></div>", iconSize: [8, 8] }) }).addTo(s2Map);
|
||||
|
||||
siteSelect.addEventListener("change", function() {
|
||||
const sn = this.value;
|
||||
const seas = availableSiteSeasons[sn] || [];
|
||||
document.getElementById("seasonSelect").innerHTML = seas.map(s => `<option value="${s}">${s}</option>`).join("");
|
||||
document.getElementById("seasonSelect").value = seas[0] || "2024";
|
||||
setSiteSeason(sn, document.getElementById("seasonSelect").value);
|
||||
});
|
||||
document.getElementById("seasonSelect").addEventListener("change", function() {
|
||||
setSiteSeason(siteSelect.value, this.value);
|
||||
});
|
||||
document.getElementById("scenarioSelect").addEventListener("change", function() {
|
||||
const p = new URLSearchParams(location.search);
|
||||
p.set("scenario", this.value);
|
||||
window.location.search = p.toString();
|
||||
});
|
||||
|
||||
await setSiteSeason(initialSite, initialSeason);
|
||||
}
|
||||
|
||||
document.getElementById("dateSlider").addEventListener("input", function() {
|
||||
document.getElementById("dateDisplay").textContent = dateFromDays(parseInt(this.value));
|
||||
drawAllPlots();
|
||||
updateS2Imagery();
|
||||
});
|
||||
|
||||
function closestFilename(dateStr) {
|
||||
const target = new Date(dateStr);
|
||||
const withData = timeseries.filter(t => t.filename);
|
||||
if (!withData.length) return null;
|
||||
const closest = withData.reduce((c, t) =>
|
||||
Math.abs(new Date(t.date) - target) < Math.abs(new Date(c.date) - target) ? t : c
|
||||
);
|
||||
return closest.filename;
|
||||
}
|
||||
|
||||
function transformBounds(bbox, fromCRS) {
|
||||
const sw = proj4(fromCRS, "EPSG:4326", [bbox[0], bbox[1]]);
|
||||
const ne = proj4(fromCRS, "EPSG:4326", [bbox[2], bbox[3]]);
|
||||
return [[sw[1], sw[0]], [ne[1], ne[0]]];
|
||||
}
|
||||
|
||||
async function loadS2Geotiff(filename) {
|
||||
const path = `../data/${siteName}/${season}/${getBasePath()}/s2/${filename}`;
|
||||
const tiff = await GeoTIFF.fromArrayBuffer(await (await fetch(path)).arrayBuffer());
|
||||
const image = await tiff.getImage();
|
||||
const rasters = await image.readRasters();
|
||||
const width = image.getWidth(), height = image.getHeight();
|
||||
const bbox = image.getBoundingBox();
|
||||
const geoKeys = image.getGeoKeys();
|
||||
const crsCode = geoKeys.ProjectedCSTypeGeoKey ? `EPSG:${geoKeys.ProjectedCSTypeGeoKey}` :
|
||||
(geoKeys.GeographicTypeGeoKey !== 4326 ? `EPSG:${geoKeys.GeographicTypeGeoKey}` : "EPSG:4326");
|
||||
const [blue, green, red] = [0, 1, 2].map(i => Array.from(rasters[i]));
|
||||
const normalize = (arr) => {
|
||||
let min = Infinity, max = -Infinity;
|
||||
for (const v of arr) if (!isNaN(v) && v > 0) { min = Math.min(min, v); max = Math.max(max, v); }
|
||||
return arr.map(v => Math.max(0, Math.min(255, ((v - min) / (max - min || 1)) * 255)));
|
||||
};
|
||||
const [rN, gN, bN] = [red, green, blue].map(normalize);
|
||||
const canvas = Object.assign(document.createElement("canvas"), { width, height });
|
||||
const ctx = canvas.getContext("2d");
|
||||
ctx.imageSmoothingEnabled = false;
|
||||
const imgData = ctx.createImageData(width, height);
|
||||
for (let i = 0; i < rN.length; i++) {
|
||||
const idx = i * 4;
|
||||
if (rN[i] === 0 && gN[i] === 0 && bN[i] === 0) imgData.data[idx + 3] = 0;
|
||||
else { imgData.data[idx] = rN[i]; imgData.data[idx + 1] = gN[i]; imgData.data[idx + 2] = bN[i]; imgData.data[idx + 3] = 255; }
|
||||
}
|
||||
ctx.putImageData(imgData, 0, 0);
|
||||
const bounds = crsCode === "EPSG:4326" ? [[bbox[1], bbox[0]], [bbox[3], bbox[2]]] : transformBounds(bbox, crsCode);
|
||||
return { dataUrl: canvas.toDataURL(), bounds };
|
||||
}
|
||||
|
||||
async function updateS2Imagery() {
|
||||
const dateStr = dateFromDays(parseInt(document.getElementById("dateSlider").value));
|
||||
const filename = closestFilename(dateStr);
|
||||
if (!filename || !s2Map) {
|
||||
if (s2Overlay) { s2Map.removeLayer(s2Overlay); s2Overlay = null; }
|
||||
document.getElementById("s2rgbdate").textContent = "";
|
||||
return;
|
||||
}
|
||||
try {
|
||||
const { dataUrl, bounds } = await loadS2Geotiff(filename);
|
||||
if (s2Overlay) s2Map.removeLayer(s2Overlay);
|
||||
s2Overlay = L.imageOverlay(dataUrl, bounds, { opacity: 0.95 }).addTo(s2Map);
|
||||
s2Map.fitBounds(bounds);
|
||||
const d = filename.split("_")[0];
|
||||
document.getElementById("s2rgbdate").textContent = `${d.slice(0,4)}-${d.slice(4,6)}-${d.slice(6,8)}`;
|
||||
} catch (e) {
|
||||
if (s2Overlay) { s2Map.removeLayer(s2Overlay); s2Overlay = null; }
|
||||
document.getElementById("s2rgbdate").textContent = "";
|
||||
}
|
||||
}
|
||||
|
||||
init();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Add table
Add a link
Reference in a new issue