# -*- coding: UTF-8 -*-
# Python
"""
04-07-2023
@author: jeremy auclair
Calculate NDVI images with xarray
"""
import os # for path management
import csv # open csv files
from pystac_client import Client # to send requests to copernicus server
from planetary_computer import sign_inplace # to get access to landsat data
from odc.stac import load # to download copernicus data
from odc.geo.geobox import GeoBox # to create geobox for data download
from fnmatch import fnmatch # for character string comparison
from typing import List, Union, Tuple # to declare variables
import xarray as xr # to manage dataset
import rioxarray # to manage dataset projections
import pandas as pd # to manage dataframes
import rasterio as rio # to open geotiff files
import numpy as np # vectorized math
import geopandas as gpd # to manage shapefile crs projections
from rasterio.enums import Resampling # reprojection algorithms
from datetime import datetime # manage dates
from dateutil.relativedelta import relativedelta # date math
from p_tqdm import p_map # for multiprocessing with progress bars
from dask.distributed import progress # for simple progress bar with dask
from psutil import cpu_count # to get number of physical cores available
from modspa_pixel.config.config import config # to import config file
from modspa_pixel.preprocessing.input_toolbox import product_str_to_datetime, read_product_info, get_band_paths
def download_ndvi_imagery(config_file: str, interp_chunk: dict = {'x': 400, 'y': 400, 'time': -1}, scaling: int = 255, acorvi_corr: int = 0) -> Union[str, Tuple[str, str]]:
"""
Use the new Copernicus data ecosystem or Planetarycomputer ecosystem to search and download
clipped and interpolated S2 or LandSat data, calculate NDVI and save it into a precube.
Arguments
=========
1. config_file: ``str``
path to configuration file
2. interp_chunk: ``dict`` ``default = {'x': 400, 'y': 400, 'time': -1}``
dictionnary containing the chunk size for
the xarray dask ndvi interpolation
3. scaling: ``int`` ``default = 255``
integer scaling to save NDVI data as integer values
4. acorvi_corr: ``int`` ``default = 0``
acorvi correction parameter to add to the red band
to adjust ndvi values for THEIA
Returns
=======
ndvi_precube_path: ``str``
path to save the ndvi pre-cube
"""
# Open config_file
config_params = config(config_file)
# Load parameters from config file
preferred_provider = config_params.preferred_provider
run_name = config_params.run_name
mode = config_params.mode
start_date = config_params.start_date
end_date = config_params.end_date
shapefile_path = config_params.shapefile_path
resolution = config_params.resolution
cloud_cover_limit = config_params.cloud_cover_limit
if preferred_provider == 'copernicus':
# Set api parameters
url = 'https://earth-search.aws.element84.com/v1'
collection = 'sentinel-2-l2a'
query = {'eo:cloud_cover' : {'lt' : cloud_cover_limit}}
modifier = None
# Set data parameters
red, nir, mask_name = 'red', 'nir', 'scl'
val1, val2 = 4, 5
# Set paths
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'SCIHUB' + os.sep + 'NDVI'
elif preferred_provider == 'usgs':
# Set api parameters
url = 'https://planetarycomputer.microsoft.com/api/stac/v1'
collection = 'landsat-c2-l2'
query = {'eo:cloud_cover' : {'lt' : cloud_cover_limit}, 'platform': {'in': ['landsat-8', 'landsat-9']}}
modifier = sign_inplace
# Set data parameters
red, nir, mask_name = 'red', 'nir08', 'qa_pixel'
val1, val2 = 21824, 21824
# Set paths
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'USGS' + os.sep + 'NDVI'
# Search parameters
bands = [red, nir, mask_name]
resampling_dict = {red: 'bilinear', nir: 'bilinear', mask_name: 'nearest'}
# Create save paths
ndvi_cube_path = save_dir + os.sep + run_name + os.sep + run_name + '_NDVI_' + 'pre' * (mode == 'parcel') + 'cube_' + start_date + '_' + end_date + '.nc' * (mode == 'pixel') + '.tif' * (mode == 'parcel')
dates_file = save_dir + os.sep + run_name + os.sep + run_name + '_NDVI_precube_' + start_date + '_' + end_date + '_dates.npy'
# Check if file exists and ndvi overwrite is false
if os.path.exists(ndvi_cube_path) and not config_params.ndvi_overwrite:
if mode == 'pixel':
return ndvi_cube_path
else:
return ndvi_cube_path, dates_file
# Open shapefile containing geometry
shapefile = gpd.read_file(shapefile_path)
bbox = shapefile.to_crs('EPSG:4326').geometry.total_bounds
# Change start and end date to better cover the chosen period
new_start_date = (datetime.strptime(start_date, '%Y-%m-%d') - relativedelta(months = 1)).strftime('%Y-%m-%d')
new_end_date = (datetime.strptime(end_date, '%Y-%m-%d') + relativedelta(months = 1)).strftime('%Y-%m-%d')
# Create request
client = Client.open(url, modifier = modifier)
search = client.search(collections = [collection], bbox = bbox, datetime = new_start_date + '/' + new_end_date, query = query, max_items = 200)
# Create geobox to better control data load geometry
geo_box = GeoBox.from_bbox(shapefile.geometry.total_bounds, shapefile.crs, resolution = resolution)
# Get data with required bands
data = load(search.items(), geobox = geo_box, groupby = 'solar_day', bands = bands, chunks = {}, resampling = resampling_dict)
if preferred_provider == 'usgs':
# Scale optical bands
data[[red, nir]] = data[[red, nir]] * 2.75e-5 - 0.2
# Create validity mask
mask = xr.where((data[mask_name] == val1) | (data[mask_name] == val2), 1, np.NaN)
# Save single red band as reference tif for later use in reprojection algorithms
ref = data[red].isel(time = 0).rio.write_crs(data.spatial_ref.values)
ref.rio.to_raster(save_dir + os.sep + run_name + os.sep + run_name + '_grid_reference.tif')
# Calculate NDVI
ndvi = ((data[nir] - data[red] - acorvi_corr) / (data[nir] + data[red] + acorvi_corr) * mask).to_dataset(name = 'NDVI')
# Convert timestamp to dates
ndvi['time'] = pd.to_datetime(pd.to_datetime(ndvi.time.values, format = '%Y-%m-%d').date)
dates = ndvi.time.values
# Mask NDVI
ndvi['NDVI'] = xr.where(ndvi.NDVI > 1, np.NaN, ndvi.NDVI)
ndvi['NDVI'] = xr.where(ndvi.NDVI < 0, 0, ndvi.NDVI)
# Save NDVI cube to netcdf
if mode == 'pixel':
# Interpolates on a daily frequency
daily_index = pd.date_range(start = dates[0], end = dates[-1], freq = 'D')
# Resample the dataset to a daily frequency and reindex with the new DateTimeIndex
ndvi = ndvi.resample(time = '1D').asfreq().reindex(time = daily_index).chunk(chunks = interp_chunk)
dates = pd.to_datetime(ndvi.time.values)
# Interpolate the dataset along the time dimension to fill nan values
ndvi = ndvi.interpolate_na(dim = 'time', method = 'linear', fill_value = 'extrapolate')
# Remove extra dates, only keep selected window
ndvi = ndvi.sel({'time': slice(config_params.start_date, config_params.end_date)})
# TODO: understand why dimensions are not in the right order anymore
# Reorder dimensions
ndvi = ndvi[['time', 'y', 'x', 'NDVI']]
# Scale ndvi
ndvi['NDVI'] = (ndvi.NDVI * scaling).round()
# Write attributes
ndvi['NDVI'].attrs['units'] = 'None'
ndvi['NDVI'].attrs['standard_name'] = 'NDVI'
ndvi['NDVI'].attrs['description'] = 'Normalized difference Vegetation Index (of the near infrared and red band). A value of one is a high vegetation presence.'
ndvi['NDVI'].attrs['scale factor'] = str(scaling)
# Write transform
ndvi.rio.write_crs(ref.rio.crs, inplace = True)
ndvi.rio.write_transform(inplace = True)
ndvi = ndvi.set_coords('spatial_ref')
# Save NDVI cube to netcdf
if ndvi.dims['y'] > 1000 and ndvi.dims['x'] > 1000:
file_chunksize = (1, interp_chunk['y'], interp_chunk['x'])
else:
file_chunksize = (1, ndvi.dims['y'], ndvi.dims['x'])
write_job = ndvi.to_netcdf(ndvi_cube_path, encoding = {"NDVI": {"dtype": "u1", "_FillValue": 0, "chunksizes": file_chunksize}}, compute = False)
write_job = write_job.persist()
progress(write_job)
return ndvi_cube_path
else:
# Drop dates with only NaNs
ndvi = ndvi.dropna(dim = 'time', how = 'all')
# Scale
ndvi['NDVI'] = ((ndvi.NDVI + 1/scaling) * (scaling - 1)).round()
# Save dates as string formats in numpy file
dates = pd.to_datetime(ndvi.time.values).strftime('%Y-%m-%d')
np.save(dates_file, dates, allow_pickle = True)
# Write crs
ndvi.rio.write_crs(ref.rio.crs, inplace = True)
# Write nodata
ndvi.NDVI.rio.write_nodata(0, inplace = True)
# Replace NaNs with 0
ndvi = ndvi.fillna(0)
# Save ndvi cube to multiband geotiff
ndvi.NDVI.rio.to_raster(ndvi_cube_path, dtype = np.uint8)
return ndvi_cube_path, dates_file
def calculate_ndvi(extracted_paths: Union[List[str], str], config_file: str, calc_chunk: dict = {'x': 400, 'y': 400, 'time': -1}, interp_chunk: dict = {'x': 400, 'y': 400, 'time': -1}, scaling: int = 255, acorvi_corr: int = 0) -> str:
"""
Calculate ndvi images in a xarray dataset, interpolate is to a daily time step (a data cube) and save it.
ndvi values are scaled and saved as ``uint8`` (0 to 255).
.. warning:: only 10 and 20 meters currently supported
Arguments
=========
1. extracted_paths: ``Union[List[str], str]``
list of paths to extracted sentinel-2 products
or path to ``csv``` file containing those paths
2. config_file: ``str``
path to configuration file
3. calc_chunk: ``dict`` ``default = {'x': 400, 'y': 400, 'time': -1}``
dictionnary containing the chunk size for
the xarray dask ndvi calculation
4. interp_chunk: ``dict`` ``default = {'x': 400, 'y': 400, 'time': -1}``
dictionnary containing the chunk size for
the xarray dask ndvi interpolation
5. scaling: ``int`` ``default = 255``
integer scaling to save NDVI data as integer values
6. acorvi_corr: ``int`` ``default = 0``
acorvi correction parameter to add to the red band
to adjust ndvi values
Returns
=======
1. ndvi_path: ``str``
path to save the ndvi cube
"""
# Open config_file
config_params = config(config_file)
print('\nCalculating NDVI cube and saving it...\n')
# Load parameters from config file
boundary_shapefile_path = config_params.shapefile_path
start_date = config_params.start_date
end_date = config_params.end_date
mode = config_params.mode
run_name = config_params.run_name
resolution = config_params.resolution
preferred_provider = config_params.preferred_provider
if preferred_provider == 'copernicus':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'SCIHUB' + os.sep + 'NDVI'
elif preferred_provider == 'theia':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'THEIA' + os.sep + 'NDVI'
elif preferred_provider == 'usgs':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'USGS' + os.sep + 'NDVI'
# If a file name is provided instead of a list of paths, load the csv file that contains the list of paths
if type(extracted_paths) == str:
with open(extracted_paths, 'r') as file:
extracted_paths = []
csvreader = csv.reader(file, delimiter='\n')
for row in csvreader:
extracted_paths.append(row[0])
# Get list of paths to red, nir and mask images
red_paths, nir_paths, mask_paths = get_band_paths(config_params, extracted_paths)
dates = [product_str_to_datetime(prod) for prod in red_paths]
# Get crs
with rio.open(red_paths[0]) as temp:
crs = temp.crs
# Open shapefile to clip the dataset
shapefile = gpd.read_file(boundary_shapefile_path)
shapefile = shapefile.to_crs(crs)
bounds = shapefile.total_bounds
# Open datasets with xarray
red = xr.open_mfdataset(red_paths, combine = 'nested', concat_dim = 'time', chunks = calc_chunk, parallel = True).squeeze(dim = ['band'], drop = True).rename({'band_data': 'red'}).sortby('y', ascending = False)
nir = xr.open_mfdataset(nir_paths, combine = 'nested', concat_dim = 'time', chunks = calc_chunk, parallel = True).squeeze(dim = ['band'], drop = True).rename({'band_data': 'nir'}).sortby('y', ascending = False)
mask = xr.open_mfdataset(mask_paths, combine = 'nested', concat_dim = 'time', chunks = calc_chunk, parallel = True).squeeze(dim = ['band'], drop = True).rename({'band_data': 'mask'}).sortby('y', ascending = False)
# Get masking condition and resolution management based on provider
if preferred_provider == 'copernicus':
# Original resolution in meters
base_resolution = 10
# Select bands
red = red.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
nir = nir.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
mask = mask.interp(x = red.coords['x'], y = red.coords['y'], method = 'nearest')
mask = xr.where((mask == 4) | (mask == 5), 1, np.NaN).astype(np.float32)
elif preferred_provider == 'theia':
# Original resolution in meters
base_resolution = 10
# Select bands
red = red.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
nir = nir.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
mask = mask.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
red = xr.where(red == -10000, np.nan, red)
nir = xr.where(nir == -10000, np.nan, nir)
mask = xr.where((mask == 0), 1, np.NaN).astype(np.float32)
# Rescale optical data for LandSat
elif preferred_provider == 'usgs':
# Original resolution in meters
base_resolution = 30
# Select bands
red = red.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1])) * 2.75e-5 - 0.2
nir = nir.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1])) * 2.75e-5 - 0.2
mask = mask.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
mask = xr.where((mask == 21824), 1, np.NaN).astype(np.float32)
# Set time coordinate
red['time'] = pd.to_datetime(dates)
nir['time'] = pd.to_datetime(dates)
mask['time'] = pd.to_datetime(dates)
# Sort by date
red = red.sortby(variables = 'time')
nir = nir.sortby(variables = 'time')
mask = mask.sortby(variables = 'time')
dates.sort()
# Save single red band as reference tif for later use in reprojection algorithms
ref = xr.open_dataset(red_paths[0]).squeeze(dim = ['band'], drop = True).rename({'band_data': 'red'}).sortby('y', ascending = False).sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1])).rio.write_crs(crs)
ref = ref.rio.reproject(ref.rio.crs, shape = (int(ref.sizes['y'] * base_resolution / resolution), int(ref.sizes['x'] * base_resolution / resolution)), resampling = Resampling.bilinear, nodata = np.NaN)
# Recalculate transform
ref.rio.write_transform(inplace = True)
ref = ref.set_coords('spatial_ref')
ref.rio.to_raster(save_dir + os.sep + run_name + os.sep + run_name + '_grid_reference.tif')
# Create save path
ndvi_cube_path = save_dir + os.sep + run_name + os.sep + run_name + '_NDVI_' + 'pre' * (mode == 'parcel') + 'cube_' + start_date + '_' + end_date + '.nc' * (mode == 'pixel') + '.tif' * (mode == 'parcel')
dates_file = save_dir + os.sep + run_name + os.sep + run_name + '_NDVI_precube_' + start_date + '_' + end_date + '_dates.npy'
# Check if file exists and ndvi overwrite is false
if os.path.exists(ndvi_cube_path) and not config_params.ndvi_overwrite:
if mode == 'pixel':
return ndvi_cube_path
else:
return ndvi_cube_path, dates_file
# Change mask resolution
if resolution != base_resolution:
mask = mask.interp(x = ref.coords['x'], y = ref.coords['y'], method = 'nearest')
# Create ndvi dataset and calculate ndvi
ndvi = ((nir.nir - red.red - acorvi_corr) / (nir.nir + red.red + acorvi_corr)).to_dataset(name = 'NDVI')
# Reproject NDVI
if resolution != base_resolution:
ndvi = ndvi.interp(x = ref.coords['x'], y = ref.coords['y'], method = 'linear')
# Mask NDVI
ndvi['NDVI'] = ndvi.NDVI * mask.mask
ndvi['NDVI'] = xr.where(ndvi.NDVI > 1, np.NaN, ndvi.NDVI)
ndvi['NDVI'] = xr.where(ndvi.NDVI < 0, 0, ndvi.NDVI)
del red, nir, mask
# Drop dates with only NaNs
ndvi = ndvi.dropna(dim = 'time', how = 'all')
# Recalculate transform
ndvi.rio.write_transform(inplace = True)
ndvi = ndvi.set_coords('spatial_ref')
if mode == 'pixel':
# Interpolates on a daily frequency
daily_index = pd.date_range(start = dates[0], end = dates[-1], freq = 'D')
# Resample the dataset to a daily frequency and reindex with the new DateTimeIndex
ndvi = ndvi.resample(time = '1D').asfreq().reindex(time = daily_index).chunk(chunks = interp_chunk)
dates = pd.to_datetime(ndvi.time.values)
# Interpolate the dataset along the time dimension to fill nan values
ndvi = ndvi.interpolate_na(dim = 'time', method = 'linear', fill_value = 'extrapolate')
# Remove extra dates, only keep selected window
ndvi = ndvi.sel({'time': slice(config_params.start_date, config_params.end_date)})
# TODO: understand why dimensions are not in the right order anymore
# Reorder dimensions
ndvi = ndvi[['time', 'y', 'x', 'NDVI']]
# Scale ndvi
ndvi['NDVI'] = (ndvi.NDVI * scaling).round()
# Write attributes
ndvi['NDVI'].attrs['units'] = 'None'
ndvi['NDVI'].attrs['standard_name'] = 'NDVI'
ndvi['NDVI'].attrs['description'] = 'Normalized difference Vegetation Index (of the near infrared and red band). A value of one is a high vegetation presence.'
ndvi['NDVI'].attrs['scale factor'] = str(scaling)
# Save NDVI cube to netcdf
if ndvi.dims['y'] > 1000 and ndvi.dims['x'] > 1000:
file_chunksize = (1, interp_chunk['y'], interp_chunk['x'])
else:
file_chunksize = (1, ndvi.dims['y'], ndvi.dims['x'])
write_job = ndvi.to_netcdf(ndvi_cube_path, encoding = {"NDVI": {"dtype": "u1", "_FillValue": 0, "chunksizes": file_chunksize}}, compute = False)
write_job = write_job.persist()
progress(write_job)
ndvi.close()
return ndvi_cube_path
else:
# Scale ndvi
ndvi['NDVI'] = (ndvi.NDVI * scaling).round()
# Write attributes
ndvi['NDVI'].attrs['units'] = 'None'
ndvi['NDVI'].attrs['standard_name'] = 'NDVI'
ndvi['NDVI'].attrs['description'] = 'Normalized difference Vegetation Index (of the near infrared and red band). A value of one is a high vegetation presence.'
ndvi['NDVI'].attrs['scale factor'] = str(scaling)
# Save dates as string formats in numpy file
dates = pd.to_datetime(ndvi.time.values).strftime('%Y-%m-%d')
np.save(dates_file, dates, allow_pickle = True)
# Write crs
ndvi.rio.write_crs(crs, inplace = True)
# Write nodata
ndvi.NDVI.rio.write_nodata(0, inplace = True)
# Replace NaNs with 0
ndvi = ndvi.fillna(0)
# Save ndvi cube to multiband geotiff
write_job = ndvi.NDVI.rio.to_raster(ndvi_cube_path, dtype = np.uint8, compute = False)
write_job = write_job.persist()
progress(write_job)
return ndvi_cube_path, dates_file
def interpolate_ndvi(ndvi_path: str, config_file: str, chunk_size: dict = {'x': 400, 'y': 400, 'time': -1}, scaling: int = 255,) -> str:
"""
Interpolate the ndvi cube to a daily frequency between the
desired dates defined in the ``json`` config file. The extra
month of data downloaded is used for a better interpolation,
it is then discarded and the final NDVI cube has the dates
defined in the config file.
Arguments
=========
1. ndvi_path: ``str``
path to ndvi pre-cube
2. config_file: ``str``
path to ``json`` config file
3. chunk_size: ``dict`` ``default = {'x': 400, 'y': 400, 'time': -1}``
chunk size to use by dask for calculation,
``'time' = -1`` means the chunk has the whole
time dimension in it. The Dataset can't be
divided along the time axis for interpolation.
4. scaling: ``int`` ``default = 255``
integer scaling to save NDVI data as integer values
Returns
=======
``None``
"""
# Open config_file
config_params = config(config_file)
# Load parameters from config file
run_name = config_params.run_name
if config_params.preferred_provider == 'copernicus':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'SCIHUB' + os.sep + 'NDVI'
elif config_params.preferred_provider == 'theia':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'THEIA' + os.sep + 'NDVI'
elif config_params.preferred_provider == 'usgs':
save_dir = config_params.data_path + os.sep + 'IMAGERY' + os.sep + 'USGS' + os.sep + 'NDVI'
# Create save path
ndvi_cube_path = save_dir + os.sep + run_name + os.sep + run_name + '_NDVI_cube_' + config_params.start_date + '_' + config_params.end_date + '.nc'
# Check if file exists and ndvi overwrite is false
if os.path.exists(ndvi_cube_path) and not config_params.ndvi_overwrite:
return ndvi_cube_path
# Open NDVI pre-cube
ndvi = xr.open_dataset(ndvi_path, chunks = chunk_size)
# Get dates of NDVI precube
dates = ndvi.time.values
# Get Spatial reference
spatial_ref = ndvi.spatial_ref.load()
# Interpolates on a daily frequency
daily_index = pd.date_range(start = dates[0], end = dates[-1], freq = 'D')
# Resample the dataset to a daily frequency and reindex with the new DateTimeIndex
ndvi = ndvi.resample(time = '1D').asfreq().reindex(time = daily_index)
dates = pd.to_datetime(ndvi.time.values)
# Interpolate the dataset along the time dimension to fill nan values
ndvi = ndvi.interpolate_na(dim = 'time', method = 'linear', fill_value = 'extrapolate').round(decimals = 0)
# Remove extra dates, only keep selected window
ndvi = ndvi.sel({'time': slice(config_params.start_date, config_params.end_date)})
# Rescale data
ndvi = (ndvi * (scaling / (scaling - 1)) - 1).round()
# Set negative values as 0
ndvi = xr.where(ndvi < 0, 0, ndvi.NDVI)
# Set values above 255 (ndvi > 1) as 255 (ndvi = 1)
ndvi = xr.where(ndvi > scaling, scaling, ndvi.NDVI)
# Rewrite spatial reference
ndvi['spatial_ref'] = spatial_ref
# Set file chunk size
if ndvi.dims['y'] > 1000 and ndvi.dims['x'] > 1000:
file_chunksize = (1, chunk_size['y'], chunk_size['x'])
else:
file_chunksize = (1, ndvi.dims['y'], ndvi.dims['x'])
# Save NDVI cube to netcdf
write_job = ndvi.to_netcdf(ndvi_cube_path, encoding = {"NDVI": {"dtype": "u1", "_FillValue": 0, "chunksizes": file_chunksize}}, compute = False)
write_job = write_job.persist()
progress(write_job)
ndvi.close()
return ndvi_cube_path
def write_geotiff(path: str, data: np.ndarray, transform: tuple, projection: str, scaling: int = 255) -> None:
"""
Writes a GeoTiff image using ``rasterio``. Takes an array and georeferencing data (obtained by ``rasterio``
on an image with same georeferencing) to write a well formatted image. Data format: ``uint8``
Arguments
=========
1. path: ``str``
true path of file to save data in (path = path + save name)
2. data: ``np.ndarray``
numpy array containging the data
3. geotransform: ``tuple``
tuple containing the geo-transform data
4. projection: ``str``
string containing the epsg projection data
5. scaling: ``int`` ``default = 255``
scaling factor for saving NDVI images as integer arrays
Returns
=======
``None``
"""
# Apply scaling
data = np.round((data + np.float32(1/scaling)) * (scaling - 1), 1)
# Write NDVI image with rasterio
with rio.open(path, mode = "w", driver = "GTiff", height = data.shape[0], width = data.shape[1], count = 1, dtype = np.uint8, crs = projection, transform = transform, nodata = 0) as new_dataset:
new_dataset.write(data, 1)
return None
def calculate_ndvi_image(args: tuple) -> str:
"""
Opens zip file of the given product to extract the red, near-infrared and mask bands (without extracting
the whole archive) and calculate the ndvi, apply a mask to keep only clear land data (no clouds, no snow,
no water, no unclassified or erroneous pixel). This array is then saved in a GeoTiff image with
the parent name file and ``'_NDVI.tif'`` extension. If overwrite is false, already existing files won't be
calculated and saved again (default value is ``false``).
This function is called in the calculate_ndvi function as a subprocess to allow multiprocessing. Variables
that have served their purpose are directly deleted to reduce memory usage.
Arguments (packed in args: ``tuple``)
=====================================
1. product_path: ``str``
path to the product to extract for ndvi calculation
2. save_dir: ``str``
directory in which to save ndvi images
3. shapefile_path: ``str``
path to the shapefile (``.shp``) for which the data is calculated. Used to clip
satellite imagery
4. overwrite: ``bool``
boolean to choose to rewrite already existing files
4. ACORVI_correction: ``int``
correction parameter to apply on the red band for stability of NDVI values
Returns
=======
1. ndvi_path: ``str``
path to the saved ndvi image
"""
# Unpack arguments
product_path, save_dir, shapefile_path, overwrite, ACORVI_correction = args
# Check provider
_, _, provider, _ = read_product_info(product_path)
# To ignore numpy warning when dividing by NaN
np.seterr(invalid='ignore', divide='ignore')
# Define offset for copernicus data
offset = 0
# Create file name
file_name = os.path.basename(product_path)
# file_name = os.path.splitext(file_name)[0]
save_name = save_dir + os.sep + file_name + '_NDVI.tif'
# Check if file is already written. If override is false, no need to calculate and write ndvi again.
# If override is true: ndvi is calculated and saved again.
if not os.path.exists(save_name) or overwrite: # File does not exist or overwrite is true
pass
else:
# Collect save path for return
return save_name
# Look for desired images in the directories
if provider == 'copernicus':
for f in os.listdir(product_path):
if fnmatch(f, '*_B04_10m.jp2'): # Red band
red_file = os.path.join(product_path, f)
elif fnmatch(f, '*_B08_10m.jp2'): # Near infrared band
nir_file = os.path.join(product_path, f)
elif fnmatch(f, '*_SCL_20m.jp2'): # Scene classification for mask
classif_file = os.path.join(product_path, f)
elif provider == 'theia':
for f in os.listdir(product_path):
if fnmatch(f, '*_FRE_B4.tif'): # Red band
red_file = os.path.join(product_path, f)
elif fnmatch(f, '*_FRE_B8.tif'): # Near infrared band
nir_file = os.path.join(product_path, f)
elif fnmatch(f, '*_MG2_R1.tif'): # Scene classification for mask
classif_file = os.path.join(product_path, f)
elif provider == 'usgs':
for f in os.listdir(product_path):
if fnmatch(f, '*_SR_B4.TIF'): # Red band
red_file = os.path.join(product_path, f)
elif fnmatch(f, '*_SR_B5.TIF'): # Near infrared band
nir_file = os.path.join(product_path, f)
elif fnmatch(f, '*_QA_PIXEL.TIF'): # Scene classification for mask
classif_file = os.path.join(product_path, f)
# Read bands, geometry and projection information
red_band = xr.open_dataarray(red_file) # read red band
projection = red_band.rio.crs
del red_file
# Open shapefile to clip the dataset
shapefile = gpd.read_file(shapefile_path)
shapefile = shapefile.to_crs(projection)
bounds = shapefile.total_bounds
del shapefile
red_band = red_band.sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1]))
transorm = red_band.rio.transform(recalc = True)
nir_band = xr.open_dataarray(nir_file).sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1])) # read nir band
del nir_file
# Read array data
red_data = red_band.values + ACORVI_correction
nir_data = nir_band.values
nir_band.close()
# Adjust data for specific providers
if provider == 'theia':
red_data = np.where((red_data == -10000), np.float32(np.NaN), red_data)
nir_data = np.where((nir_data == -10000), np.float32(np.NaN), nir_data)
elif provider == 'usgs':
red_data = red_data * 2.75e-5 - 0.2
nir_data = nir_data * 2.75e-5 - 0.2
# Calculate ndvi
ndvi_data = np.divide((nir_data - red_data), (nir_data + red_data + 2*offset), dtype = np.float32)
del red_data, nir_data
# Read classif data
classif_band = xr.open_dataarray(classif_file).sel(x = slice(bounds[0], bounds[2]), y = slice(bounds[3], bounds[1])) # read classif band
del classif_file
# Adjust mask based on provider
if provider == 'copernicus':
classif_band = xr.where((classif_band == 4) | (classif_band == 5), 1, np.NaN).interp(x = red_band.coords['x'], y = red_band.coords['y'], method = 'nearest')
elif provider == 'theia':
classif_band = xr.where((classif_band == 0), 1, np.NaN)
elif provider == 'usgs':
classif_band = xr.where((classif_band == 21824), 1, np.NaN)
# Read array data
binary_mask = classif_band.values
classif_band.close()
red_band.close()
# Apply mask
np.multiply(ndvi_data, binary_mask, out=ndvi_data, dtype = np.float32)
del binary_mask
# Clip out of range data
np.minimum(ndvi_data, 1, out = ndvi_data, dtype = np.float32)
np.maximum(ndvi_data, 0, out = ndvi_data, dtype = np.float32)
# Write image
write_geotiff(save_name, ndvi_data[0], transorm, projection)
del ndvi_data, transorm, projection
return save_name
def calculate_ndvi_parcel(ndvi_path: Union[List[str], str], save_dir: str, save_path: str, shapefile_path: str, overwrite: bool = False, max_cpu: int = 4, ACORVI_correction: int = 0) -> List[str]:
"""
Opens the red, near infrared and scene classification to calculate the ndvi, apply a mask to keep only clear land data (no clouds, no snow,
no water, no unclassified or erroneous pixel). This array is then saved in a GeoTiff image with
the parrent name file and ``'_NDVI.tif'`` extension. If overwrite is false, already existing fils won't be
calculated and saved again (default value is ``false``).
This function calls the calculate_ndvi_image function as a subprocess to allow multiprocessing. A modified
version of the ``tqdm`` module (``p_tqdm``) is used for progress bars.
Arguments
=========
1. ndvi_path: ``list[str]`` or ``str``
list of paths to the products to extract for ndvi calculation or path to a ``csv`` file that contains this list
2. save_dir: ``str``
directory in which to save ndvi images
3. save_path : ``str``
path to a csv file containing the paths to the saved ndvi images
4. shapefile_path: ``str``
path to the shapefile (``.shp``) for which the data is calculated. Used to clip
satellite imagery
5. overwrite: ``bool`` ``default = False``
boolean to choose to rewrite already existing files
5. max_cpu: ``int`` `default = 4`
max number of CPUs that the pool can use, if max_cpu is bigger than available CPUs, the pool only uses availables CPUs
6. ACORVI_correction: ``int`` ``default = 500``
correction parameter to apply on the red band for stability of NDVI values
Returns
=======
1. ndvi_path: ``list[str]``
list of paths to the saved ndvi images
"""
# If a file name is provided instead of a list of paths, load the csv file that contains the list of paths
if type(ndvi_path) == str:
with open(ndvi_path, 'r') as file:
ndvi_path = []
csvreader = csv.reader(file, delimiter='\n')
for row in csvreader:
ndvi_path.append(row[0])
ndvi_path = [] # Where saved image paths will be stored
# Prepare arguments for multiprocessing
args = [(product, save_dir, shapefile_path, overwrite, ACORVI_correction) for product in ndvi_path]
# Get number of CPU cores and limit max value (working on a cluster requires os.sched_getaffinity to get true number of available CPUs,
# this is not true on a "personnal" computer, hence the use of the min function)
try:
nb_cores = min([max_cpu, cpu_count(logical = False), len(os.sched_getaffinity(0))])
except:
nb_cores = min([max_cpu, cpu_count(logical = False)]) # os.sched_getaffinity won't work on windows
print('\nStarting NDVI calculations with %d cores for %d images...\n' %(nb_cores, len(ndvi_path)))
# Start pool and get results
results = p_map(calculate_ndvi_image, args, **{"num_cpus": nb_cores})
# Collect results and sort them
for result in results:
ndvi_path.append(result)
ndvi_path.sort()
# Save ndvi paths as a csv file
with open(save_path, 'w', newline='') as f:
# using csv.writer method from CSV package
write = csv.writer(f)
for ndvi_image in ndvi_path:
write.writerow([ndvi_image])
return ndvi_path