VHR example#

Let’s use EOReader with Very High Resolution data.

Imports#

import os

# EOReader
from eoreader.reader import Reader
from eoreader.bands import GREEN, NDVI, TIR_1, CLOUDS, HILLSHADE, to_str
from eoreader.env_vars import DEM_PATH

Create the logger#

# Create logger
import logging
from sertit import logs

logger = logging.getLogger("eoreader")
logs.init_logger(logger)

Open the VHR product#

Please be aware that EOReader will always work in UTM projection.
So if you give WGS84 data, EOReader will reproject the stacks and this can be time-consuming

# Set a DEM
os.environ[DEM_PATH] = os.path.join(
    "/home", "data", "DS2", "BASES_DE_DONNEES", "GLOBAL",
    "MERIT_Hydrologically_Adjusted_Elevations", "MERIT_DEM.vrt"
)

# Open your product
path = os.path.join("/home", "data", "DATA", "PRODS", "PLEIADES", "5547047101", "IMG_PHR1A_PMS_001")
reader = Reader()
prod = reader.open(path, remove_tmp=True)
prod

eoreader.PldProduct 'PHR1A_PMS_202005110231585_ORT_5547047101'
Attributes:
	condensed_name: 20200511T023158_PLD_ORT_PMS_5547047101
	path: /home/data/DATA/PRODS/PLEIADES/5547047101/IMG_PHR1A_PMS_001
	constellation: Pleiades
	sensor type: Optical
	product type: Ortho Single Image
	default pixel size: 0.5
	default resolution: 0.5
	acquisition datetime: 2020-05-11T02:31:58
	band mapping:
		BLUE: 3
		GREEN: 2
		RED: 1
		NIR: 4
		NARROW_NIR: 4
	needs extraction: False
	cloud cover: 0.0

# Plot the quicklook
prod.plot()

/opt/conda/lib/python3.10/site-packages/rasterio/__init__.py:304: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.
  dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)
/opt/conda/lib/python3.10/site-packages/rioxarray/_io.py:1132: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.
  warnings.warn(str(rio_warning.message), type(rio_warning.message))  # type: ignore
../_images/56b7699afc227cc6186f2bf44fbdef2752ae1c273eb087bc8bbb89f1870e9a34.png 
# Get the bands information
prod.bands

eoreader.SpectralBand 'RED'
Attributes:
	id: 1
	eoreader_name: RED
	common_name: red
	gsd (m): 0.5
	asset_role: reflectance
	Center wavelength (nm): 650.0
	Bandwidth (nm): 120.0
eoreader.SpectralBand 'GREEN'
Attributes:
	id: 2
	eoreader_name: GREEN
	common_name: green
	gsd (m): 0.5
	asset_role: reflectance
	Center wavelength (nm): 560.0
	Bandwidth (nm): 120.0
eoreader.SpectralBand 'BLUE'
Attributes:
	id: 3
	eoreader_name: BLUE
	common_name: blue
	gsd (m): 0.5
	asset_role: reflectance
	Center wavelength (nm): 495.0
	Bandwidth (nm): 70.0
eoreader.SpectralBand 'NIR'
Attributes:
	id: 4
	eoreader_name: NIR
	common_name: nir
	gsd (m): 0.5
	asset_role: reflectance
	Center wavelength (nm): 840.0
	Bandwidth (nm): 200.0
eoreader.SpectralBand 'NIR'
Attributes:
	id: 4
	eoreader_name: NIR
	common_name: nir
	gsd (m): 0.5
	asset_role: reflectance
	Center wavelength (nm): 840.0
	Bandwidth (nm): 200.0

print(f"Acquisition datetime: {prod.datetime}")
print(f"Condensed name: {prod.condensed_name}")

Acquisition datetime: 2020-05-11 02:31:58
Condensed name: 20200511T023158_PLD_ORT_PMS_5547047101

# Open here some more interesting geographical data: extent and footprint
extent = prod.extent()
footprint = prod.footprint()

base = extent.plot(color='cyan', edgecolor='black')
footprint.plot(ax=base, color='blue', edgecolor='black', alpha=0.5)

<Axes: >
../_images/f095a4abc9d548967541a94601c5146822d0f7c90a59df68072f286e57304786.png

Here, if you want to orthorectify or pansharpen your data manually, you can set your stack here.

prod.ortho_stack = "/path/to/ortho_stack.tif"

If you do not provide this stack, but you give a non-orthorectified product to EOReader (i.e. SEN or PRJ products for Pleiades), you must provide a DEM to orthorectify correctly the data.

⚠⚠⚠ DIMAP SEN products are orthorectified using RPCs and not the rigorous sensor model. A shift of several meters can occur. Please refer to this issue.

Load some bands#

# Select the bands you want to load
bands = [GREEN, NDVI, TIR_1, CLOUDS, HILLSHADE]

# Be sure they exist for Pleiades sensor
ok_bands = [band for band in bands if prod.has_band(band)]
print(to_str(ok_bands))  # Pleiades doesn't provide TIR and SHADOWS bands

['GREEN', 'NDVI', 'CLOUDS', 'HILLSHADE']

# Load those bands as a xarray.Dataset
band_ds = prod.load(ok_bands)
band_ds[GREEN]

2023-05-31 13:35:44,569 - [DEBUG] - Loading bands ['GREEN', 'NIR', 'RED']
2023-05-31 13:35:44,574 - [DEBUG] - Read GREEN
2023-05-31 13:35:44,603 - [INFO] - Warping DIM_PHR1A_PMS_202005110231585_ORT_5547047101 to UTM with a 0.5 m pixel size.
2023-05-31 13:35:44,674 - [DEBUG] - Reading warped GREEN.
2023-05-31 13:35:44,835 - [DEBUG] - Manage nodata for band GREEN
2023-05-31 13:35:45,714 - [DEBUG] - Converting GREEN to reflectance
2023-05-31 13:36:42,544 - [DEBUG] - Read NIR
2023-05-31 13:36:42,566 - [INFO] - Warping DIM_PHR1A_PMS_202005110231585_ORT_5547047101 to UTM with a 0.5 m pixel size.
2023-05-31 13:36:42,612 - [DEBUG] - Reading warped NIR.
2023-05-31 13:36:42,649 - [DEBUG] - Manage nodata for band NIR
2023-05-31 13:36:43,513 - [DEBUG] - Converting NIR to reflectance
2023-05-31 13:37:40,721 - [DEBUG] - Read RED
2023-05-31 13:37:40,744 - [INFO] - Warping DIM_PHR1A_PMS_202005110231585_ORT_5547047101 to UTM with a 0.5 m pixel size.
2023-05-31 13:37:40,796 - [DEBUG] - Reading warped RED.
2023-05-31 13:37:40,836 - [DEBUG] - Manage nodata for band RED
2023-05-31 13:37:41,626 - [DEBUG] - Converting RED to reflectance
2023-05-31 13:41:38,957 - [DEBUG] - Loading indices ['NDVI']
2023-05-31 13:41:41,627 - [DEBUG] - Loading DEM bands ['HILLSHADE']
2023-05-31 13:41:41,628 - [DEBUG] - Warping DEM for 20200511T023158_PLD_ORT_PMS_5547047101
2023-05-31 13:41:41,630 - [DEBUG] - Using DEM: /home/data/DS2/BASES_DE_DONNEES/GLOBAL/MERIT_Hydrologically_Adjusted_Elevations/MERIT_DEM.vrt
2023-05-31 13:41:41,751 - [DEBUG] - Computing hillshade DEM for PHR1A_PMS_202005110231585_ORT_5547047101
2023-05-31 13:42:04,800 - [DEBUG] - Loading Cloud bands ['CLOUDS']

<xarray.DataArray <SpectralBandNames.GREEN: 'GREEN'> (band: 1, y: 18124,
                                                      x: 16754)>
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
Coordinates:
  * x            (x) float64 7.024e+05 7.024e+05 ... 7.108e+05 7.108e+05
  * y            (y) float64 9.689e+06 9.689e+06 9.689e+06 ... 9.68e+06 9.68e+06
  * band         (band) int64 1
    spatial_ref  int64 0
Attributes: (12/13)
    long_name:         GREEN
    constellation:     Pleiades
    constellation_id:  PLD
    product_path:      /home/data/DATA/PRODS/PLEIADES/5547047101/IMG_PHR1A_PM...
    product_name:      PHR1A_PMS_202005110231585_ORT_5547047101
    product_filename:  IMG_PHR1A_PMS_001
    ...                ...
    product_type:      Ortho Single Image
    acquisition_date:  20200511T023158
    condensed_name:    20200511T023158_PLD_ORT_PMS_5547047101
    orbit_direction:   DESCENDING
    radiometry:        reflectance
    cloud_cover:       0.0
# The nan corresponds to the nodata you see on the footprint
# Plot a subsampled version
band_ds[GREEN][:, ::10, ::10].plot()

<matplotlib.collections.QuadMesh at 0x7f4b8c275960>
../_images/cfba2559f1b2fe3a65db30896d658a202e34831db2492385bebbfe72a9bc55c1.png 
# Plot a subsampled version
band_ds[NDVI][:, ::10, ::10].plot()

<matplotlib.collections.QuadMesh at 0x7f4b8c198d90>
../_images/0a8a7d351eee73a4cf0d355c13c09ef650cbcddf3b3d35b5cf9b2afe5f0dec35.png 
# Plot a subsampled version
band_ds[CLOUDS][:, ::10, ::10].plot()

<matplotlib.collections.QuadMesh at 0x7f4b84f7dc60>
../_images/c0377a9ca9c33170bf11260e9439a509f193695d8b0cd39dbf44764d78333020.png 
# Plot a subsampled version
band_ds[HILLSHADE][:, ::10, ::10].plot()

<matplotlib.collections.QuadMesh at 0x7f4b84e610c0>
../_images/f3c9598369ad8c0252f0ab48cbb1f1a52bfd1e8d7b7361e729bd3a2ae2864aa1.png

Stack some bands#

# You can also stack those bands
stack = prod.stack(ok_bands)

2023-05-31 13:42:41,277 - [DEBUG] - Loading bands ['GREEN', 'NIR', 'RED']
2023-05-31 13:42:41,278 - [DEBUG] - Read GREEN
2023-05-31 13:42:41,298 - [DEBUG] - Read NIR
2023-05-31 13:42:41,318 - [DEBUG] - Read RED
2023-05-31 13:43:13,254 - [DEBUG] - Loading indices ['NDVI']
2023-05-31 13:43:13,272 - [DEBUG] - Loading DEM bands ['HILLSHADE']
2023-05-31 13:43:13,273 - [DEBUG] - Already existing DEM for 20200511T023158_PLD_ORT_PMS_5547047101. Skipping process.
2023-05-31 13:43:13,274 - [DEBUG] - Already existing hillshade DEM for PHR1A_PMS_202005110231585_ORT_5547047101. Skipping process.
2023-05-31 13:43:13,293 - [DEBUG] - Loading Cloud bands ['CLOUDS']
2023-05-31 13:43:47,228 - [DEBUG] - Stacking

# Plot a subsampled version
import matplotlib.pyplot as plt

nrows = len(stack)
fig, axes = plt.subplots(nrows=nrows, figsize=(2 * nrows, 6 * nrows), subplot_kw={"box_aspect": 1})

for i in range(nrows):
    stack[i, ::10, ::10].plot(x="x", y="y", ax=axes[i])
../_images/618dc07ab242466eae68eecba5465bd29874fd317051d7378a8c88b0a74fb7ff.png