VHR example¶
Let’s use EOReader with Very High Resolution data.
Warning:
import os
import glob
# First of all, we need some VHR data, let's use Pleiades data
path = glob.glob(os.path.join("/home", "data", "DATA", "PRODS", "PLEIADES", "5547047101", "IMG_PHR1A_PMS_001"))[0]
# Create logger
import logging
logger = logging.getLogger("eoreader")
logger.setLevel(logging.INFO)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# create formatter
formatter = logging.Formatter('%(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
from eoreader.reader import Reader
# Create the reader
eoreader = Reader()
# Open your product
prod = eoreader.open(path, remove_tmp=True)
print(f"Acquisition datetime: {prod.datetime}")
print(f"Condensed name: {prod.condensed_name}")
# 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
Acquisition datetime: 2020-05-11 02:31:58
Condensed name: 20200511T023158_PLD_ORT_PMS
from eoreader.bands import *
from eoreader.env_vars import DEM_PATH
# Here, if you want to orthorectify or pansharpen your data manually, you can set your stack here.
# If you do not provide this stack but you give a non-orthorectified product to EOReader
# (ie. SEN or PRJ products for Pleiades), you must provide a DEM to orthorectify correctly the data
# prod.ortho_stack = ""
os.environ[DEM_PATH] = os.path.join("/home", "data", "DS2", "BASES_DE_DONNEES", "GLOBAL", "MERIT_Hydrologically_Adjusted_Elevations", "MERIT_DEM.vrt")
# Open here some more interesting geographical data: extent
extent = prod.extent
extent.geometry.to_crs("EPSG:4326").iat[0] # Display
# Open here some more interesting geographical data: footprint
footprint = prod.footprint
footprint.geometry.to_crs("EPSG:4326").iat[0] # Display
# 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 dict of xarray.DataArray
band_dict = prod.load(ok_bands)
band_dict[GREEN]
Reprojecting band GREEN to UTM with a 0.5 m resolution.
Reprojecting band NIR to UTM with a 0.5 m resolution.
Reprojecting band RED to UTM with a 0.5 m resolution.
<xarray.DataArray '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: * band (band) int64 1 * 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 spatial_ref int64 0 Attributes: scale_factor: 1.0 add_offset: 0.0 long_name: GREEN sensor: Pleiades sensor_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
# The nan corresponds to the nodata you see on the footprint
%matplotlib inline
# Plot a subsampled version
band_dict[GREEN][:, ::10, ::10].plot()
<matplotlib.collections.QuadMesh at 0x7f625017ee80>
# Plot a subsampled version
band_dict[NDVI][:, ::10, ::10].plot()
<matplotlib.collections.QuadMesh at 0x7f62487e0970>
# Plot a subsampled version
band_dict[CLOUDS][:, ::10, ::10].plot()
<matplotlib.collections.QuadMesh at 0x7f6248722d60>
# Plot a subsampled version
band_dict[HILLSHADE][:, ::10, ::10].plot()
<matplotlib.collections.QuadMesh at 0x7f6248650910>
# You can also stack those bands
stack = prod.stack(ok_bands)
stack
# Error in plotting with a list
if "long_name" in stack.attrs:
stack.attrs.pop("long_name")
# 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])