nghiadang
373 lines
13 KiB
Python
373 lines
13 KiB
Python
"""
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Create an interactive map for selecting satellite imagery and exporting image files.
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"""
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# Load modules
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import datacube
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import itertools
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import numpy as np
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import matplotlib.pyplot as plt
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from odc.ui import select_on_a_map
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from datacube.utils.geometry import CRS
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from datacube.utils import masking
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from skimage import exposure
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from ipyleaflet import (WMSLayer, basemaps, basemap_to_tiles)
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from traitlets import Unicode
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from deafrica_tools.spatial import reverse_geocode
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from deafrica_tools.dask import create_local_dask_cluster
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def select_region_app(date,
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satellites,
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size_limit=10000):
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"""
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An interactive app that allows the user to select a region from a
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map using imagery from Sentinel-2 and Landsat. The output of this
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function is used as the input to :func:`export_image_app` to export high-
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resolution satellite images.
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Last modified: September 2021
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Parameters
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----------
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date : str
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The exact date used to plot imagery on the interactive map
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(e.g. ``date='1988-01-01'``).
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satellites : str
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The satellite data to plot on the interactive map. The
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following options are supported:
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``'Landsat-9'``: data from the Landsat 9 satellite
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``'Landsat-8'``: data from the Landsat 8 satellite
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``'Landsat-7'``: data from the Landsat 7 satellite
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``'Landsat-5'``: data from the Landsat 5 satellite
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``'Sentinel-2'``: data from Sentinel-2A and Sentinel-2B
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``'Sentinel-2 geomedian'``: data from the Sentinel-2 annual geomedian
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size_limit : int, optional
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An optional size limit for the area selection in sq km.
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Defaults to 10000 sq km.
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Returns
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-------
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A dictionary containing:
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* 'geopolygon' (defining the area to export imagery from),
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* 'date' (date used to export imagery), and
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* 'satellites' (the satellites from which to extract imagery).
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These are passed to the :func:`export_image_app` function to export the image.
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"""
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########################
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# Select and load data #
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########################
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# Load DEA WMS
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class TimeWMSLayer(WMSLayer):
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time = Unicode('').tag(sync=True, o=True)
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# WMS layers
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wms_params = {
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'Landsat-9': 'ls9_sr',
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'Landsat-8': 'ls8_sr',
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'Landsat-7': 'ls7_sr',
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'Landsat-5': 'ls5_sr',
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'Sentinel-2': 's2_l2a',
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'Sentinel-2 geomedian': 'gm_s2_annual'
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}
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time_wms = TimeWMSLayer(url='https://ows.digitalearth.africa/',
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layers=wms_params[satellites],
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time=date,
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format='image/png',
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transparent=True,
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attribution='Digital Earth Africa')
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# Plot interactive map to select area
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basemap = basemap_to_tiles(basemaps.OpenStreetMap.Mapnik)
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geopolygon = select_on_a_map(height='1000px',
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layers=(
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basemap,
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time_wms,
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),
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center=(4, 20),
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zoom=4)
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# Test size of selected area
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area = geopolygon.to_crs(crs=CRS('epsg:6933')).area / 1000000
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if area > size_limit:
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print(f'Warning: Your selected area is {area:.00f} sq km. '
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f'Please select an area of less than {size_limit} sq km.'
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f'\nTo select a smaller area, re-run the cell '
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f'above and draw a new polygon.')
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else:
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return {'geopolygon': geopolygon,
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'date': date,
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'satellites': satellites}
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def export_image_app(geopolygon,
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date,
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satellites,
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style='True colour',
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resolution=None,
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vmin=0,
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vmax=2000,
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percentile_stretch=None,
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power=None,
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image_proc_funcs=None,
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output_format="jpg",
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standardise_name=False):
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"""
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Exports Digital Earth Africa satellite data as an image file
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based on the extent and time period selected using
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:func:`select_region_app`. The function supports Sentinel-2 and Landsat
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data, creating True and False colour images.
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By default, files are named using:
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``"<product> - <YYYY-MM-DD> - <site, state> - <description>.png"``
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Set ``standardise_name=True`` for a machine-readable name:
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``"<product>_<YYYY-MM-DD>_<site-state>_<description>.png"``
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Last modified: September 2021
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Parameters
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----------
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geopolygon : datacube.utils.geometry object
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A datacube geopolygon providing the spatial bounds used to load
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satellite data.
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date : str
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The exact date used to extract imagery
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(e.g. `date='1988-01-01'`).
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satellites : str
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The satellite data to be used to extract imagery. The
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following options are supported:
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``'Landsat-9'``: data from the Landsat 9 satellite
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``'Landsat-8'``: data from the Landsat 8 satellite
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``'Landsat-7'``: data from the Landsat 7 satellite
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``'Landsat-5'``: data from the Landsat 5 satellite
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``'Sentinel-2'``: data from Sentinel-2A and Sentinel-2B
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``'Sentinel-2 geomedian'``: data from the Sentinel-2 annual geomedian
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style : str, optional
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The style used to produce the image. Two options are currently
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supported:
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* ``'True colour'``: Creates a true colour image using the red,
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green and blue satellite bands
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* ``'False colour'``: Creates a false colour image using
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short-wave infrared, infrared and green satellite bands.
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The specific bands used vary between Landsat and Sentinel-2.
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resolution : tuple, optional
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The spatial resolution to load data. By default, the tool will
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automatically set the best possible resolution depending on the
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satellites selected (i.e 30 m for Landsat, 10 m for Sentinel-2).
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Increasing this (e.g. to ``resolution=(-100, 100)``) can be useful
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for loading large spatial extents.
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vmin, vmax : int or float
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The minimum and maximum surface reflectance values used to
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clip the resulting imagery to enhance contrast.
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percentile_stretch : tuple of floats, optional
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An tuple of two floats (between 0.00 and 1.00) that can be used
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to clip the imagery to based on percentiles to get more control
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over the brightness and contrast of the image. The default is
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``None``; ``(0.02, 0.98)`` is equivelent to ``robust=True``. If this
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parameter is used, ``vmin`` and ``vmax`` will have no effect.
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power : float, optional
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Raises imagery by a power to reduce bright features and
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enhance dark features. This can add extra definition over areas
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with extremely bright features like snow, beaches or salt pans.
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image_proc_funcs : list of funcs, optional
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An optional list containing functions that will be applied to
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the output image. This can include image processing functions
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such as increasing contrast, unsharp masking, saturation etc.
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The function should take AND return a `numpy.ndarray` with
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shape ``[y, x, bands]``. If your function has parameters, you
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can pass in custom values using a lambda function, e.g.:
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``[lambda x: skimage.filters.unsharp_mask(x, radius=5, amount=0.2)]``
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output_format : str, optional
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The output file format of the image. Valid options include ``'jpg'``
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and ``'png'``. Defaults to ``'jpg'``.
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standardise_name : bool, optional
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Whether to export the image file with a machine-readable
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file name (e.g. ``<product>_<YYYY-MM-DD>_<site-state>_<description>.png``)
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"""
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###########################
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# Set up satellite params #
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###########################
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sat_params = {
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'Landsat-9': {
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'products': ['ls9_sr'],
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'resolution': [-30, 30],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_1', 'nir', 'green']
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}
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},
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'Landsat-8': {
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'products': ['ls8_sr'],
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'resolution': [-30, 30],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_1', 'nir', 'green']
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}
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},
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'Landsat-7': {
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'products': ['ls7_sr'],
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'resolution': [-30, 30],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_1', 'nir', 'green']
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}
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},
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'Landsat-5': {
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'products': ['ls5_sr'],
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'resolution': [-30, 30],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_1', 'nir', 'green']
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}
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},
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'Sentinel-2': {
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'products': ['s2_l2a'],
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'resolution': [-10, 10],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_2', 'nir_1', 'green']
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}
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},
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'Sentinel-2 geomedian': {
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'products': ['gm_s2_annual'],
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'resolution': [-10, 10],
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'styles': {
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'True colour': ['red', 'green', 'blue'],
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'False colour': ['swir_2', 'nir_1', 'green']
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}
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},
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}
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#############
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# Load data #
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#############
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# Connect to datacube database
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dc = datacube.Datacube(app='Exporting_satellite_images')
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# Configure local dask cluster
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client = create_local_dask_cluster(return_client=True)
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# Create query after adjusting interval time to UTC by
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# adding a UTC offset of -10 hours.
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start_date = np.datetime64(date)
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query_params = {
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'time': (str(start_date)),
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'geopolygon': geopolygon
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}
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# Find matching datasets
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dss = [
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dc.find_datasets(product=i, **query_params)
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for i in sat_params[satellites]['products']
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]
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dss = list(itertools.chain.from_iterable(dss))
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# Get CRS and sensor
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crs = str(dss[0].crs)
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if satellites == 'Sentinel-2 geomedian':
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sensor = satellites
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else:
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sensor = dss[0].metadata_doc['properties']['eo:platform'].capitalize()
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sensor = sensor[0:-1].replace('_', '-') + sensor[-1].capitalize()
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# Use resolution if provided, otherwise use default
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if resolution:
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sat_params[satellites]['resolution'] = resolution
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load_params = {
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'output_crs': crs,
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'resolution': sat_params[satellites]['resolution'],
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'resampling': 'bilinear'
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}
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# Load data from datasets
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ds = dc.load(datasets=dss,
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measurements=sat_params[satellites]['styles'][style],
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group_by='solar_day',
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dask_chunks={
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'time': 1,
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'x': 3000,
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'y': 3000
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},
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**load_params,
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**query_params)
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ds = masking.mask_invalid_data(ds)
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rgb_array = ds.isel(time=0).to_array().values
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############
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# Plotting #
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############
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# Create unique file name
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centre_coords = geopolygon.centroid.coords[0][::-1]
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site = reverse_geocode(coords=centre_coords)
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fname = (f"{sensor} - {date} - {site} - {style}, "
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f"{load_params['resolution'][1]} m resolution.{output_format}")
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# Remove spaces and commas if requested
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if standardise_name:
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fname = fname.replace(' - ', '_').replace(', ',
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'-').replace(' ',
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'-').lower()
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print(
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f'\nExporting image to {fname}.\nThis may take several minutes to complete...'
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)
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# Convert to numpy array
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rgb_array = np.transpose(rgb_array, axes=[1, 2, 0])
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# If percentile stretch is supplied, calculate vmin and vmax
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# from percentiles
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if percentile_stretch:
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vmin, vmax = np.nanpercentile(rgb_array, percentile_stretch)
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# Raise by power to dampen bright features and enhance dark.
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# Raise vmin and vmax by same amount to ensure proper stretch
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if power:
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rgb_array = rgb_array**power
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vmin, vmax = vmin**power, vmax**power
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# Rescale/stretch imagery between vmin and vmax
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rgb_rescaled = exposure.rescale_intensity(rgb_array.astype(float),
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in_range=(vmin, vmax),
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out_range=(0.0, 1.0))
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# Apply image processing funcs
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if image_proc_funcs:
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for i, func in enumerate(image_proc_funcs):
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print(f'Applying custom function {i + 1}')
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rgb_rescaled = func(rgb_rescaled)
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# Plot RGB
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plt.imshow(rgb_rescaled)
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# Export to file
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plt.imsave(fname=fname, arr=rgb_rescaled, format=output_format)
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# Close dask client
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client.shutdown()
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print('Finished exporting image.')
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