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nghiadang
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"""
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Loading and interacting with data in the change filmstrips notebook,
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inside the Real_world_examples folder.
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"""
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# Load modules
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import os
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import dask
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import datacube
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import warnings
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import numpy as np
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import pandas as pd
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import xarray as xr
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import matplotlib.pyplot as plt
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from odc.algo import geomedian_with_mads
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from odc.ui import select_on_a_map
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from dask.utils import parse_bytes
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from datacube.utils.geometry import CRS, assign_crs
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from datacube.utils.rio import configure_s3_access
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from datacube.utils.dask import start_local_dask
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from ipyleaflet import basemaps, basemap_to_tiles
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# Load utility functions
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from deafrica_tools.datahandling import load_ard, mostcommon_crs
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from deafrica_tools.dask import create_local_dask_cluster
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def run_filmstrip_app(
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output_name,
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time_range,
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time_step,
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tide_range=(0.0, 1.0),
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resolution=(-30, 30),
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max_cloud=0.5,
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ls7_slc_off=False,
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size_limit=10000,
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):
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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, then load Digital Earth Africa Landsat data and combine it
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using the geometric median ("geomedian") statistic to reveal the
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median or 'typical' appearance of the landscape for a series of
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time periods.
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The results for each time period are combined into a 'filmstrip'
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plot which visualises how the landscape has changed in appearance
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across time, with a 'change heatmap' panel highlighting potential
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areas of greatest change.
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For coastal applications, the analysis can be customised to select
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only satellite images obtained during a specific tidal range
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(e.g. low, average or high tide).
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Last modified: April 2020
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Parameters
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----------
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output_name : str
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A name that will be used to name the output filmstrip plot file.
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time_range : tuple
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A tuple giving the date range to analyse
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(e.g. `time_range = ('1988-01-01', '2017-12-31')`).
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time_step : dict
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This parameter sets the length of the time periods to compare
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(e.g. `time_step = {'years': 5}` will generate one filmstrip
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plot for every five years of data; `time_step = {'months': 18}`
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will generate one plot for each 18 month period etc. Time
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periods are counted from the first value given in `time_range`.
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tide_range : tuple, optional
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An optional parameter that can be used to generate filmstrip
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plots based on specific ocean tide conditions. This can be
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valuable for analysing change consistently along the coast.
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For example, `tide_range = (0.0, 0.2)` will select only
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satellite images acquired at the lowest 20% of tides;
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`tide_range = (0.8, 1.0)` will select images from the highest
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20% of tides. The default is `tide_range = (0.0, 1.0)` which
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will select all images regardless of tide.
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resolution : tuple, optional
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The spatial resolution to load data. The default is
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`resolution = (-30, 30)`, which will load data at 30 m pixel
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resolution. Increasing this (e.g. to `resolution = (-100, 100)`)
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can be useful for loading large spatial extents.
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max_cloud : float, optional
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This parameter can be used to exclude satellite images with
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excessive cloud. The default is `0.5`, which will keep all images
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with less than 50% cloud.
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ls7_slc_off : bool, optional
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An optional boolean indicating whether to include data from
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after the Landsat 7 SLC failure (i.e. SLC-off). Defaults to
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False, which removes all Landsat 7 observations > May 31 2003.
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size_limit : int, optional
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An optional integer (in hectares) specifying the size limit
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for the data query. Queries larger than this size will receive
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a warning that he data query is too large (and may
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therefore result in memory errors).
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Returns
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-------
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ds_geomedian : xarray Dataset
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An xarray dataset containing geomedian composites for each
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timestep in the analysis.
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"""
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########################
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# Select and load data #
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########################
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# Define centre_coords as a global variable
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global centre_coords
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# Test if centre_coords is in the global namespace;
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# use default value if it isn't
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if "centre_coords" not in globals():
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centre_coords = (6.587292, 1.532833)
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# Plot interactive map to select area
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basemap = basemap_to_tiles(basemaps.Esri.WorldImagery)
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geopolygon = select_on_a_map(height="600px",
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layers=(basemap,),
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center=centre_coords,
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zoom=14)
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# Set centre coords based on most recent selection to re-focus
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# subsequent data selections
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centre_coords = geopolygon.centroid.points[0][::-1]
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# Test size of selected area
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msq_per_hectare = 10000
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area = geopolygon.to_crs(crs=CRS("epsg:6933")).area / msq_per_hectare
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radius = np.round(np.sqrt(size_limit), 1)
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if area > size_limit:
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print(f"Warning: Your selected area is {area:.00f} hectares. "
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f"Please select an area of less than {size_limit} hectares."
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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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print("Starting analysis...")
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# Connect to datacube database
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dc = datacube.Datacube(app="Change_filmstrips")
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# Configure local dask cluster
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client = create_local_dask_cluster(return_client=True)
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# Obtain native CRS
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crs = mostcommon_crs(dc=dc,
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product="ls8_sr",
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query={
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"time": "2014",
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"geopolygon": geopolygon
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})
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# Create query based on time range, area selected, custom params
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query = {
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"time": time_range,
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"geopolygon": geopolygon,
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"output_crs": crs,
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"resolution": resolution,
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"dask_chunks": {
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"x": 3000,
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"y": 3000
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},
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"align": (resolution[1] / 2.0, resolution[1] / 2.0),
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}
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# Load data from all three Landsats
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warnings.filterwarnings("ignore")
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ds = load_ard(
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dc=dc,
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measurements=["red", "green", "blue"],
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products=["ls5_sr", "ls7_sr", "ls8_sr"],
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min_gooddata=max_cloud,
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ls7_slc_off=ls7_slc_off,
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**query,
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)
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# Optionally calculate tides for each timestep in the satellite
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# dataset and drop any observations out side this range
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if tide_range != (0.0, 1.0):
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from deafrica_tools.coastal import tidal_tag
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ds = tidal_tag(ds=ds, tidepost_lat=None, tidepost_lon=None)
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min_tide, max_tide = ds.tide_height.quantile(tide_range).values
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ds = ds.sel(time=(ds.tide_height >= min_tide) &
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(ds.tide_height <= max_tide))
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ds = ds.drop("tide_height")
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print(f" Keeping {len(ds.time)} observations with tides "
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f"between {min_tide:.2f} and {max_tide:.2f} m")
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# Create time step ranges to generate filmstrips from
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bins_dt = pd.date_range(start=time_range[0],
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end=time_range[1],
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freq=pd.DateOffset(**time_step))
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# Bin all satellite observations by timestep. If some observations
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# fall outside the upper bin, label these with the highest bin
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labels = bins_dt.astype("str")
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time_steps = (pd.cut(ds.time.values, bins_dt,
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labels=labels[:-1]).add_categories(
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labels[-1]).fillna(labels[-1]))
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time_steps_var = xr.DataArray(time_steps, [("time", ds.time.values)],
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name="timestep")
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# Resample data temporally into time steps, and compute geomedians
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ds_geomedian = (ds.groupby(time_steps_var).apply(
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lambda ds_subset: geomedian_with_mads(
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ds_subset, compute_mads=False, compute_count=False)))
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print("\nGenerating geomedian composites and plotting "
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"filmstrips... (click the Dashboard link above for status)")
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ds_geomedian = ds_geomedian.compute()
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# Reset CRS that is lost during geomedian compositing
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ds_geomedian = assign_crs(ds_geomedian, crs=ds.geobox.crs)
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############
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# Plotting #
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############
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# Convert to array and extract vmin/vmax
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output_array = ds_geomedian[["red", "green", "blue"]].to_array()
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percentiles = output_array.quantile(q=(0.02, 0.98)).values
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# Create the plot with one subplot more than timesteps in the
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# dataset. Figure width is set based on the number of subplots
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# and aspect ratio
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n_obs = output_array.sizes["timestep"]
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ratio = output_array.sizes["x"] / output_array.sizes["y"]
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fig, axes = plt.subplots(1,
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n_obs + 1,
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figsize=(5 * ratio * (n_obs + 1), 5))
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fig.subplots_adjust(wspace=0.05, hspace=0.05)
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# Add timesteps to the plot, set aspect to equal to preserve shape
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for i, ax_i in enumerate(axes.flatten()[:n_obs]):
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output_array.isel(timestep=i).plot.imshow(ax=ax_i,
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vmin=percentiles[0],
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vmax=percentiles[1])
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ax_i.get_xaxis().set_visible(False)
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ax_i.get_yaxis().set_visible(False)
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ax_i.set_aspect("equal")
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# Add change heatmap panel to final subplot. Heatmap is computed
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# by first taking the log of the array (so change in dark areas
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# can be identified), then computing standard deviation between
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# all timesteps
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(np.log(output_array).std(dim=["timestep"]).mean(
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dim="variable").plot.imshow(ax=axes.flatten()[-1],
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robust=True,
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cmap="magma",
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add_colorbar=False))
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axes.flatten()[-1].get_xaxis().set_visible(False)
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axes.flatten()[-1].get_yaxis().set_visible(False)
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axes.flatten()[-1].set_aspect("equal")
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axes.flatten()[-1].set_title("Change heatmap")
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# Export to file
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date_string = "_".join(time_range)
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ts_v = list(time_step.values())[0]
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ts_k = list(time_step.keys())[0]
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fig.savefig(
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f"filmstrip_{output_name}_{date_string}_{ts_v}{ts_k}.png",
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dpi=150,
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bbox_inches="tight",
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pad_inches=0.1,
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)
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# close dask client
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client.shutdown()
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return ds_geomedian
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