Mapping longer-term changes in water extent with WOfS

Keywords: data used; WOfS, water; extent, analysis; time series, visualisation; animation

Contexte

The United Nations have prescribed 17 « Sustainable Development Goals » (SDGs). This notebook attempts to monitor SDG Indicator 6.6.1 - change in the extent of water-related ecosystems. Indicator 6.6.1 has 4 sub-indicators: > i. The spatial extent of water-related ecosystems > ii. The quantity of water contained within these ecosystems > iii. The quality of water within these ecosystems > iv. The health or state of these ecosystems

This notebook primarily focuses on the first sub-indicator - spatial extents.

Description

The notebook demonstrates how to load, visualise, and analyse the WOfS annual summary product to gather insights into the longer-term extent of water bodies. It provides a compliment to the Water_extent_sentinel_2 notebook which focussing on more recent water extents at seasonal time intervals.


Getting started

To run this analysis, run all the cells in the notebook, starting with the « Load packages » cell.

Load packages

Import Python packages that are used for the analysis.

[1]:
%matplotlib inline

# Force GeoPandas to use Shapely instead of PyGEOS
# In a future release, GeoPandas will switch to using Shapely by default.
import os
os.environ['USE_PYGEOS'] = '0'

import datacube
import numpy as np
import xarray as xr
import seaborn as sns
import geopandas as gpd
import matplotlib.pyplot as plt
from IPython.display import Image
from matplotlib.colors import ListedColormap
from matplotlib.patches import Patch
from datacube.utils.geometry import Geometry

from deafrica_tools.bandindices import calculate_indices
from deafrica_tools.plotting import display_map, xr_animation
from deafrica_tools.spatial import xr_rasterize
from deafrica_tools.areaofinterest import define_area

Connect to the datacube

Activate the datacube database, which provides functionality for loading and displaying stored Earth observation data.

[2]:
dc = datacube.Datacube(app='water_extent')

Analysis parameters

The following cell sets the parameters, which define the area of interest and the length of time to conduct the analysis over.

The parameters are:

  • lat: The central latitude to analyse (e.g. 10.338).

  • lon : The central longitude to analyse (e.g. -1.055).

  • lat_buffer : The number of degrees to load around the central latitude.

  • lon_buffer : The number of degrees to load around the central longitude.

  • start_year and end_year: The date range to analyse (e.g. ('1990', '2020').

Select location

To define the area of interest, there are two methods available:

  1. By specifying the latitude, longitude, and buffer. This method requires you to input the central latitude, central longitude, and the buffer value in square degrees around the center point you want to analyze. For example, lat = 10.338, lon = -1.055, and buffer = 0.1 will select an area with a radius of 0.1 square degrees around the point with coordinates (10.338, -1.055).

  2. By uploading a polygon as a GeoJSON or Esri Shapefile. If you choose this option, you will need to upload the geojson or ESRI shapefile into the Sandbox using Upload Files button 3add57fc1b504f89929a9dec258dbd71 in the top left corner of the Jupyter Notebook interface. ESRI shapefiles must be uploaded with all the related files (.cpg, .dbf, .shp, .shx). Once uploaded, you can use the shapefile or geojson to define the area of interest. Remember to update the code to call the file you have uploaded.

To use one of these methods, you can uncomment the relevant line of code and comment out the other one. To comment out a line, add the "#" symbol before the code you want to comment out. By default, the first option which defines the location using latitude, longitude, and buffer is being used.

If running the notebook for the first time, keep the default settings below. This will demonstrate how the analysis works and provide meaningful results. The example covers part of the Lake Sulunga. Tanzania.

[3]:
# Method 1: Specify the latitude, longitude, and buffer
aoi = define_area(lat=-5.9460, lon=35.5188 , buffer=0.03)

# Method 2: Use a polygon as a GeoJSON or Esri Shapefile.
#aoi = define_area(vector_path='aoi.shp')

#Create a geopolygon and geodataframe of the area of interest
geopolygon = Geometry(aoi["features"][0]["geometry"], crs="epsg:4326")
geopolygon_gdf = gpd.GeoDataFrame(geometry=[geopolygon], crs=geopolygon.crs)

# Get the latitude and longitude range of the geopolygon
lat_range = (geopolygon_gdf.total_bounds[1], geopolygon_gdf.total_bounds[3])
lon_range = (geopolygon_gdf.total_bounds[0], geopolygon_gdf.total_bounds[2])


# Define the start year and end year
start_year = '1990'
end_year = '2021'

View the area of Interest on an interactive map

The next cell will display the selected area on an interactive map. The red border represents the area of interest of the study. Zoom in and out to get a better understanding of the area of interest. Clicking anywhere on the map will reveal the latitude and longitude coordinates of the clicked point.

[4]:
display_map(lon_range, lat_range)
[4]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Load WOfS annual summaries

[5]:
#Create a query object
query = {
    'x': lon_range,
    'y': lat_range,
    'resolution': (-30, 30),
    'output_crs':'EPSG:6933',
    'time': (start_year, end_year),
}

#load wofs
ds = dc.load(product="wofs_ls_summary_annual",
             **query)

print(ds)
<xarray.Dataset>
Dimensions:      (time: 32, y: 255, x: 194)
Coordinates:
  * time         (time) datetime64[ns] 1990-07-02T11:59:59.999999 ... 2021-07...
  * y            (y) float64 -7.534e+05 -7.534e+05 ... -7.61e+05 -7.61e+05
  * x            (x) float64 3.424e+06 3.424e+06 3.424e+06 ... 3.43e+06 3.43e+06
    spatial_ref  int32 6933
Data variables:
    count_wet    (time, y, x) int16 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
    count_clear  (time, y, x) int16 3 3 3 3 3 3 3 3 ... 21 21 21 18 19 19 19 20
    frequency    (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Attributes:
    crs:           epsg:6933
    grid_mapping:  spatial_ref

Clip the datasets to the shape of the area of interest

A geopolygon represents the bounds and not the actual shape because it is designed to represent the extent of the geographic feature being mapped, rather than the exact shape. In other words, the geopolygon is used to define the outer boundary of the area of interest, rather than the internal features and characteristics.

Clipping the data to the exact shape of the area of interest is important because it helps ensure that the data being used is relevant to the specific study area of interest. While a geopolygon provides information about the boundary of the geographic feature being represented, it does not necessarily reflect the exact shape or extent of the area of interest.

[6]:
#Rasterise the area of interest polygon
aoi_raster = xr_rasterize(gdf=geopolygon_gdf, da=ds, crs=ds.crs)
#Mask the dataset to the rasterised area of interest
ds = ds.where(aoi_raster == 1)

Facet plot a subset of the annual WOfS summaries

[7]:
ds.isel(time=[5,10,15,20,25]).frequency.plot(col='time', col_wrap=5, cmap=sns.color_palette("mako_r", as_cmap=True));
../../../_images/sandbox_notebooks_Real_world_examples_Water_extent_WOfS_18_0.png

Animating time series

In the next cell, we plot the dataset we loaded above as an animation GIF, using the `xr_animation <../Frequently_used_code/Animated_timeseries.ipynb>`__ function. The output_path will be saved in the directory where the script is found and you can change the names to prevent files overwrite.

[8]:
out_path = 'annual_water_frequency.gif'

xr_animation(ds=ds,
             output_path=out_path,
             interval=400,
             bands=['frequency'],
             show_text='WOfS Annual Summary',
             show_date = '%Y',
             width_pixels=300,
             annotation_kwargs={'fontsize': 15},
             imshow_kwargs={'cmap': sns.color_palette("mako_r", as_cmap=True), 'vmin': 0.0, 'vmax': 0.9},
             colorbar_kwargs={'colors': 'black'},
             show_colorbar=False)

# Plot animated gif
plt.close()
Image(filename=out_path)
Exporting animation to annual_water_frequency.gif
[8]:
<IPython.core.display.Image object>

Calculate the annual area of water extent

The number of pixels can be used for the area of the waterbody if the pixel area is known. Run the following cell to generate the necessary constants for performing this conversion.

[9]:
pixel_length = query["resolution"][1]  # in metres
m_per_km = 1000  # conversion from metres to kilometres
area_per_pixel = pixel_length**2 / m_per_km**2

Threshold WOfS annual frequency to classify water/not-water

Calculates the area of pixels classified as water (if ds.frequency is > 0.20, then the pixel will be considered regular open water during the year)

[10]:
water_threshold = 0.20
[11]:
#threshold
water_extent = ds.frequency > water_threshold

#calculate area
ds_valid_water_area = water_extent.sum(dim=['x', 'y']) * area_per_pixel

Plot the annual area of open water

[12]:
plt.figure(figsize=(18, 4))
ds_valid_water_area.plot(marker='o', color='#9467bd')
plt.title(f'Observed Annual Area of Water from {start_year} to {end_year}')
plt.xlabel('Dates')
plt.ylabel('Waterbody area (km$^2$)')
plt.tight_layout()
../../../_images/sandbox_notebooks_Real_world_examples_Water_extent_WOfS_27_0.png

Determine minimum and maximum water extent

The next cell extract the Minimum and Maximum extent of water from the dataset using the min and max functions, we then add the dates to an xarray.DataArray.

[13]:
min_water_area_date, max_water_area_date =  min(ds_valid_water_area), max(ds_valid_water_area)
time_xr = xr.DataArray([min_water_area_date.time.values, max_water_area_date.time.values], dims=["time"])

print(time_xr)
<xarray.DataArray (time: 2)>
array(['1992-07-01T23:59:59.999999000', '2021-07-02T11:59:59.999999000'],
      dtype='datetime64[ns]')
Dimensions without coordinates: time

Plot the dates when the min and max water extent occur

Plot water classified pixel for the two dates where we have the minimum and maximum surface water extent.

[14]:
water_extent.sel(time=time_xr).plot.imshow(col="time", col_wrap=2, figsize=(14, 6));
../../../_images/sandbox_notebooks_Real_world_examples_Water_extent_WOfS_31_0.png

Compare two time periods

The following cells determine the maximum extent of water for two different years. * baseline_year : The baseline year for the analysis * analysis_year : The year to compare to the baseline year

[15]:
baseline_time = '2019'
analysis_time = '2021'

baseline_ds, analysis_ds = ds_valid_water_area.sel(time=baseline_time, method ='nearest'), ds_valid_water_area.sel(time=analysis_time, method ='nearest')

Plotting

Plot water extent for the two chosen periods.

[16]:
compare = water_extent.sel(time=[baseline_ds.time.values, analysis_ds.time.values])

compare.plot(col="time",col_wrap=2,figsize=(10, 5), cmap='viridis', add_colorbar=False);
../../../_images/sandbox_notebooks_Real_world_examples_Water_extent_WOfS_35_0.png

Calculating the change for the two nominated periods

The cells below calculate the amount of water gain, loss and stable for the two periods

[17]:
analyse_total_value = compare.isel(time=1).astype(int)
change = analyse_total_value - compare.isel(time=0).astype(int)

water_appeared = change.where(change == 1)
permanent_water = change.where((change == 0) & (analyse_total_value == 1))
permanent_land = change.where((change == 0) & (analyse_total_value == 0))
water_disappeared = change.where(change == -1)


The cell below calculate the area of water extent for water_loss, water_gain, permanent water and land

[18]:
total_area = analyse_total_value.count().values * area_per_pixel
water_apperaed_area = water_appeared.count().values * area_per_pixel
permanent_water_area = permanent_water.count().values * area_per_pixel
water_disappeared_area = water_disappeared.count().values * area_per_pixel

Plotting

The water variables are plotted to visualised the result

[19]:
water_appeared_color = "Green"
water_disappeared_color = "Yellow"
stable_color = "Blue"
land_color = "Brown"

fig, ax = plt.subplots(1, 1, figsize=(10, 10))

compare[1].plot.imshow(cmap="Pastel1",
                                       add_colorbar=False,
                                       add_labels=False,
                                       ax=ax)
water_appeared.plot.imshow(
    cmap=ListedColormap([water_appeared_color]),
    add_colorbar=False,
    add_labels=False,
    ax=ax,
)
water_disappeared.plot.imshow(
    cmap=ListedColormap([water_disappeared_color]),
    add_colorbar=False,
    add_labels=False,
    ax=ax,
)
permanent_water.plot.imshow(cmap=ListedColormap([stable_color]),
                            add_colorbar=False,
                            add_labels=False,
                            ax=ax)

plt.legend(
    [
        Patch(facecolor=stable_color),
        Patch(facecolor=water_disappeared_color),
        Patch(facecolor=water_appeared_color),
        Patch(facecolor=land_color),
    ],
    [
        f"Water to Water {round(permanent_water_area, 2)} km2",
        f"Water to No Water {round(water_disappeared_area, 2)} km2",
        f"No Water to Water: {round(water_apperaed_area, 2)} km2",
    ],
    loc="lower left",
)

plt.title("Change in water extent: " + baseline_time + " to " + analysis_time);
../../../_images/sandbox_notebooks_Real_world_examples_Water_extent_WOfS_41_0.png

Prochaines étapes

Return to the « Analysis parameters » section, modify some values (e.g. latitude, longitude, start_year, end_year) and re-run the analysis. You can use the interactive map in the « View the selected location » section to find new central latitude and longitude values by panning and zooming, and then clicking on the area you wish to extract location values for. You can also use Google maps to search for a location you know, then return the latitude and longitude values by clicking the map.

Change the year also in « Compare Two Time Periods - a Baseline and an Analysis » section, (e.g. base_year, analyse_year) and re-run the analysis.


Additional information

License: The code in this notebook is licensed under the Apache License, Version 2.0. Digital Earth Africa data is licensed under the Creative Commons by Attribution 4.0 license.

Contact: If you need assistance, please post a question on the Open Data Cube Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on Github.

Compatible datacube version:

[20]:
print(datacube.__version__)
1.8.15

Last Tested:

[21]:
from datetime import datetime
datetime.today().strftime('%Y-%m-%d')
[21]:
'2023-09-19'