# Water Observations from Space (WOfS)¶

Keywords: datasets; wofs_ls, datasets; wofs_ls_summary_alltime, datasets; wofs_ls_summary_annual

## Contexte¶

Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite.

Ils sont générés en utilisant l’algorithme de classification WOfS sur les données du satellite Landsat. Il existe plusieurs produits WOfS disponibles pour le continent africain, comme indiqué ci-dessous :

Type de produit

Description

Résumé annuel du WOfS

Le rapport entre les observations humides et claires de chaque année civile.

Résumé de tous les temps de WOfS

Le rapport entre les observations humides et claires sur l’ensemble du temps

WOFLs (WOfS Feature Layers)

Classification eau et non-eau générée par scène

• WOfS Annual/All-Time Summary: The frequency a pixel was classified as wet. This requires:

• Total number of clear observations for each pixel: the number of observations that were clear (no cloud or shadow) for the selected time period. The classification algorithm then assigns these as either wet, or dry.

• Nombre total d’observations humides pour chaque pixel : le nombre d’observations qui étaient claires et humides pour la période sélectionnée.

The WOfS Summaries are calculated as the ratio of clear wet observations to total clear observations.

$\text{WOfS Summary (frequency)} = \frac{\text{Number of Clear and Wet Observations}}{\text{Number of Clear Observations}}$
• WOFLs (WOfS Feature Layers): Individual water-classified images are called Water Observation Feature Layers (WOFLs), and are created from the input satellite data. There is one WOFL for each satellite dataset processed for the occurrence of water. For more information on WOFLs, see the Applying WOfS bitmasking notebook.

Reference

Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341-352.

## Description¶

This notebook demonstrates how to: 1. Load and plot WOFLS for multiple time-steps 2. Load WOfS annual summaries 3. Load the WOfS all-time summary

For a detailed example of using WOfS for water resource management, see the Water_extent_WOfS notebook in the DE Africa sandbox.

## Pour commencer¶

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

[1]:

%matplotlib inline

import datacube
import seaborn as sns
import matplotlib.pyplot as plt
from datacube.utils import geometry
from datacube.utils.geometry import CRS

from deafrica_tools.plotting import display_map, plot_wofs
from deafrica_tools.datahandling import wofs_fuser, mostcommon_crs

/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
warnings.warn(


### Connect to the datacube¶

[2]:

dc = datacube.Datacube(app="Intro_WOfS")


### List of WOfS products available in Digital Earth Africa¶

[3]:

products = dc.list_products()
display_columns = ['name', 'description']
dc_products = products[display_columns]
dc_products[dc_products['name'].str.contains("wofs_ls")]

[3]:

name description
name
wofs_ls wofs_ls Historic Flood Mapping Water Observations from...
wofs_ls_summary_alltime wofs_ls_summary_alltime Water Observations from Space Alltime Statistics
wofs_ls_summary_annual wofs_ls_summary_annual Water Observations from Space Annual Statistics

## Analysis parameters¶

The following items are included in the « query » that defines what the datacube need to return.

• lat, lon, buffer: center lat/lon and analysis window size for the area of interest

• time: date range to fetch the scenes. The approximate time between two scenes is 16 days. If there is a location near a swathe boundary, it may be captured in two passes and so there could be two images within the 16 day period.

The default location is Lake Ngami in Botswana.

[4]:

lat, lon =  -20.4855, 22.7547
buffer = 0.175
time_range = ('2019-01-01', '2019-01-20')

#add lat,lon,buffer togethert to get bounding box
x = (lon-buffer, lon+buffer)
y =  (lat+buffer, lat-buffer)


### View the selected location¶

[5]:

# View the location
display_map(x=x, y=y)

[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

## Load WOfS Feature Layers (WOFLs)¶

Here, it is not necessary to directly call on the bit flags as we can use the selection wet=True to create the water mask, while dry=True gives the opposite. In this case, isel is used to select a single timestep, and shows the wet/dry pixels for that increment only.

[6]:

# Create a reusable query
query = {
'x': x,
'y': y,
'time': time_range,
'resolution': (-30, 30)
}

#grab crs of location
output_crs = mostcommon_crs(dc=dc, product='wofs_ls', query=query)

group_by="solar_day",
fuse_func=wofs_fuser,
output_crs = output_crs,
collection_category="T1",
**query)
print(wofls)

<xarray.Dataset>
Dimensions:      (time: 4, y: 1306, x: 1231)
Coordinates:
* time         (time) datetime64[ns] 2019-01-09T08:26:36.339805 ... 2019-01...
* y            (y) float64 -2.247e+06 -2.247e+06 ... -2.286e+06 -2.286e+06
* x            (x) float64 6.646e+05 6.646e+05 ... 7.014e+05 7.015e+05
spatial_ref  int32 32634
Data variables:
water        (time, y, x) uint8 64 64 64 64 64 64 64 64 ... 0 0 0 0 0 0 0 0
Attributes:
crs:           EPSG:32634
grid_mapping:  spatial_ref


### Plotting data¶

We can plot WOFLs using the plot_wofs function. We can see that our study area includes one large waterbody. We can also see that some observations contain clouds, cloud shadow, and regions of no-data.

[7]:

# Classify different types of data in the area of interest
plot_wofs(wofls.water, col='time', size=4, col_wrap=4);


## Masking using WOfS bit flags¶

WOFLs uses bit flags to flag pixels as « wet » or otherwise. For more details on bitmasking with WOfS, see the Applying WOfS bitmasking notebook.

We can convert the WOfS bit field into a binary array containing True and False values. This allows us to use the WOFL data as a mask that can be applied to other datasets.

The make_mask function allows us to create a mask using the flag labels (e.g. « wet » or « dry ») rather than the binary numbersin the mask. For example, we can easily identify pixels that were wet in each image (i.e. yellow) by passing the flag wet=True:

[8]:

# Keeping only dry, non-cloudy pixels

wofls_wet.water.plot(col='time', size=4, col_wrap=4);


To look at a summary of WOFLs over a calender year, we can load the wofs_ls_summary_annual product. This can be useful to understand at a glance the annual dynamics of a waterbody. The WOfS Annual Summary product is pre-calculated, which makes it faster to load. It has three measuremts: count_wet, count_clear, and frequency.

[9]:

wofs_annual = dc.load(product='wofs_ls_summary_annual',
like=wofls.geobox,
time='2019')

print(wofs_annual)

<xarray.Dataset>
Dimensions:      (time: 1, y: 1306, x: 1231)
Coordinates:
* time         (time) datetime64[ns] 2019-07-02T11:59:59.999999
* y            (y) float64 -2.247e+06 -2.247e+06 ... -2.286e+06 -2.286e+06
* x            (x) float64 6.646e+05 6.646e+05 ... 7.014e+05 7.015e+05
spatial_ref  int32 32634
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 35 34 34 34 34 34 34 ... 51 51 51 51 51 51
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:32634
grid_mapping:  spatial_ref


### Plotting WOfS frequency¶

The plot below will have values that range from 0 to 1. Values that approach 1 indicate a permanent waterbody, while values closer to 0 indicate a more ephemeral or seasonal waterbody.

[10]:

wofs_annual.frequency.plot(size=6, cmap=sns.color_palette("mako_r", as_cmap=True))
plt.title('WOfS Annual Summary for 2019');


To look at a summary of WOFLs over the entire Landsat archive (around 1984 to present), we can load the wofs_ls_summary_alltime product.

[11]:

wofs_alltime = dc.load(product='wofs_ls_summary_alltime',
like=wofls.geobox)

print(wofs_alltime)

<xarray.Dataset>
Dimensions:      (time: 1, y: 1306, x: 1231)
Coordinates:
* time         (time) datetime64[ns] 2002-12-31T23:59:59.999999
* y            (y) float64 -2.247e+06 -2.247e+06 ... -2.286e+06 -2.286e+06
* x            (x) float64 6.646e+05 6.646e+05 ... 7.014e+05 7.015e+05
spatial_ref  int32 32634
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 487 487 487 487 488 ... 845 847 846 844 842
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:32634
grid_mapping:  spatial_ref


### Plot the WOfS all-time summary¶

[12]:

wofs_alltime.frequency.plot(size=6, cmap=sns.color_palette("mako_r", as_cmap=True))
plt.title('WOfS All-Time Summary (1984 to present)');


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 repoart an issue with this notebook, you can file one on Github.

Compatible datacube version:

[13]:

print(datacube.__version__)

1.8.7


Last Tested:

[14]:

from datetime import datetime
datetime.today().strftime('%Y-%m-%d')

[14]:

'2022-06-28'