Holy Ganga River carves her own way: Determining the long-term changes in the Ganga River Basin, India using Google Earth Engine and high-resolution mapping of global surface water.

Quick Overview:

This project uses the dataset described in the Nature letter High-resolution mapping of global surface water and its long-term changes in the Ganga River Basin, India. This tutorial provides examples of how to use Google Earth Engine to visualize data layers available in the GSW dataset, and presents some typical visualizations and analyses.

About the Data-set

Water Occurrence (1984-2015)

The GSW dataset contains many data layers that present the surface water data in different ways. This dataset contains a summary of where and how often surface water occurred over the entire time-period between March 1984 and October 2015.
Typically, more cloud-free observations (and thus valid observations) are available during dry seasons than wet. Without monthly weighting, the overall water occurrence (that is, computed over the full period) would be biased by temporal distribution of the valid observations (that is, giving more weight to the dry season than to the wet season).

In this tutorial, we will:

  1. add a styled map layer for visualizing water occurrence change intensity, and
  2. create a grouped reducer for summing the area of each transition class within a specified region-of-interest, and
  3. summarize the change intensity in a specified region-of-interest using a histogram.

Here is the code: Note that this code is modified from the code in Google Earth Engine.

Import images from GSW dataset and store it in variable named “gsw”

var gsw = ee.Image('JRC/GSW1_0/GlobalSurfaceWater'); 
 var occurrence = gsw.select('occurrence');
 var change = gsw.select("change_abs");
 var transition = gsw.select('transition');
 var roi = ee.Geometry.Polygon(
         [[[105.531921, 10.412183],
           [105.652770, 10.285193],
           [105.949401, 10.520218],
           [105.809326, 10.666006]]]);

Adding Visualization Parameters

The visualization parameters stored in JSON structure VIS_OCCURRENCE indicate that red should be used for a minimum value of 0% and blue for a maximum value of 100%.

var VIS_OCCURRENCE = {
     min: 0,
     max: 100,
     palette: ['red', 'blue']
 };
 var VIS_CHANGE = {
     min: -50,
     max: 50,
     palette: ['red', 'black', 'limegreen']
 };

Creating a Threshold Layer

The water occurrence image contains information on how often water is expected using a range of values from 0 to 100%. However, it is often useful to define a binary water layer (i.e. “water” vs. “non-water”) based on a certain percentage of occurrence (i.e. a threshold value). We will use this simple binary layer as a clean background layer over which other GSW layers can be placed. Creating this threshold layer can be done using the following statements, which uses a threshold value of 90% to separate water and non-water.

 var VIS_WATER_MASK = {
   palette: ['white', 'black']
 };

Create a feature for a transition class that includes the area covered.

function createFeature(transition_class_stats) {
   transition_class_stats = ee.Dictionary(transition_class_stats);
   var class_number = transition_class_stats.get('transition_class_value');
   var result = {
       transition_class_number: class_number,
       transition_class_name: lookup_names.get(class_number),
       transition_class_palette: lookup_palette.get(class_number),
       area_m2: transition_class_stats.get('sum')
   };
   return ee.Feature(null, result);   // Creates a feature without a geometry.
 }

Create a JSON dictionary that defines piechart colors based on the transition class palette.

// https://developers.google.com/chart/interactive/docs/gallery/piechart
 function createPieChartSliceDictionary(fc) {
   return ee.List(fc.aggregate_array("transition_class_palette"))
     .map(function(p) { return {'color': p}; }).getInfo();
 }
// Create a dictionary for looking up names of transition classes.
var lookup_names = ee.Dictionary.fromLists(
    ee.List(gsw.get('transition_class_values')).map(ee.String),
    gsw.get('transition_class_names')
);
// Create a dictionary for looking up colors of transition classes.
var lookup_palette = ee.Dictionary.fromLists(
    ee.List(gsw.get('transition_class_values')).map(ee.String),
    gsw.get('transition_class_palette')
);
// Create a water mask layer, and set the image mask so that non-water areas are transparent.
var water_mask = occurrence.gt(90).mask(1);
// Generate a histogram object and print it to the console tab.
var histogram = ui.Chart.image.histogram({
  image: change,
  region: roi,
  scale: 30,
  minBucketWidth: 10
});
histogram.setOptions({
  title: 'Histogram of surface water change intensity.'
});
print(histogram);
// Summarize transition classes in a region of interest.
var area_image_with_transition_class = ee.Image.pixelArea().addBands(transition);
var reduction_results = area_image_with_transition_class.reduceRegion({
  reducer: ee.Reducer.sum().group({
    groupField: 1,
    groupName: 'transition_class_value',
  }),
  geometry: roi,
  scale: 30,
  bestEffort: true,
});
print('reduction_results', reduction_results);
var roi_stats = ee.List(reduction_results.get('groups'));
var transition_fc = ee.FeatureCollection(roi_stats.map(createFeature));
print('transition_fc', transition_fc);
// Add a summary chart.
var transition_summary_chart = ui.Chart.feature.byFeature({
    features: transition_fc,
    xProperty: 'transition_class_name',
    yProperties: ['area_m2', 'transition_class_number']
  })
  .setChartType('PieChart')
  .setOptions({
    title: 'Summary of transition class areas',
    slices: createPieChartSliceDictionary(transition_fc),
    sliceVisibilityThreshold: 0  // Don't group small slices.
  });
print(transition_summary_chart);

Initialize Map Location

//Map.setCenter(84.7866,25.7722,12); //Ganga at Patna
Map.setCenter(86.881,25.4472,10); //Ganga at Bhagalpur
//Map.setCenter(90.325,23.4207,10); //Bhramaputra River, Bangladesh
//////////////////////////////////////////////////////////////
// Map Layers
//////////////////////////////////////////////////////////////
Map.addLayer({
  eeObject: water_mask,
  visParams: VIS_WATER_MASK,
  name: '90% occurrence water mask',
  shown: false
});
Map.addLayer({
  eeObject: occurrence.updateMask(occurrence.divide(100)),
  name: "Water Occurrence (1984-2015)",
  visParams: VIS_OCCURRENCE,
  shown: false
});
Map.addLayer({
  eeObject: change,
  visParams: VIS_CHANGE,
  name: 'occurrence change intensity',
  shown: false
});
Map.addLayer({
  eeObject: transition,
  name: 'Transition classes (1984-2015)',
  shown: false
});
// Create a circle by drawing a 20000 meter buffer around a point.
var roi = ee.Geometry.Point([86.9306, 26.5296]).buffer(10000);
Map.addLayer({
  eeObject: occurrence.updateMask(occurrence.divide(100))
  .clip(roi),
  name: "Clipped",
  visParams: VIS_OCCURRENCE,
  //shown: false
});

References

Some interesting phenomenon across the globe.


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