Charting Yearly Forest Loss

Calculating Yearly Forest Loss

In the previous section you learned how to calculate total forest area lost in the given region of interest using the reduceRegion method. Instead of calculating the total loss, it would be helpful to compute the loss for each year. The way to achieve this in Earth Engine is using a Grouped Reducer.

To group output of reduceRegion(), you can specify a grouping band that defines groups by integer pixel values. In the following example, we slightly modify the previous code and add the lossYear band to the original image. Each pixel in the lossYear band contain values from 0 to 14 - indicating the year in which the loss occurred. We also change the reducer to a grouped reducer, specifying the band index of the grouping band (1) so the pixel areas will be summed and grouped according to the value in the lossYear band.

// Load country boundaries from LSIB.
var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
// Get a feature collection with just the Congo feature.
var congo = countries.filter(ee.Filter.eq('country_co', 'CF'));

// Get the loss image.
// This dataset is updated yearly, so we get the latest version.
var gfc2017 = ee.Image('UMD/hansen/global_forest_change_2017_v1_5');
var lossImage = gfc2017.select(['loss']);
var lossAreaImage = lossImage.multiply(ee.Image.pixelArea());

var lossYear = gfc2017.select(['lossyear']);
var lossByYear = lossAreaImage.addBands(lossYear).reduceRegion({
  reducer: ee.Reducer.sum().group({
    groupField: 1
    }),
  geometry: congo,
  scale: 30,
  maxPixels: 1e9
});
print(lossByYear);

Once you run the above code, you will see the yearly forest loss area printed out in a nested list called groups. We can format the output a little to make the result a dictionary, with year as the key and loss area as the value. Notice that we are using the format() method to convert the year values from 0-14 to 2000-2014.

var statsFormatted = ee.List(lossByYear.get('groups'))
  .map(function(el) {
    var d = ee.Dictionary(el);
    return [ee.Number(d.get('group')).format("20%02d"), d.get('sum')];
  });
var statsDictionary = ee.Dictionary(statsFormatted.flatten());
print(statsDictionary);

Making a chart

Now that we have yearly loss numbers, we are ready to prepare a chart. We will use the ui.Chart.array.values() method. This method takes an array (or list) of input values and an array (or list) of labels for the X-axis.

var chart = ui.Chart.array.values({
  array: statsDictionary.values(),
  axis: 0,
  xLabels: statsDictionary.keys()
}).setChartType('ColumnChart')
  .setOptions({
    title: 'Yearly Forest Loss',
    hAxis: {title: 'Year', format: '####'},
    vAxis: {title: 'Area (square meters)'},
    legend: { position: "none" },
    lineWidth: 1,
    pointSize: 3
  });
print(chart);

The result should look like the chart below.

In the next section, you'll learn about another deforestation monitoring dataset, FORMA, and compare it to the Hansen et al. data.