Masking out Copernicus 2019 100m global land cover classes and for an NDVI time-series analysis in Google Earth Engine

Question

I am quite new to Google Earth Engine and Java scripting. I am delineating land cover data for an administrative area in Kenya (Samburu county) from the Copernicus Global Landcover 100m product. I've calculated the frequency & percentages for the different land cover classes in the county. I am trying to mask out each of the land cover classes and then do an NDVI time-series analysis for each of these land cover classes for my study area in Google Earth Engine.

How do I mask out the landcover classes?

Here is the code that I have scripted.

The link to the code is https://code.earthengine.google.com/?scriptPath=users%2Fwawerujohn%2Fhallo_world%3ASamburu_Landcover_100m_Copernicus%20(copy)

var globcover = ee.Image("COPERNICUS/Landcover/100m/Proba-V-C3/Global/2019")
.select('discrete_classification');

// Extract the landcover band
var landcover = globcover.select('discrete_classification');
   
// Clip the image to the polygon geometry
var landcover_roi = landcover.clip(geometry);
    
// Add a map layer of the landcover clipped to the polygon.
Map.addLayer(landcover_roi);
    
// Print out the frequency of landcover occurrence for the polygon.
var frequency = landcover.reduceRegion({
  reducer:ee.Reducer.frequencyHistogram(),
  geometry:geometry,
  scale:1000
});

//var maskShrub = landcover_roi.updateMask(value.eq(20));
//Map.addLayer(maskShrub, {min: 0, max: 1, palette: palette}, 'Shrub only')

var dict = ee.Dictionary(frequency.get('discrete_classification'));
var sum = ee.Array(dict.values()).reduce(ee.Reducer.sum(),[0]).get([0]);
var new_dict = dict.map(function(k,v) {
  return ee.Number(v).divide(sum).multiply(100);
});
print('Land Cover (%)',new_dict);

print('landcover frequency', frequency.get('discrete_classification'));

Map.setCenter(37, 1, 8);

Map.addLayer(globcover, {}, "Land Cover");

Export.image.toDrive({
  image: landcover_roi,
  description: 'Landcover',
  scale: 100,
  region: mask,
  fileFormat: 'GeoTIFF',
  formatOptions: {
    cloudOptimized: true
  }
});

Answer 1

You could select the land cover values you are interested in to create a mask and then apply it to all the images in the collection you will be using for your time series analysis. Here's an example using the landsat 8 surface reflectance collection.

// Select the class you are interested in analyzing
var LCclass112 = landcover_roi.eq(112);
// Filter collection to ROI and dates of interest
// Finally, apply the mask to every image in the collection using map
var l8sr4TS = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterBounds(geometry)
.filterDate('2015-01-01','2018-01-01')
.map(function(image){
  return image.updateMask(LCclass112);
});