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I am trying to compute a distance raster in GEE. I am looking for a tool similar to ArcMap’s Euclidean Distance.

I tried to use ee.Image.distance, however, I get an error related to the kernel.

Here is the the code:

// Country variable
var pais = 'Honduras';

// Collection adm1
var limites =ee.FeatureCollection("FAO/GAUL/2015/level1");

// Define ROI (Country)
var roi= limites.filter(ee.Filter.eq('ADM0_NAME', pais));

// Load GFC
var dataset = ee.Image('UMD/hansen/global_forest_change_2020_v1_8');
print (dataset);

Map.setCenter(-86.62,14.82, 8);
var treeLossVisParam = {
  bands: ['lossyear'],
  min: 0,
  max: 20,
  palette: ['yellow', 'red','purple']
};
Map.addLayer(dataset.clip(roi), treeLossVisParam, 'tree loss year');

// compute euclidian distance map from deforested pixels
var dist= dataset.select('lossyear').distance({kernel:null, skipMasked:false})
print('dist', dist)

and its link in GEE:

https://code.earthengine.google.com/5211d228ce97e647f9078eee7ad45ee2

This is the type of raster that I am looking for:

enter image description here

1 Answer 1

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After running your code, I got this error message:

Image (Error)
Image.distance: Must specify a distance kernel.

So, I modified it as follows (by using an euclidean kernel distance equal to 50):

// Country variable
var pais = 'Honduras';

// Collection adm1
var limites =ee.FeatureCollection("FAO/GAUL/2015/level1");

// Define ROI (Country)
var roi= limites.filter(ee.Filter.eq('ADM0_NAME', pais));

// Load GFC
var dataset = ee.Image('UMD/hansen/global_forest_change_2020_v1_8');
print (dataset);

Map.setCenter(-86.62,14.82, 8);
var treeLossVisParam = {
  bands: ['lossyear'],
  min: 0,
  max: 20,
  palette: ['yellow', 'red','purple']
};
Map.addLayer(dataset.clip(roi), treeLossVisParam, 'tree loss year');

// compute euclidian distance map from deforested pixels
var dist= dataset.select('lossyear')
  .distance({kernel:ee.Kernel.euclidean(50), skipMasked:false})
  .rename('distance')
  .clip(roi);

print('dist', dist);

var imageVisParam = {"opacity":1,
                     "bands":["distance"],
                     "max":11.180339887498949,
                     "palette":["22ff20","1a35ff","ffa925","ff0a36","2fe1ff","fd4bff"]};

Map.addLayer(dist, imageVisParam, 'distance');

After running above code without any error, I got following distance map. You can try other kernel distances for a particular result (I tried out until 500, but this one is too high). I hope this helps.

enter image description here

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