Google Earth Engine – How to Resolve Black Exported TIFF File Issue

classificationgoogle-earth-engine

I am trying to export the TIFF file from Google Earth Engine, but the exported file appears black.

var image = ee.Image(ee.ImageCollection("COPERNICUS/S2")
    .filterBounds(roi)
    .filterDate('2021-06-01', '2021-06-30')
    .sort('CLOUD_COVER')
    .first()
    .clip(roi))
  
  Map.addLayer(image, {bands: ['B4', 'B3', 'B2'],min:0, max: 3000}, 'True colour image')

var classNames = heterogeneitybaresoil.merge(vegetation);
print(classNames)

var bands = ['B2', 'B3', 'B4','B8', ];
var training = image.select(bands).sampleRegions({
  collection: classNames,
  properties: ['landcover'],
  scale: 30
});
print(training);

var classifier = ee.Classifier.smileCart().train({
  features: training,
  classProperty: 'landcover',
  inputProperties: bands
});


//Run the classification
var classified = image.select(bands).classify(classifier);

//Display classification
Map.centerObject(classNames, 11);
Map.addLayer(classified,
{min: 0, max: 1, palette: ['green','red']},
'classification');


// Create a task that you can launch from the Tasks tab.
Export.image.toDrive({
  image: classified,
  description: 'Akamas',
  scale: 30
});

this is how looks like my exported tiff

the script of the above (in order to help you to run it)
https://code.earthengine.google.com/f8bc253d6c67799c92012441177173a0?hideCode=true

Best Answer

If you want to export the red/green image, call Image.visualize on the output, using the same visualization parameters you're using to display it. Otherwise, you're Exporting an image of 0s and 1s, which when you display it will appear black and almost-black.

Export.image.toDrive({
  image: classified.visualize({min: 0, max: 1, palette: ['green','red']}),
  description: 'Akamas',
  scale: 30
});

Related Solutions

[GIS] Google Earth Engine, Supervised Classification

I'd do something like this:

// Landsat 5 TM Raw Image Collection
var lt5 = ee.ImageCollection("LANDSAT/LT5_L1T");

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Early = lt5.filterDate('1985-01-01', '1986-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud = ee.Image(lakeTiticaca_Early.sort('CLOUD_COVER').first());

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Later = lt5.filterDate('2011-01-01', '2012-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud2 = ee.Image(lakeTiticaca_Later.sort('CLOUD_COVER').first());

// Applying MNDWI Function to Least Cloudy Images 
var mNDWI_Early = leastCloud.expression(
  '(Green - MIR) / (Green + MIR)', {
  'Green': leastCloud.select('B2'),
  'MIR': leastCloud.select('B5')
 });

var mNDWI_Later = leastCloud2.expression(
 '(Green - MIR) / (Green + MIR)', {
 'Green': leastCloud2.select('B2'),
 'MIR': leastCloud2.select('B5')
 });

// Creating an image collection from the MNDWI calculated Images 
var constant1 = ee.Image(mNDWI_Early).select([0],["mNDWI"]);
Map.addLayer(leastCloud,{bands:["B4","B5","B3"]},"before")
var constant2 = ee.Image(mNDWI_Later).select([0],["mNDWI"]);
Map.addLayer(leastCloud2,{bands:["B4","B5","B3"]},"after")

var dif = constant2.subtract(constant1)
Map.addLayer(dif,{},"dif")

var img1 = leastCloud.addBands(constant1)
var img2 = leastCloud2.addBands(constant2)

var final = ee.Image.cat(img1,img2)
Map.addLayer(final,{},"final")

//* Supervised Image Classification *//

var training = ee.FeatureCollection([water_nochange, water_gain, water_loss, land_nochange]).flatten()

var sample = final.sampleRegions(training, ["class"], 30)

var trained = ee.Classifier.cart().train(sample, "class")

var classified = final.classify(trained)
Map.addLayer(classified,{min:1, max:4, palette:["#b3b3ff","#0000e6", "#ff0000", "#009900"]},"classified")

You can access the feature collections in the following link: https://code.earthengine.google.com/3f4c2908fcf7c469357b80783f3ef26f

You should mask clouds, use another path/row to include the missing part of the lake, and make more training points. You can also try another classifier like svm, randomForest, etc.

[GIS] Black vertical lines on exported image from Google Earth Engine

This problem is caused through your reduction on the line with:

var to_export = image.mean().select(['NDVI']);

Compositions and reductions have no projection and are just reported as being WGS84.

This script exports the map without the vertical lines (https://code.earthengine.google.com/0de29cd6954bada783ef0bacbb5b1ba6):

var region = ee.FeatureCollection('ft:1A9vRY9l-eBNBFBwAV_mLAEeCIPYS2T14vsKhiEQc');
var crop = region.geometry();

function mask_image(image){
  return image.updateMask(image.select('pixel_qa').bitwiseAnd(ee.Image.constant(2)).eq(2));
}

function ndvi(image){
  return image.addBands(image.normalizedDifference(['B5','B4']).rename('NDVI'));
}

var start = ee.Date('2013-01-01');
var end = ee.Date('2017-12-31');
var image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
              .filterDate(start,end)
              .filterBounds(crop)
              .map(mask_image)
              .map(ndvi);

var original_proj = image.first().projection();

print(original_proj.crs());
print(image.first().projection().nominalScale());

var to_export = image.mean().select(['NDVI']);

print(to_export.projection().crs());
print(to_export.projection().nominalScale());

to_export = to_export.reproject(original_proj);

Export.image.toDrive({
  image: to_export,
  description: 'NDVI-test',
  folder:'Landsat8-NDVI',
  scale: to_export.projection().nominalScale().getInfo(),
  region: crop,
  maxPixels: 1e10,
  crs: 'EPSG: 32718'
  });

It prints the crs and scale of an image in the image-collection called 'image' and does the same from the image called 'to_export' which is created by taking the mean of the images in 'image'.

You can see that initially, the crs is EPSG:32619 with a scale of 30 meters. After the mean-reduction, the crs of 'to_export' is EPSG:4326 with a scale of 111319.49079327357 meters. So we need to add one more line to your code, before proceeding to the export, i.e. 'to_export.reproject(original_proj);'.

Now the projection is correct again and your export doesn't contain the vertical lines.

Best Answer

Related Solutions

[GIS] Google Earth Engine, Supervised Classification

[GIS] Black vertical lines on exported image from Google Earth Engine

Related Question