map_firemodels

//load  vegetation
var ESA_LC_glob = ee.ImageCollection("COPERNICUS/Landcover/100m/Proba-V-C3/Global")
              .filterDate(ee.Date('2019-01-01'), ee.Date('2020-01-01'));
var FMDS = ee.Image("users/dsandovalhdh/LC20_F40_200").reproject('EPSG:4326', null, 1000);
print(FMDS,'FMDS');
var ESA_LC = ee.Image(ESA_LC_glob.first()).select(['bare-coverfraction','grass-coverfraction','shrub-coverfraction' , 'tree-coverfraction']).updateMask(FMDS);
print(ESA_LC,'ESA_LC');

Map.addLayer(ESA_LC,
 {min: [0, 0, 0], max: [100, 100, 100], bands: ['grass-coverfraction','tree-coverfraction' , 'shrub-coverfraction']}, 'ESA_LC');
Map.addLayer(FMDS, {min: 91, max: 193}, 'FMDS');

var df =FMDS.addBands(ESA_LC);
print(df,'df');
/////////////////// Random Forests
// Make a training dataset by sampling the stacked images.
var training = df.sample({
  //region: bbox,
  scale: 1000,
  numPixels: 10000
});
//train the classifier 20 random forests
var bands = ['bare-coverfraction','grass-coverfraction','shrub-coverfraction' , 'tree-coverfraction'];
var classifier = ee.Classifier.smileRandomForest(20)
  .train(training, 'b1', bands);


// classify world
ESA_LC_glob =ee.Image(ESA_LC_glob.first());
var glob_FMDLS = ESA_LC_glob.classify(classifier);
//Classifier.smileRandomForest
print(glob_FMDLS,'glob_FMDLS');
Map.addLayer(glob_FMDLS, {min: 91, max: 193}, 'glob_FMDLS');
/*
var samples = df.stratifiedSample({
  numPoints:5000,
  //region: region,
  geometries: false,

});
//print(samples,'samples');
Export.table.toDrive(samples,
"Fire_analysis",
"firemodels_sample",
"firemodels_sample");
*/