Google Earth Engine – Union Polygons

dissolvegoogle-earth-enginepolygon

I am trying to compare the NDVI-values of different land cover classes over a certain time period and display it in a chart.
So in a first step, I'd like to extract the crop land cover and use its geometry to calculate just the NDVI of the croplands
This is my code so far.

var area = ee.FeatureCollection('users/jonathanreith/KK')
Map.centerObject(area,9);
var lc  = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover');

var crop = lc.eq(30)

// Convert the crop landcover to vectors.
var crop = crop.reduceToVectors({
  geometry: area,
  geometryType: 'polygon',
  eightConnected: false,
  labelProperty: 'landcover',
  scale: 300
});

print(crop)
var cropu = crop.union(1)
print(cropu)

//chart
var NDVITimeSeries = ui.Chart.image.seriesByRegion({
    imageCollection: l8,
    regions: cropu,
    reducer: ee.Reducer.mean()
});

Unfortunately, crop consists of thousands of features, while union somehow erases all the information and just leaves the outer bounding box.

So how do I create an union feature with its geometries intact?
My Code
I already checked these Q&As:

Best Answer

If I understand correct, you need a MultiPoint Feature for the TimeSeries. I think you just need to filter the 'crop' FeatureCollection from reduceToVectors for the features containing the crop landcover by:

crop = crop.filter(ee.Filter.equals('landcover',1))

then you can extract the feauteres as one MultiPolygon with:

var cropu = crop.geometry()

Related Solutions

How to Extract LANDSAT Time-Series Data by Polygon Using Google Earth Engine

The code that you copied uses an Earth Engine object of type "Image", and Image objects have a method/function named clip. Your reworked code (i.e. Landsat) uses an Earth Engine object of type "ImageCollection", and ImageCollection objects do not have a method named clip, so it produces an error. To get around this, you can either:

Convert the ImageCollection to an Image using a reducing method (i.e. median, mode, mosaic, max, etc.)
Write a function that clips an Image object, and map that function over the collection using ee.ImageCollection.map().

Also, it probably doesn't make sense to add the entire Landsat 5 collection as a layer on the interactive map... it contains over 25 years of data. Add a step that filters the Landsat collection down to a shorter time interval, using ImageCollection.filterDate().

Google Earth Engine – Iterating Over Years for Feature Collection

You are filtering year in the correct way. This is how I'd do it:

//Load and filter the Hansen data
var gfc2014 = ee.Image('UMD/hansen/global_forest_change_2015')
              .select(['treecover2000','loss','gain','lossyear']);

// list for filter iteration
var years = ee.List.sequence(1, 14)

// turn your scale into a var in case you want to change it
var scale = gfc2014.projection().nominalScale()

//add country districts as a feature collection
var distr = ee.FeatureCollection('ft:1U7sXFHXtxQ--g7XMeXlvPhNXPBcDtPg8Yzr2pvsg', 'geometry');

//look at tree cover, find the area
var treeCover = gfc2014.select(['treecover2000']);

// most recent version of Hansen's data has the treecover2000 layer
// ranging from 0-100. It needs to be divided by 100 if ones wants
// to calculate the areas in ha and not hundreds of ha. If not, the
// layers areaLoss/areaGain are not comparable to the areaCover. Thus
treeCover = treeCover.divide(100); // Thanks to Bruno

var areaCover = treeCover.multiply(ee.Image.pixelArea())
                .divide(10000).select([0],["areacover"])

// total loss area
var loss = gfc2014.select(['loss']);
var areaLoss = loss.gt(0).multiply(ee.Image.pixelArea()).multiply(treeCover)
               .divide(10000).select([0],["arealoss"]);

// total gain area
var gain = gfc2014.select(['gain'])
var areaGain = gain.gt(0).multiply(ee.Image.pixelArea()).multiply(treeCover)
               .divide(10000).select([0],["areagain"]);

// final image
var total = gfc2014.addBands(areaCover)
            .addBands(areaLoss)
            .addBands(areaGain)

Map.addLayer(total,{},"total")

// Map cover area per feature
var districtSums = areaCover.reduceRegions({
  collection: distr,
  reducer: ee.Reducer.sum(),
  scale: scale,
});


var addVar = function(feature) {

  // function to iterate over the sequence of years
  var addVarYear = function(year, feat) {
    // cast var
    year = ee.Number(year).toInt()
    feat = ee.Feature(feat)

    // actual year to write as property
    var actual_year = ee.Number(2000).add(year)

    // filter year:
    // 1st: get mask
    var filtered = total.select("lossyear").eq(year)
    // 2nd: apply mask
    filtered = total.updateMask(filtered)

    // reduce variables over the feature
    var reduc = filtered.reduceRegion({
      geometry: feature.geometry(),
      reducer: ee.Reducer.sum(),
      scale: scale,
      maxPixels: 1e13
    })

    // get results
    var loss = ee.Number(reduc.get("arealoss"))
    var gain = ee.Number(reduc.get("areagain"))

    // set names
    var nameloss = ee.String("loss_").cat(actual_year)
    var namegain = ee.String("gain_").cat(actual_year)

    // alternative 1: set property only if change greater than 0
    var cond = loss.gt(0).or(gain.gt(0))
    return ee.Algorithms.If(cond, 
                            feat.set(nameloss, loss, namegain, gain),
                            feat)

    // alternative 2: always set property
    // set properties to the feature
    // return feat.set(nameloss, loss, namegain, gain)
  }

  // iterate over the sequence
  var newfeat = ee.Feature(years.iterate(addVarYear, feature))

  // return feature with new properties
  return newfeat
}

// Map over the FeatureCollection
var areas = districtSums.map(addVar);

Map.addLayer(areas, {}, "areas")

In that script you get 3 fields: loss_{year}, gain_{year}, sum But if you want better 4 fields: loss, gain, year, sum; change for:

return ee.Algorithms.If(cond, 
                        feat.set("loss", loss, "gain", gain, "year", actual_year), 
                        feat)

You could also compute percentage and set it to the features.

Edit: Thank to @Bruno_Conte_Leite, who made me reconsider my answer, I have made some updates, the one suggested by Bruno and others.

Scale: I suggest to keep the original scale of Hansen data.
treeCover: most recent version of Hansen's data has the treecover2000 layer ranging from 0-100. It needs to be divided by 100 if ones wants to calculate the areas in ha and not hundreds of ha. (Bruno)
areaLoss and areaGain: Added .multiply(treeCover) otherwise the area would be of the whole pixel and not of the indicated percentage
maxPixels: I added maxPixels: 1e13 in the reduction

Best Answer

Related Solutions

How to Extract LANDSAT Time-Series Data by Polygon Using Google Earth Engine

Google Earth Engine – Iterating Over Years for Feature Collection

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