Google Earth Engine – How to Import Geometry/Feature Collection in GeoJSON Format into Google Earth Engine

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I have a GeoJSON file. I have opened it in Sublime Text Editor

How can I import it in Google Earth Engine?

Best Answer

You should be able to import GeoJSON geometry objects directly.

Here is an example of a MultiPolygon:

feature_geometry =
{
'type': 'MultiPolygon',
'coordinates': [[[
    [-90, 24],
    [-90.001, 40],
    [-90.001, 39.001],
    [-90, 31.001],
    [-90, 27]
]]]
}

Also, as mentioned in the comments, you can directly convert geojson into a shapefile using qgis or ogr then import into GEE.

Related Solutions

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

Google Earth Engine – Calculating Image Collection Statistics in Google Earth Engine

Earth Engine provides very nice functionality to calculate statistics on imagery called reducers. There are plenty of ways to use reducers but as simple examples you can calculate the statistics at the pixel level or provide a geometry to calculate statistics:

var modis = ee.ImageCollection("MODIS/006/MOD13Q1")
.filterDate("2000-01-01","2001-01-01")
.select("NDVI");

print(modis)

var mod13 = modis.map(function(img){
  return img.multiply(0.0001)
  .copyProperties(img,['system:time_start','system:time_end']);
});

// calculate the mean of value for each pixel
var meanMod13 = mod13.reduce(ee.Reducer.mean())
Map.addLayer(meanMod13,{min:0,max:1},'Mean NDVI')

// calculate the mean value for a region
var geom = ee.Geometry.Rectangle([-88,34,-87,35])
Map.addLayer(geom)
// *Note that reduceRegion works only on ee.Image not ee.ImageCollection data types
var zonalStats = meanMod13.reduceRegion({
    geometry: geom,
    reducer: ee.Reducer.mean(),
    scale: 1000,
    bestEffort: true
});
print(zonalStats)

I hope this helps!

Best Answer

Related Solutions

Google Earth Engine – Iterating Over Years for Feature Collection

Google Earth Engine – Calculating Image Collection Statistics in Google Earth Engine

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