[GIS] Convert image boundary to polygon geometry in earth engine

google-earth-engine

I have imported Hansen image for global forest change and clipped it for a specific region(India). Then using the code below I extracted the areas where tree cover is greater then 25%.

var treeCanopyCover = hansen.clip(india).select('treecover2000');
var greater25 = treeCanopyCover.gte(25);
var treeCover_greater25 = treeCanopyCover.updateMask(greater25);



var treeCanopyCoverVis = {
  min: 25.0,
 max: 100.0,
palette:
  ['3d3d3d', '080a02', '080a02', '080a02', '106e12', '37a930', '03ff17'],

};


Map.setCenter(82.5, 23.5);
 Map.addLayer(treeCover_greater25, treeCanopyCoverVis, 'Tree Canopy Cover');

Since the output is an image I can't appply 'filterBounds' to filter an image Collection over this area.Is there any way that I can convert this to polygon geometry so that I can filter my image collection over these forest areas?

Best Answer

You can try using reduceToVectors, but for a large area as India that would possibly run out of computation. I think it is better if you apply the geometry of India to filter the image collection on. You can then use updateMask to masked out all the pixels that are not forest in 2000 based on the Hansen dataset:

 // get landsat 5 images (as an example, could be any image collection)
 var landsat5 = ee.ImageCollection("LANDSAT/LT05/C01/T1_TOA")
  // filter bounds on the geometry of India
  .filterBounds(india)
  // filter date on the similar year as the tree cover (not necesarry)
  .filterDate('2000','2001');

// Mask out all the pixels that are tree cover
var maskedLandsat5 = landsat5.map(function(image){
  return image.updateMask(greater25);
});

Link code

Related Solutions

[GIS] Iterating over years for features in feature collection using Google Earth Engine

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

[GIS] Getting Accurate Pixel Area in Google Earth Engine

what you want is to force the projection of pixel area raster.

So, If you do this and use it to calculate stuff

var pa = ee.Image.pixelArea().reproject({crs:'EPSG:4326',scale:30})

or simply add the layer to map and check, the values do not change based on zoom level. It does take a while though. If you want to confirm it quickly, you can use the inspector tab/tool to check for values.

Best Answer

Related Solutions

[GIS] Iterating over years for features in feature collection using Google Earth Engine

[GIS] Getting Accurate Pixel Area in Google Earth Engine

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