[GIS] Getting List elements from .reduceRegion() results in Google Earth Engine

google-earth-engine

I have a FeatureCollection that I am using .reduceRegions() over the Hansen Forest Loss dataset using ee.Reducer.fixedHistogram() to get the count of pixels per year/per feature.

The result is a feature which I can see the results in the 'histogram' property but can't select those results to store in another variable to do further analysis.

How can I select the results from the reducer (ex: how can I select 3.011764705882353 in the first element)?

I keep getting the following error:

ComputedObject (Error)
List.get, argument 'list': Invalid type. Expected: List<Object>. Actual: 
Float<dimensions=2>.

Here is the code I have:

var forest_years = forest.select('lossyear')

var test = forest_years.reduceRegions({
  collection:GB_patches,
  reducer: ee.Reducer.fixedHistogram(0,18,18),
  scale:30,
})


var first = ee.Feature(test.first())
var patch_years = ee.List(first.get('histogram'))
print (first)
print (patch_years)
print (patch_years.get(1))

Link to GEE code: https://code.earthengine.google.com/9ab9b35f4cc49b45f29386c70a0cb2cc

Best Answer

Yes, certainly it's a weir behavior. When you print(patch_years) it clearly says it's a List, but when trying to get a value it says that it is a Float<dimensions=2>, which matches with the column named 'histogram' in the FeatureCollection.

A workaround would be to bring it to the client side, and get it there:

var forest_years = forest.select('lossyear')

var test = forest_years.reduceRegions({
  collection:GB_patches,
  reducer: ee.Reducer.fixedHistogram(0,18,18),
  scale:30,
})


var first = ee.Feature(test.first())
var patch_years = ee.List(first.get('histogram'))
var patch_years_list = patch_years.getInfo()
print (first)
print (patch_years_list)
print (patch_years_list[1])

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 – Batch Processing Data of Multiple ROIs in Google Earth Engine

Here's an example that hopefully does something like what you want, but you'll need to modify it to meet your requirements for "manually selected marker points" and Sentinel 2.

In general, do NOT use for-loops or getInfo(). To be clear, DO NOT USE FOR LOOPS unless you have a very good reason to be doing so. If you don't know, use map() for all the reasons described here, here, here and here. (And check the same guides for why to NOT use getInfo() or convert to a list as I've done here.) The reason it's OK to do this here (necessary, in fact) is because Export is a client side function and you can't use a client function in map().

// Get some imagery to play with.
var landsat = ee.ImageCollection("LANDSAT/LC08/C01/T1")
    .filterDate('2016-01-01', '2017-01-01');

var composite = ee.Algorithms.Landsat.simpleComposite({
  collection: landsat,
  asFloat: true
});

var rgbVis = {bands: ["B4", "B3", "B2"], min:0, max: 0.3};
Map.addLayer(composite, rgbVis, "RGB");

// This is a hacky way to get a pixel grid at arbitrary resolution.
var pixels = ee.Image.random().multiply(10000000).toInt32()
    .reduceToVectors({
      reducer: ee.Reducer.countEvery(),
      geometry: Map.getBounds(true),
      geometryType: 'bb' ,
      eightConnected: false,
      scale: 20000,
      crs: 'EPSG:4326'
    });
Map.addLayer(pixels);

// Only do this is you have a few regions.  Not suitable for 
// large feature collections.
var pixelsList = pixels.toList(pixels.size());

// This is one of the few places in the EE API where you need 
// a for-loop and a getInfo() call.  Export is a client function.
for (var i=0; i<pixels.size().getInfo(); i++) {
  Export.image.toDrive({
    image: composite, 
    description: 'foo_' + i, 
    fileNamePrefix: 'foo_' + i, 
    region: ee.Feature(pixelsList.get(i)).geometry(), 
    scale: 30, 
  });
}

If you really need precise control over the export regions, you can make lists of coordinates and turn those into a collection of the ROIs you want to export. All that aside, you still need to click 'Run' on the exports. If you want it completely automated, use the Python API and ee.batch.Export followed by task.start().

Best Answer

Related Solutions

Google Earth Engine – Iterating Over Years for Feature Collection

Google Earth Engine – Batch Processing Data of Multiple ROIs in Google Earth Engine

Related Question