google-earth-enginegoogle-fusion-tables
I have upload a kml file including main metropolitans in us as a fusion table, I can print the featurecollection but cann't visualize it in code editor.What's wrong with my code?
var fc = ee.FeatureCollection('ft:1Eii75QFBlQcYILLBFsU6zv9LhgJLa8FPHNGSZijQ')
Map.addLayer(fc, {}, 'metro');
print('fc',fc)
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
As this is a common operation, there's a function to do it in one go, so you don't have to do your own multi-temporal aggregation: ui.Chart.image.seriesByRegion.
var modis = ee.ImageCollection('MODIS/006/MOD11A1');
var modisLST = modis.filterBounds(fc2)
.filterDate('2003-12-25', '2004-02-25')
.select('LST_Day_1km');
// Convert temperature to Celsius.
modisLST = modisLST.map(function(img){
return img.multiply(0.02).subtract(273.15).copyProperties(img, ['system:time_start'])
});
// Create a graph of the time-series.
var graph = ui.Chart.image.seriesByRegion({
imageCollection: modisLST,
regions: fc2,
reducer: ee.Reducer.mean()
})
print(graph)
If you click on the box in the upper right corner of the chart, you can download the CSV used to generate the chart.
If, for some reason, you still want to do your own aggregation, you should use reduceRegions to reduce each region separately. The way you're doing it with reduceRegion, you are indeed getting the mean of all the regions. The downside of doing it yourself is that you then have to rearrange the results into something the charting API can understand.
Best Answer
You can specify the property to use as the geometry by passing it as the second parameter to ee.FeatureCollection: