elaOnMars · May 10, 2016 13:16 · May 9, 2016 · May 9, 2016 · May 9, 2016 · May 9, 2016
diff --git a/gistfile1.txt → greencities.js b/gistfile1.txt → greencities.js
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,135 @@
+// tables with germany shape and the cities we are using in our application
+var germany = ee.FeatureCollection('ft:1KDrYXBDlAx1fhcfmWRx7u_qqN2O_gwBNInjnGmnZ')
+var cities = ee.FeatureCollection('ft:1w4PgU3okfzwKFEIpH32oPMlOtei6hUWa9tkXv5Rt');
+
+// landsat properties we need to create our image collection over different years
+// we use a feature collection here, because we can easily filter it 
+var landsats = ee.FeatureCollection([
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LT5_L1T_TOA'), nir: 'B4', red: 'B3', from: 1984, to: 1992 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LT4_L1T_TOA'), nir: 'B4', red: 'B3', from: 1992, to: 1994 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LT5_L1T_TOA'), nir: 'B4', red: 'B3', from: 1994, to: 1999 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LE7_L1T_TOA'), nir: 'B4', red: 'B3', from: 1999, to: 2003 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LT5_L1T_TOA'), nir: 'B4', red: 'B3', from: 2003, to: 2012 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LE7_L1T_TOA'), nir: 'B4', red: 'B3', from: 2012, to: 2013 }),
+  ee.Feature(null, { collection: ee.ImageCollection('LANDSAT/LC8_L1T_TOA'), nir: 'B5', red: 'B4', from: 2013, to: 2016 })
+]);
+
+// color palette preview: http://gka.github.io/palettes/#colors=#101721,#282e36,#2f423d,#345744,#376d4b,#398552,#399b58,#37b35e,#30cc64,#24e56a,#00ff70|steps=11|bez=0|coL=0
+var palette = ['#101721','#282e36', '#2f423d', '#345744', '#376d4b', '#398552', '#399b58', '#37b35e', '#30cc64', '#24e56a', '#00ff70'];
+
+var startYear = 2005;
+var endYear = 2015;
+var startDay = '-06-01';
+var endDay = '-07-31';
+var ndviThresholdMin = 0.45;
+var ndviThresholdMax = 0.8;
+var cloudCoverMax = 5;
+var yearList = ee.List.sequence(startYear, endYear);
+
+Map.setCenter(10.5, 51.3, 6);
+
+// create an image collection with images between 
+// start- and endyear in summer months for germany
+var accumulateImages = function(year, imageCollection){
+  var startDate = ee.Date(ee.String(ee.Number(year).toInt()).cat(startDay));
+  var endDate = ee.Date(ee.String(ee.Number(year).toInt()).cat(endDay));
+  var landsat = getLandsatByYear(year);
+
+  var ndviCollection = ee.ImageCollection(landsat.get('collection'))
+    .filterBounds(germany)
+    .filterDate(startDate, endDate)
+    .filterMetadata('CLOUD_COVER', 'less_than', cloudCoverMax)
+    .map(addNDVI(landsat));
+
+  return ee.ImageCollection(imageCollection).merge(ndviCollection);
+}
+
+var resultCollection = yearList.iterate(accumulateImages, ee.ImageCollection([]));
+resultCollection = ee.ImageCollection(resultCollection);
+
+print(resultCollection);
+
+
+// to crossvalidate our result, we can randomly filter out 10% of the images
+// resultCollection = resultCollection
+//  .randomColumn('random')
+//  .sort('random')
+//  .limit(resultCollection.size().multiply(0.9).toInt());
+
+
+// create collection that only has the ndvi band
+// and reduce that collection to one image with the median reducer
+var resultCollectionReduced = ee.ImageCollection(resultCollection)
+  .select('ndvi')
+  .reduce(ee.Reducer.median());
+
+// add greenamount and center properties for all cities 
+cities = cities.map(function(feature){
+    feature = feature.set('center', ee.Feature(feature.centroid()).geometry().coordinates());
+    return feature.set('greenamount', addGreenamount(resultCollectionReduced, 0.45, feature));
+  });
+
+// export data as geojson
+Export.table(cities, 'greencities_export', { fileFormat: 'GeoJSON' });
+
+// add clipped layer to the map
+Map.addLayer(
+  resultCollectionReduced.clip(germany), 
+  { min: ndviThresholdMin, max: ndviThresholdMax, palette: palette }, 
+  'NDVI map'
+);
+
+var ndviRGB = resultCollectionReduced.visualize({
+  min: ndviThresholdMin,
+  max: ndviThresholdMax,
+  palette: palette
+});
+
+// export colored shape of germany
+Export.image(ndviRGB, 'germany_ndvi', {
+  scale: 30,
+  region: germany.geometry(),
+  maxPixels: 130000000000
+});
+
+
+
+// helper functions 
+
+function getLandsatByYear(year) {
+  return landsats
+    .filter(ee.Filter.lte('from', year)
+    .and(ee.Filter.gt('to', year)))
+    .first();
+}
+
+// add greenamount for a specific threshold for a feature
+function addGreenamount(ndviReduced, threshold, feature){
+  var ndviAll = ndviReduced.gte(-1);
+  var ndviHigh = ndviReduced.gte(threshold);
+
+  var allReducedSum = ndviAll.reduceRegion({
+    reducer: ee.Reducer.sum(),
+    geometry: feature.geometry(),
+    scale: 30
+  });
+
+  var partReducedSum = ndviHigh.reduceRegion({
+    reducer: ee.Reducer.sum(),
+    geometry: feature.geometry(),
+    scale: 30
+  });
+
+  var value_all = ee.Number(allReducedSum.get('ndvi_median'));
+  var value_high = ee.Number(partReducedSum.get('ndvi_median'));
+
+  return value_high.divide(value_all).multiply(100);
+}
+
+
+// returns an image with a new ndvi band with the given landsat bands  
+function addNDVI(landsat) {
+  return function(image) {
+    return image.addBands(image.normalizedDifference([landsat.get('nir'), landsat.get('red')]).rename('ndvi'));
+  }
+}