nagoya_jan12

///////////////////////////////////////////////////////////////
//                                                           //
//  Accessing Google Earth Engine, GeoQuery, GeoBoundaries,  //
//                and other new data sources                 //
//                                                           //
//          DATA SCIENCE FOR SUSTAINABLE DEVELOPMENT         //                     
//                     JANUARY 12, 2022                      //
//                                                           //
///////////////////////////////////////////////////////////////

// Self-paced GEE tutorials: https://developers.google.com/earth-engine/tutorials/tutorials

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//  PART 1  //
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// 1 - UPLOAD PHL ADM1 SHAPEFILE FROM GEOBOUNDARIES AS AN ASSET

// Read more: https://developers.google.com/earth-engine/guides/table_upload
// Read more: https://thegeoict.com/blog/2019/08/05/uploading-a-shapefile-to-google-earth-engine/

// 2 - IMPORT THE SHAPEFILE INTO THE CONSOLE AS A FEATURECOLLECTION

// In the Earth Engine API, a FeatureCollection is defined as a group of related features.
// A shapefile is imported as a group of features. The shapefile we downloaded from geoBoundaries
// is a group of individual polygons (each province in the Philippines) grouped into one.
// Each province is a feature, so the whole shapefile is a FeatureCollection, or group of features.

// Read more: https://developers.google.com/earth-engine/guides/feature_collections

var phl_adm1 = ee.FeatureCollection('users/lkmills/geoBoundaries-PHL-ADM1_simplified1');
print('PHL ADM1: ', phl_adm1); // Print data in right-hand console

Map.setCenter(122.69, 11.68, 6); // Center the map on the Philippines
Map.addLayer(phl_adm1, {color: 'green'}, 'PHL ADM1'); // Visualize data on map

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//  PART 2  //
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// COLLECT NIGHTTIME LIGHT DATA FOR EACH PROVINCE

// Import nighttime lights data from the GEE data catalog
// Read more: https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS
var nighttimeLights = ee.ImageCollection("NOAA/DMSP-OLS/NIGHTTIME_LIGHTS").filter(ee.Filter.date('2000-01-01', '2014-01-01'));

// Make a list of years (2000 - 2013) for which we want nighttime lights data 
var years_nighttime = ee.List.sequence(2000, 2013, 1).map(function(year){
  var start = ee.Number(year);
  var end = start.add(ee.Number(1));
  var sumYear = nighttimeLights.select('stable_lights')
                              .filter(ee.Filter.calendarRange(start, end, 'year')).mean();
  return sumYear.set('year', start);
});

// Get the total annual luminosity for each province
var nighttimeLights = ee.ImageCollection(years_nighttime).map(function(img){
  var rR = img.reduceRegions({collection: phl_adm1,
                            reducer: ee.Reducer.mean(),
                            scale: 250,
                            tileScale: 4});
  return rR.map(function(feat){
    return feat.set('year', img.get('year'));
  });
  
}).flatten();
print('Nighttime Lights: ', nighttimeLights);

// Export the data to Google Drive
Export.table.toDrive({
  collection: nighttimeLights, 
  description: 'nighttimeLights', 
  selectors: ['shapeName', 'year', 'mean'] 
});

//////////////
//  PART 3  //
//////////////

// COLLECT VEGETATION HEALTH DATA FOR EACH PROVINCE

// Function for the Normalized Difference Vegetation Index (NDVI).
// NDVI is used to measure vegetation health on a scale of 0 to 1.
// Lower values indicate barren or unhealthy vegetation. Higher values
// indicate greenness and healthy vegetation.
var modisNDVI = ee.ImageCollection('MODIS/006/MOD13Q1').filterBounds(phl_adm1)
              .select('NDVI')
              .map(function(img){
                        var rescaled_NDVI = img.select('NDVI')
                                                .multiply(0.0001)
                                                .rename('NDVI_rescaled');
                        return img.addBands(rescaled_NDVI);});

// Make a list of years (2000 - 2021) for which we want mean NDVI data  
var years_ndvi = ee.List.sequence(2000, 2021, 1).map(function(year){
  var start = ee.Number(year);
  var end = start.add(ee.Number(1));
  var ndviMeanYear = modisNDVI.select('NDVI_rescaled')
                              .filter(ee.Filter.calendarRange(start, end, 'year')).mean();
  return ndviMeanYear.set('year', start);
});

// Get the annual mean NDVI for each province
var ndviMean = ee.ImageCollection(years_ndvi).map(function(img){
  var rR = img.reduceRegions({collection: phl_adm1,
                            reducer: ee.Reducer.mean(),
                            scale: 250,
                            tileScale: 4});
  return rR.map(function(feat){
    return feat.set('year', img.get('year'));
  });
  
}).flatten();
print('Mean NDVI: ', ndviMean.first());

// Export the data to Google Drive
Export.table.toDrive({
  collection: ndviMean, 
  description: 'ndviMean', 
  selectors: ['shapeName', 'year', 'mean'] 
});

//////////////
//  PART 4  //
//////////////

// Function to calculate mean elevation for each province
var elevation_func = function(feature) {
  var collection = ee.Image('CGIAR/SRTM90_V4').select('elevation');
  var clipped = collection.clip(feature.geometry());
  var meanDictionary = clipped.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: feature.geometry(),
    scale: 30,
    maxPixels: 1e9
  });
  return feature.set({elevation: meanDictionary.get('elevation')});
};

var dataset = ee.Image('CGIAR/SRTM90_V4').select('elevation');
Map.addLayer(dataset, {min: 0, max: 60}, 'elevation data');


// Apply elevation function to the province data
var elevation = phl_adm1.map(elevation_func);
print('Elevation: ', elevation);

// Export the data to Google Drive
Export.table.toDrive({
  collection: elevation, 
  description: 'elevation', 
  selectors: ['shapeName', 'elevation'] 
});