Lab 17 - Health Applications Part 1

Part 1: Getting Base Data into GEE

Start by loading the study area - woredas (districts) in the Amhara region of Ethiopia:

// Import study area data
var woredas = ee.FeatureCollection("users/sounny/amhara_woredas_20170207");

// Create region outer boundary to filter products on
var amhara = woredas.geometry().bounds();

Visualize the woreda boundaries on the map:

// Create an empty image into which to paint the features
var empty = ee.Image().byte();
// Paint all the polygon edges
var outline = empty.paint({
    featureCollection: woredas,
    color: 1,
    width: 1
});
// Add woreda boundaries to the map
Map.setCenter(38, 11.5, 7);
Map.addLayer(outline, {palette: '000000'}, 'Woredas');

Loading Remote Sensing Data

We'll use four data products for disease modeling:

var gpm = ee.ImageCollection('NASA/GPM_L3/IMERG_V06');
var LSTTerra8 = ee.ImageCollection('MODIS/061/MOD11A2')
    .filterDate('2001-06-26', Date.now());
var brdfReflect = ee.ImageCollection('MODIS/006/MCD43A4');
var brdfQa = ee.ImageCollection('MODIS/006/MCD43A2');

Part 2: Data Preparation

Define your analysis date range:

// Define requested start and end dates
var reqStartDate = ee.Date('2021-10-01');
var reqEndDate = ee.Date('2021-11-30');

Handle LST 8-day composite dates:

// Get date of first LST image
var LSTEarliestDate = LSTTerra8.first().date();
// Filter collection to dates before requested start
var priorLstImgCol = LSTTerra8.filterDate(LSTEarliestDate, reqStartDate);
// Get the latest date of earlier images
var LSTPrevMax = priorLstImgCol.reduceColumns({
    reducer: ee.Reducer.max(),
    selectors: ['system:time_start']
});
var LSTStartDate = ee.Date(LSTPrevMax.get('max'));
print('LSTStartDate', LSTStartDate);

Part 3: Zonal Statistics on Precipitation

Filter and calculate daily precipitation:

// Filter GPM by date
var gpmFiltered = gpm
    .filterDate(precipStartDate, reqEndDate.advance(1, 'day'))
    .filterBounds(amhara)
    .select('precipitationCal');

// Function to calculate daily precipitation
function calcDailyPrecip(curdate) {
    curdate = ee.Date(curdate);
    var curyear = curdate.get('year');
    var curdoy = curdate.getRelative('day', 'year').add(1);
    var totprec = gpmFiltered
        .filterDate(curdate, curdate.advance(1, 'day'))
        .select('precipitationCal')
        .sum()
        .multiply(0.5)  // Convert from mm/hr to mm (every half-hour)
        .rename('totprec');
    return totprec
        .set('doy', curdoy)
        .set('year', curyear)
        .set('system:time_start', curdate);
}

Summarize precipitation by woreda:

// Function to calculate zonal statistics by woreda
function sumZonalPrecip(image) {
    var image2 = image.addBands([
        image.metadata('doy').int(),
        image.metadata('year').int()
    ]);
    var output = image2.select(['year', 'doy', 'totprec'])
        .reduceRegions({
            collection: woredas,
            reducer: ee.Reducer.mean(),
            scale: 1000
        });
    return output;
}
var precipWoreda = precipSummary.map(sumZonalPrecip);
var precipFlat = precipWoreda.flatten();

Part 4: Land Surface Temperature

Calculate mean LST from day and night observations with quality filtering:

// Filter by QA information
function filterLstQa(image) {
    var qaday = image.select(['QC_Day']);
    var qanight = image.select(['QC_Night']);
    var daymask = qaday.rightShift(6).lte(2);
    var nightmask = qanight.rightShift(6).lte(2);
    var outimage = image.select(['LST_Day_1km', 'LST_Night_1km']);
    var outmask = ee.Image([daymask, nightmask]);
    return outimage.updateMask(outmask);
}

// Rescale and convert to Celsius
function rescaleLst(image) {
    var LST_day = image.select('LST_Day_1km')
        .multiply(0.02).subtract(273.15).rename('LST_day');
    var LST_night = image.select('LST_Night_1km')
        .multiply(0.02).subtract(273.15).rename('LST_night');
    var LST_mean = image.expression(
        '(day + night) / 2', {
            'day': LST_day.select('LST_day'),
            'night': LST_night.select('LST_night')
        }).rename('LST_mean');
    return image.addBands(LST_day).addBands(LST_night).addBands(LST_mean);
}

Part 5: Spectral Index - NDWI

Calculate NDWI (Normalized Difference Water Index) to assess vegetation moisture:

// Calculate spectral indices
function calcBrdfIndices(image) {
    var curyear = ee.Date(image.get('system:time_start')).get('year');
    var curdoy = ee.Date(image.get('system:time_start'))
        .getRelative('day', 'year').add(1);
    var ndwi6 = image.normalizedDifference(['nir_r', 'swir2_r'])
        .rename('ndwi6');
    return image.addBands(ndwi6)
        .set('doy', curdoy)
        .set('year', curyear);
}

Part 6: Map Display

Visualize the calculated environmental variables:

var displayDate = ee.Date('2021-10-01');

// Filter to display date
var precipImage = dailyPrecip
    .filterDate(displayDate, displayDate.advance(1, 'day'))
    .first().select('totprec');
var LSTmImage = dailyLst
    .filterDate(displayDate, displayDate.advance(1, 'day'))
    .first().select('LST_mean');
var ndwi6Image = dailyBrdf
    .filterDate(displayDate, displayDate.advance(1, 'day'))
    .first().select('ndwi6');

// Add layers to map
Map.addLayer(precipImage, {min: 0, max: 20, palette: ['f7fbff', '08306b']}, 
    'Precipitation', true, 0.75);
Map.addLayer(LSTmImage, {min: 0, max: 40, palette: ['fff5f0', '67000d']}, 
    'LST Mean', false, 0.75);
Map.addLayer(ndwi6Image, {min: 0, max: 1, palette: ['ffffe5', '004529']}, 
    'NDWI6', false, 0.75);

Part 7: Exporting Data

Export the summarized data as CSV files to Google Drive:

// Export precipitation data
Export.table.toDrive({
    collection: precipFlat,
    description: precipFilename,
    selectors: ['wid', 'woreda', 'doy', 'year', 'totprec']
});

// Export LST data
Export.table.toDrive({
    collection: LSTFlat,
    description: LSTFilename,
    selectors: ['wid', 'woreda', 'doy', 'year', 'LST_day', 'LST_night', 'LST_mean']
});

// Export spectral data
Export.table.toDrive({
    collection: brdfFlat,
    description: brdfFilename,
    selectors: ['wid', 'woreda', 'doy', 'year', 'ndwi6']
});