Snow And Forest

/**** Start of imports. If edited, may not auto-convert in the playground. ****/
var waterImages = ee.Image("MODIS/MOD44W/MOD44W_005_2000_02_24"),
    snowCover = ee.ImageCollection("MODIS/006/MOD10A1"),
    dataset = ee.Image("UMD/hansen/global_forest_change_2022_v1_10"),
    maps = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017");
/***** End of imports. If edited, may not auto-convert in the playground. *****/

var startDoy = 1;

var startYear = 2000;
var endYear = 2019;

var startDate;
var startYear;

function addDateBands(img) {
  // Get image date.
  var date = img.date();
  // Get calendar day-of-year.
  var calDoy = date.getRelative('day', 'year');
  // Get relative day-of-year; enumerate from user-defined startDoy.
  var relDoy = date.difference(startDate, 'day');
  // Get the date as milliseconds from Unix epoch.
  var millis = date.millis();
  // Add all of the above date info as bands to the snow fraction image.
  var dateBands = ee.Image.constant([calDoy, relDoy, millis, startYear])
    .rename(['calDoy', 'relDoy', 'millis', 'year']);
  // Cast bands to correct data type before returning the image.
  return img.addBands(dateBands)
    .cast({'calDoy': 'int', 'relDoy': 'int', 'millis': 'long', 'year': 'int'})
    .set('millis', millis);
}

var waterMask = waterImages.select('water_mask').not();

var completeCol = snowCover.select('NDSI_Snow_Cover');

// Pixels must have been 10% snow covered for at least 2 weeks in 2018.
var snowCoverEphem = completeCol.filterDate('2018-01-01', '2019-01-01')
  .map(function(img) {
    return img.gte(10);
  })
  .sum()
  .gte(14);

// Pixels must not be 10% snow covered more than 124 days in 2018.
var snowCoverConst = completeCol.filterDate('2018-01-01', '2019-01-01')
  .map(function(img) {
    return img.gte(10);
  })
  .sum()
  .lte(124);

var analysisMask = waterMask.multiply(snowCoverEphem).multiply(snowCoverConst);

var years = ee.List.sequence(startYear, endYear);

var annualList = years.map(function(year) {
  // Set the global startYear variable as the year being worked on so that
  // it will be accessible to the addDateBands mapped to the collection below.
  startYear = year;
  // Get the first day-of-year for this year as an ee.Date object.
  var firstDoy = ee.Date.fromYMD(year, 1, 1);
  // Advance from the firstDoy to the user-defined startDay; subtract 1 since
  // firstDoy is already 1. Set the result as the global startDate variable so
  // that it is accessible to the addDateBands mapped to the collection below.
  startDate = firstDoy.advance(startDoy-1, 'day');
  // Get endDate for this year by advancing 1 year from startDate.
  // Need to advance an extra day because end date of filterDate() function
  // is exclusive.
  var endDate = startDate.advance(1, 'year').advance(1, 'day');
  // Filter the complete collection by the start and end dates just defined.
  var yearCol = completeCol.filterDate(startDate, endDate);
  // Construct an image where pixels represent the first day within the date
  // range that the lowest snow fraction is observed.
  var noSnowImg = yearCol
    // Add date bands to all images in this particular collection.
    .map(addDateBands)
    // Sort the images by ascending time to identify the first day without
    // snow. Alternatively, you can use .sort('millis', false) to
    // reverse sort (find first day of snow in the fall).
    .sort('millis')
    // Make a mosaic composed of pixels from images that represent the
    // observation with the minimum percent snow cover (defined by the
    // NDSI_Snow_Cover band); include all associated bands for the selected
    // image.
    .reduce(ee.Reducer.min(5))
    // Rename the bands - band names were altered by previous operation.
    .rename(['snowCover', 'calDoy', 'relDoy', 'millis', 'year'])
    // Apply the mask.
    .updateMask(analysisMask)
    // Set the year as a property for filtering by later.
    .set('year', year);

  // Mask by minimum snow fraction - only include pixels that reach 0
  // percent cover. Return the resulting image.
  return noSnowImg.updateMask(noSnowImg.select('snowCover').eq(0));
});

var annualCol = ee.ImageCollection.fromImages(annualList);

// Define the years to difference.
var firstYear = 2005;
var secondYear = 2015;

// Calculate difference image.
var firstImg = annualCol.filter(ee.Filter.eq('year', firstYear))
  .first().select('calDoy');
var secondImg = annualCol.filter(ee.Filter.eq('year', secondYear))
  .first().select('calDoy');
var dif = secondImg.subtract(firstImg);

// Define visualization arguments.
var visArgs = {
  min: -15,
  max: 15,
  palette: ['b2182b', 'ef8a62', 'fddbc7', 'f7f7f7', 'd1e5f0', '67a9cf', '2166ac']};

// Map it.
var usa = maps.filter(ee.Filter.eq('country_co', 'US'));

Map.centerObject(usa, 6);
Map.addLayer(usa);

dif = dif.clip(usa);
Map.addLayer(dif, visArgs, '2015-2005 first day no snow dif');

var treeLossVisParam = {
  bands: ['lossyear'],
  min: 0,
  max: 22,
  palette: ['yellow', 'red']
};
dataset = dataset.clip(usa);
Map.addLayer(dataset, treeLossVisParam, 'tree loss year');