/*
*------------------------------------------------------------------------*
** @date 2024-11-19
** @title Process and download Landsat data collection 2 Level 2
** @description The script provides an example processing chain for data cleaning, mosaicking and downloading
** Landsat Collection 2 Level 2 for a specific area provided by the user. This example shows the processing for
** NDVI and NDMI indices and it is part of the AVOCADO package tutorial for continuous monitoring of forest
** change dynamics with satellite time series(Decuyper et al., 2022).
** @tutorial_url /uuaa/labgrs/proyectos/tutorial-to-the-anomaly-vegetation-change-detection-avocado
*------------------------------------------------------------------------*
*/
///////////////////////////////////////////////////////////////////////////////////

// START of input variables, change the following lines as suggested and as needed

////////////////////////////////////////////////////////////////////////////////////
// Specify the location of the before uploaded shapefile in your assets (see middle tab on the left!!!)

var input_polygon = 'users/joselastra/SMF_CONAF/pn_campana';

// Export folder in your google drive

var input_export_folder = 'Pudahuel_test';

// Start and end dates
var input_StartStr = ee.String('1990-01-01');
var input_FinishStr = ee.String('2022-12-31');

/* Specify needed ratios with either TRUE (if calculation is needed) or FALSE (if you are not interested)
available indices: NDVI (ndvi_ind) and NDMI (ndmi_ind).
Users can add more indices following the same approach.
*/

var ndvi_ind = ['TRUE'];
var ndmi_ind = ['TRUE'];

///////////////////////////////////////////////////////////////////////////////////////
// END of input variables.
// You should probably not change anything beyond this line, until the very last line
///////////////////////////////////////////////////////////////////////////////////////

// Buffer to download around the above area, use 0 for no buffer
var input_buffer = 0;

// Convert text string dates to date tpe
var Start = ee.Date(input_StartStr);
var Finish = ee.Date(input_FinishStr);

// Create a feature collection out of the fustion table id
var Polygon = ee.FeatureCollection(ee.String(input_polygon));

// Buffer the area of interest
var PolygonBuffer = input_buffer === 0 ? Polygon.first().geometry() : Polygon.first().geometry().buffer(input_buffer);

// Add stuff on the map
Map.addLayer(PolygonBuffer,null,'Buffer');
Map.centerObject(PolygonBuffer);

// Standard names to rename the bands regardless of collection
var selected_bands = ['blue','green','red','nir','swir','swir2','QA_PIXEL'];

// Applies scaling factors to optical bands.
var applyScaleFactors = function (image) {
var opticalBands = image.select('SR_.*').multiply(0.0000275).add(-0.2);
return image.addBands(opticalBands, null, true);
};

// Merge the 3 collections, select, and rename the bands to standard names

var Collection = ee.ImageCollection('LANDSAT/LT05/C02/T1_L2').map(applyScaleFactors).select(['SR_B1','SR_B2','SR_B3','SR_B4','SR_B5','SR_B7','QA_PIXEL'],selected_bands)
.merge(ee.ImageCollection('LANDSAT/LE07/C02/T1_L2').map(applyScaleFactors).select(['SR_B1','SR_B2','SR_B3','SR_B4','SR_B5','SR_B7','QA_PIXEL'],selected_bands))
.merge(ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').map(applyScaleFactors).select(['SR_B2','SR_B3','SR_B4','SR_B5','SR_B6','SR_B7','QA_PIXEL'],selected_bands))
.sort("system:time_start") // Sort by Date
.filterDate(Start,Finish) // Filter by date
.filterBounds(PolygonBuffer) // Filter by area
.filterMetadata("CLOUD_COVER","less_than",70) // Filter by cloud cover
.map(function(image) {return image.mask(image.select('QA_PIXEL') // apply cleaning with QA band
.remap([1,5440,21824],[1,1,1],0) // 1...21824 -> 1 = not masked
);

}
);

print('Collection of Landsat Scenes',Collection);

// Mosaicking by day
// Create list with dates
var Days = Finish.difference(Start, 'day');
var Dates = ee.List.sequence(0, Days.subtract(1)).map(function(Day){return Start.advance(Day,'day')});

// Function that does day mosaicking
var day_mosaicking = function day_mosaicking(date, newlist) {
// Cast
date = ee.Date(date);
newlist = ee.List(newlist);

// Filter collection between date and the next day
var filtered = Collection.filterDate(date, date.advance(1,'day'));
var first = filtered.first();

// Make the mosaic
var image = ee.Image(filtered.mosaic());
image = image.set('system:time_start',date.millis());
image = image.set('SATELLITE',first.get('SATELLITE'));
// Add the mosaic to a list only if the collection has images
return ee.List(ee.Algorithms.If(filtered.size(), newlist.add(image), newlist));
};

// Iterate through dates and create a collection with mosaicked images
Collection = ee.ImageCollection(ee.List(Dates.iterate(day_mosaicking, ee.List([]))));

// Calculate the normalized difference index (ndi), give it a name and merge all single band images
var mergeBands = function mergeBands(image, previous) {
return ee.Image(previous).addBands(image);
};

// NDVI calculation

if (ndvi_ind == 'TRUE'){

var input_index = "NDVI";
var index_bands = ['nir','red'];

var NDVI = Collection
.map(function(image) {return image.normalizedDifference(index_bands)
.rename(ee.String(input_index+'_').cat(ee.Date(image.get('system:time_start')).format('YYYYMMdd')));
})
.iterate(mergeBands, ee.Image([]));

var NDVI = ee.Image(NDVI).multiply(ee.Image(10000))
.round()
.toFloat()
.clip(PolygonBuffer);

// (iv) extracting the band layer names for dates to export a table of the dates
var asList = NDVI.bandNames().map(function (layer) {
return ee.Feature(null, {Date: ee.String(layer)});
});

var feature_date = ee.FeatureCollection(asList);

Export.image.toDrive({
image:NDVI,
description: 'Landsat_' + input_index + '_' + input_StartStr.getInfo() + '_' + input_FinishStr.getInfo(),
folder: input_export_folder,
region: PolygonBuffer,
scale: 30,
maxPixels: 10e10
});

Export.table.toDrive({
collection:feature_date,
description: 'Date_Landsat_' + input_index + '_' + input_StartStr.getInfo() + '_' + input_FinishStr.getInfo(),
folder: input_export_folder
});

}

// NDMI calculation
if (ndmi_ind == 'TRUE'){

var input_index = "NDMI";
var index_bands = ['nir','swir'];
var NDMI = Collection
.map(function(image) {return image.normalizedDifference(index_bands)
.rename(ee.String(input_index+'_').cat(ee.Date(image.get('system:time_start')).format('YYYYMMdd')));
})
.iterate(mergeBands, ee.Image([]));

var NDMI = ee.Image(NDMI).multiply(ee.Image(10000))
.round()
.toFloat()
.clip(PolygonBuffer);

// (iv) extracting the band layer names for dates to export a table of the dates
var asList = NDMI.bandNames().map(function (layer) {
return ee.Feature(null, {Date: ee.String(layer)});
});

var feature_date = ee.FeatureCollection(asList);

Export.image.toDrive({
image:NDMI,
description: 'Landsat_' + input_index + '_' + input_StartStr.getInfo() + '_' + input_FinishStr.getInfo(),
folder: input_export_folder,
region: PolygonBuffer,
scale: 30,
maxPixels: 10e10
});

Export.table.toDrive({
collection:feature_date,
description: 'Date_Landsat_' + input_index + '_' + input_StartStr.getInfo() + '_' + input_FinishStr.getInfo(),
folder: input_export_folder
});
}