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pre_processing_thesis_mh.js
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pre_processing_thesis_mh.js
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Imports: Sentinel-2 MSI: MultiSpectral Instrument, Level-1C
ALOS DSM: Global 30m
// Author:
// Meylin Herrera // mhscience525@gmail.com
//
// Date:
// 29/08/2019
//
// Description:
// This script is used to analyze landslide events;
// Generates a set cloud-free images pre-and-pos-event that includes ndvi, slope, elevation, glcm
// Generates the image difference using band rationing (red/green) and includes ndvi, slope, elevation, glcm.
//
// Imports:
// - Add to GEE sentinel2 and dem_alos
//
// Dependencies:
// - geetools https://github.com/fitoprincipe/geetools-code-editor
//
// Input:
// - A feature collection, from google fusion table (to generate a fusion table see https://fusiontables.google.com)
// with the following mandatory colums [landslide_id, event_date, longitude, latidude]
// For more information to read fusion tables check ee.FeatureCollection
// you can point to your fusion tables by changing fusion_table_id (do not use more than 50 rows per table)
//
// Output:
// - A series of images to download to Google drive, Check Export_to_drive;
// diff_id contains the image difference
// post_description_id contains the pos-event image
// pre_description_id contains the pre-event image
//
// Properties:
// Change the values under PROPERTIES, as default search from the event date of free images is 4 and for a composite is 12 months.
//
// How to use this code:
// 1. Set your Google Drive and Fusion tables.
// 2. Rename the Properties; Use your settings from step 1.
// 3. Check the console tab, wait until your images are ready to download.
// 4. Download the images to google drive, from Tasks tab
//Properties
//Set your appropiate google drive-folder and landslide fusion table,
//for more information look the header of this file.
var my_google_drive_folder = "Landslides_images_June2019";
var fusion_table_id = 'ft:1pt_zzsvIVF1WawnCIinydD9y8NQrLuvrZgsv89OB';
var free_cloud_month_range = 4;
var composite_image_search_range = 12;
var download_prefix_description = 'landslide_test';
// Dependencies
var cloud_masks = require('users/fitoprincipe/geetools:cloud_masks');
var sentinel2function = cloud_masks.sentinel2();
var rgbVis = {
min : 0.0,
max : 3000,
bands : ['B4', 'B3', 'B2'],
};
// Util: prepare image for download
var create_download_image = function (id, image, region, description) {
var download_image = {
id : id,
image : image,
region : region,
description : description + '_' + id
};
return download_image;
};
// Util: Exports an image to drive within a region and a description
var export_to_drive = function (image, region, description) {
Export.image.toDrive({
image : image, //.visualize(rgbVis),
description : description,
scale : 10,
folder : my_google_drive_folder,
region : region,
});
};
// Util: Download a collection of images to drive within a region and a description as harcoded variables
var download_images_to_drive = function (download_images) {
//Calls export_to_drive over a collection
for (var i = 0; i < download_images.length; i++) {
var image = download_images[i].image;
var description = download_images[i].description;
var region = download_images[i].region;
export_to_drive(image, region, description);
}
};
// Main: process_landslides_images receives a collection of landslides locations, free_cloud range, composite range and
// download description,
var process_landslides_images = function (satellite, landslides, cloud_months, composite_months, description_prefix) {
landslides.toList(landslides.size()).evaluate(function (landslide_list) {
var min_images = landslide_list.length;
var collector = create_image_collector(min_images);
print('Processing Images');
for (var i = 0; i < landslide_list.length; i++) {
var landslide_item = ee.Feature(landslide_list[i]);
var description_post = 'post_' + description_prefix;
var description_pre = 'pre_' + description_prefix;
//Start 2 pre-and-post image proceses,
process_images_from_landslide_post(satellite, landslide_item, cloud_months, composite_months, description_post, collector);
process_images_from_landslide_pre(satellite, landslide_item, cloud_months, composite_months, description_pre, collector);
}
});
};
// Main: Attempts to retrive the image with the lower percentage of clouds
var find_less_cloudy_image = function (images, cloud_index, composite_case, callback, context) {
print('Looking for free cloud images');
var sort_post = true;
if (context === 'pre') {
sort_post = false;
}
var increase = 5;
var composition_threshold = 30;
var error = 110;
ee.ImageCollection(images).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', cloud_index)).size().gt(0).evaluate(function (result) {
if (result) {
var image_found = ee.ImageCollection(images).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', cloud_index)).map(sentinel2function).sort('CLOUDY_PIXEL_PERCENTAGE', true).first(); //'system:time_start'
callback(ee.Image(image_found));
} else if (cloud_index >= composition_threshold) {
composite_case();
} else if (cloud_index >= error) {
print('Something wrong just happened; Image cloud index greater than 100, nothing found :(');
return -1;
} else {
var increment = cloud_index + increase;
find_less_cloudy_image(images, increment, composite_case, callback, context);
}
});
};
// Main: Creates a composite image
var composite_image = function (images, cloud_index, callback) {
var up = 10;
var error = 110;
ee.ImageCollection(images).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', cloud_index)).size().gt(0).evaluate(function (result) {
if (result) {
var composite = ee.ImageCollection(images).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', cloud_index)).map(sentinel2function).median();
callback(ee.Image(composite));
} else if (cloud_index > 110) {
print('Something wrong just happened during composite; Image cloud index greater than 100, nothing found :(');
return -1;
} else {
var increment = cloud_index + up;
composite_image(images, increment, callback);
}
});
};
// Util: Retrive coordinates
var get_coordinates = function (landslide, callback) {
var coordinates = landslide.geometry().coordinates();
ee.Geometry.Point(coordinates).evaluate(
function (result) {
callback(result);
});
};
//Util: calculate texture parameters
var texture = function (image) {
//compute the texture
var nir = image.select('B8');
// Define a neighborhood with a kernel.
var square = ee.Kernel.square({
radius : 4
});
// Compute entropy and display.
var entropy = nir.entropy(square);
var glcm = nir.glcmTexture({
size : 4
});
return glcm;
};
// Main: Creates a region and exectute a callback fucntion with point, region as parameters
var process_region_and_coordinates = function (landslide, callback) {
var create_region = function (point) {
var region = ee.Geometry.Point([point.coordinates[0], point.coordinates[1]]).buffer(2500).bounds();
region.evaluate(function (region_result) {
callback(point, region_result);
});
};
get_coordinates(landslide, create_region);
};
// Main: Process images after the landslide event
var process_images_from_landslide_post = function (satellite, landslide, adv_months_cloud_case, adv_months_composite_case, description, image_collector) {
var context = 'post';
//3.B Composite Path, if the cloud index percetange is above the threshold this function will be called
//see find_less_cloudy_image
var composite_case = function () {
//1 Composite runs on a 8 Months period
var process_composite_event_date = function (landslide) {
var event_date = ee.Date(landslide.get('event_date'));
event_date.evaluate(function (ev_result) {
process_composite_post_event_date(ev_result);
});
};
//2 Calculate the end of range after the landslide
var process_composite_post_event_date = function (ev_result) {
var post_event = ee.Date(ev_result.value).advance(ee.Number(adv_months_composite_case), 'Month');
post_event.evaluate(function (post_ev_result) {
var event_date = ee.Date(ev_result.value);
var post_event = ee.Date(post_ev_result.value);
process_composite_event_time_range(event_date, post_event);
});
//3 With the images in range from (1) and (2) Process the images see process_images function
var process_composite_event_time_range = function (event_date, post_event) {
var get_image_from_composite = function (coordinates, region) {
var images = satellite.filterBounds(coordinates).filterDate(event_date, post_event);
process_composite_images(images, context, region, landslide, description, image_collector);
};
process_region_and_coordinates(landslide, get_image_from_composite);
};
};
process_composite_event_date(landslide);
};
//3 With the images in range from (1) and (2) Process the images see process_images function
var process_event_time_range = function (event_date, post_event) {
var get_image = function (coordinates, region) {
var images = satellite.filterBounds(coordinates).filterDate(event_date, post_event);
process_images(images, context, region, composite_case, landslide, description, image_collector);
};
process_region_and_coordinates(landslide, get_image);
};
//1 Get the event date from landlide event date history
var process_event_date = function (landslide) {
var event_date = ee.Date(landslide.get('event_date'));
event_date.evaluate(function (ev_result) {
process_post_event_date(ev_result);
});
};
//2 Calculate the end of range after the landslide
var process_post_event_date = function (ev_result) {
var post_event = ee.Date(ev_result.value).advance(adv_months_cloud_case, 'Month');
post_event.evaluate(function (post_ev_result) {
var event_date = ee.Date(ev_result.value);
var post_event = ee.Date(post_ev_result.value);
process_event_time_range(event_date, post_event);
});
};
process_event_date(landslide);
};
// Main: Process images before the landslide event
var process_images_from_landslide_pre = function (satellite, landslide, back_months_cloud_case, back_months_composite_case, description, image_collector) {
var context = 'pre';
//3.B Composite Path, if the cloud index percetange is above the threshold this function will be called
//see find_less_cloudy_image
var composite_case = function () {
//1 Composite runs on a 8 Months period
var process_composite_event_date = function (landslide) {
var event_date = ee.Date(landslide.get('event_date'));
event_date.evaluate(function (ev_result) {
process_composite_post_event_date(ev_result);
});
};
//2 Calculate the end of range after the landslide
var process_composite_post_event_date = function (end_date_result) {
//3 With the images in range from (1) and (2) Process the images see process_images function
var process_composite_event_time_range = function (event_date, post_event) {
var get_image_from_composite = function (coordinates, region) {
var images = satellite.filterBounds(coordinates).filterDate(event_date, post_event);
process_composite_images(images, context, region, landslide, description, image_collector);
};
process_region_and_coordinates(landslide, get_image_from_composite);
};
var start_event = ee.Date(end_date_result.value).advance(ee.Number(-1 * back_months_composite_case), 'Month');
start_event.evaluate(function (start_ev_result) {
var start_date = ee.Date(start_ev_result.value);
var end_date = ee.Date(end_date_result.value);
process_composite_event_time_range(start_date, end_date);
});
};
process_composite_event_date(landslide);
};
//3 With the images in range from (1) and (2) Process the images see process_images function
var process_event_time_range = function (event_date, post_event) {
var get_image = function (coordinates, region) {
var images = satellite.filterBounds(coordinates).filterDate(event_date, post_event);
process_images(images, context, region, composite_case, landslide, description, image_collector);
};
process_region_and_coordinates(landslide, get_image);
};
//1 Get the event date from landlide event date history
var process_event_date = function (landslide) {
var event_date = ee.Date(landslide.get('event_date'));
event_date.evaluate(function (ev_result) {
process_post_event_date(ev_result);
});
};
//2 Calculate the end of range after the landslide
var process_post_event_date = function (end_date_result) {
var start_event = ee.Date(end_date_result.value).advance(-1 * back_months_cloud_case, 'Month');
start_event.evaluate(function (start_ev_result) {
var start_date = ee.Date(start_ev_result.value);
var end_date = ee.Date(end_date_result.value);
process_event_time_range(start_date, end_date);
});
};
process_event_date(landslide);
};
var process_composite_images = function (images, context, region, landslide, description, download_body) {
print('Running Composite');
//4 download the images
var download = function () {
print('Waiting to complete');
if (download_body.retrieve_done()) {
download_body.calculate_difference();
download_images_to_drive(download_body.list_pre);
download_images_to_drive(download_body.list_post);
}
};
//2 After cleaning the images, add the bands
var add_bands_callback = function (image_input) {
// Compute NDVI from the NAIP imagery.
var naipNDVI = image_input.normalizedDifference(['B8', 'B4']);
var test = naipNDVI.reduceNeighborhood({ // Compute standard deviation (SD) as texture of the NDVI.
reducer : ee.Reducer.stdDev(),
kernel : ee.Kernel.circle(7),
});
// // Display the results.
Map.addLayer(test, {
min : 0,
max : 0.3
}, 'SD of NDVI', false);
var imageRGB = image_input.visualize({
bands : ['B5', 'B4', 'B3'],
min : 0.0,
max : 3000
});
// print ('imageRGB',imageRGB )
//calculate ndvi
var nir = image_input.select('B8');
var red = image_input.select('B4');
var blue = image_input.select('B2');
var ndvi = nir.subtract(red).divide(nir.add(red)).rename('NDVI');
var green = image_input.select('B3');
var gndvi = nir.subtract(green).divide(nir.add(green)).rename('GNDVI');
//calculate brightness
var brightness = (((red.add(green).add(blue)).divide(3)).divide(1000)).rename('brightness');
//calculate slope and elevation from ALOS and adapt projection to input image
var imageProjection = ndvi.projection(); //get input imagery projection
var slope_alos = ee.Terrain.slope(dem_alos).rename('slope');
var dem_projected = dem_alos.reproject({
crs : imageProjection
});
var slope_projected = slope_alos.reproject({
crs : imageProjection
});
//merge the bands
var image = image_input.float().select('B4', 'B3', 'B2', 'B8').addBands(ndvi.float().select('NDVI'))
.addBands(slope_projected.float().select('slope'))
.addBands(dem_projected.float().select('AVE').rename('elevation'))
.addBands(test.float().select('nd_stdDev'))
.addBands(imageRGB.float().select('vis-red', 'vis-green', 'vis-blue'))
.addBands(gndvi.float().select('GNDVI'))
.addBands(brightness.float().select('brightness'));
append_to_download(image);
};
//3 Create download object with image, region and description
var append_to_download = function (image) {
var l_id = landslide.get('landslide_id');
l_id.evaluate(function (l_id_result) {
var download_image = create_download_image(l_id_result, image, region, description);
if (context === 'pre') {
download_body.list_pre.push(download_image);
} else if (context === 'post') {
download_body.list_post.push(download_image);
} else {
print('Error Context not found');
}
download();
});
};
//1 Run the cloud algorithm to get a set of clean images
composite_image(images, 50, add_bands_callback);
};
var process_images = function (images, context, region, composite_case, landslide, description, download_body) {
//4 download the images
var download = function () {
print('Waiting to complete');
if (download_body.retrieve_done()) {
download_body.calculate_difference();
download_images_to_drive(download_body.list_pre);
download_images_to_drive(download_body.list_post);
}
};
//2 After cleaning the images, add the bands
var add_bands_callback = function (image_input) {
//calculate image texture
var image_texture = texture(image_input);
// Compute NDVI from the NAIP imagery.
var naipNDVI = image_input.normalizedDifference(['B8', 'B4']);
var test = naipNDVI.reduceNeighborhood({ // Compute standard deviation (SD) as texture of the NDVI.
reducer : ee.Reducer.stdDev(),
kernel : ee.Kernel.circle(7),
});
// // Display the results.
Map.addLayer(test, {
min : 0,
max : 0.3
}, 'SD of NDVI', false);
var imageRGB = image_input.visualize({
bands : ['B5', 'B4', 'B3'],
min : 0.0,
max : 3000
});
//calculate ndvi
var nir = image_input.select('B8');
var red = image_input.select('B4');
var blue = image_input.select('B2');
var ndvi = nir.subtract(red).divide(nir.add(red)).rename('NDVI');
//calculate gndvi
var green = image_input.select('B3');
var gndvi = nir.subtract(green).divide(nir.add(green)).rename('GNDVI');
var brightness = (((red.add(green).add(blue)).divide(3)).divide(1000)).rename('brightness');
var imageProjection = ndvi.projection(); //get input imagery projection
var slope_alos = ee.Terrain.slope(dem_alos).rename('slope');
var dem_projected = dem_alos.reproject({
crs : imageProjection
});
var slope_projected = slope_alos.reproject({
crs : imageProjection
});
//merge the bands
var image = image_input.float().select('B4', 'B3', 'B2', 'B8').addBands(ndvi.float().select('NDVI'))
.addBands(slope_projected.float().select('slope'))
.addBands(dem_projected.float().select('AVE').rename('elevation'))
// .addBands(image_texture.float().select('B8_contrast'))
.addBands(test.float().select('nd_stdDev'))
.addBands(imageRGB.float().select('vis-red', 'vis-green', 'vis-blue'))
// .addBands(hsv2.float().select('vis-red', 'vis-green', 'vis-blue'))
.addBands(gndvi.float().select('GNDVI'))
.addBands(brightness.float().select('brightness'));
append_to_download(image);
};
//3 Create download object with image, region and description
var append_to_download = function (image) {
var l_id = landslide.get('landslide_id');
l_id.evaluate(function (l_id_result) {
var download_image = create_download_image(l_id_result, image, region, description);
if (context === 'pre') {
download_body.list_pre.push(download_image);
} else if (context === 'post') {
download_body.list_post.push(download_image);
} else {
print('Error Context not found');
}
download();
});
};
//1 Run the cloud algorithm to get a set of clean images
find_less_cloudy_image(images, 0, composite_case, add_bands_callback, context);
};
// The image collector acts as a sychronization objects that, gather all the images from pre-and-post processes in GEE servers, once all images are cleared from clouds triggers,
// runs the image difference algorithm, when all images are ready download the result to Google Drive.
var create_image_collector = function (min_images) {
return {
min_images : min_images,
list_pre : [],
list_post : [],
retrieve_done : function () {
return this.min_images <= this.list_pre.length && this.min_images <= this.list_post.length;
},
calculate_difference : function () {
var list_band_ratio_pre = [];
var list_band_ratio_post = [];
var image_difference = [];
var difference_ready = function () {
if (image_difference.length === min_images) {
print(image_difference);
for (var x = 0; x < image_difference.length; x++) {
export_to_drive(image_difference[x].image, image_difference[x].region, image_difference[x].description);
}
}
};
var run_difference = function () {
// Functions to Normalize NDVI and RGB
var subtract_band = function (image_pre_a, image_post_a, ndvi_change, brightness, region, l_id) {
var ratio_after = image_post_a.subtract(image_pre_a).add(0.50); //add 0.50 if ratio is no normalized
ratio_after = ratio_after.float().select('rg').addBands(ndvi_change.float().select('ndvi_change'))
.addBands(brightness.float().select('brightness_change'));
var description = 'diff_landslide';
var image_difference_download = create_download_image(l_id, ratio_after, region, description);
image_difference.push(image_difference_download);
difference_ready();
};
var calculate_mean = function (image, polygon) {
var buffer_processed = ee.Feature(polygon);
var stats_values = image.reduceRegion({
reducer : ee.Reducer.mean(), // calculate max
geometry : buffer_processed.geometry(),
scale : 10000,
maxPixels : 1e9,
});
var values = stats_values;
return values;
};
var calculate_ndvi = function (b4, b8) {
var nir = b8;
var red = b4;
return nir.subtract(red).divide(nir.add(red)).rename('NDVI');
};
var calculate_brightness = function (b2, b3, b4) {
var red = b4;
var green = b3;
var blue = b2;
return (((red.add(green).add(blue)).divide(3)).divide(1000)).rename('brightness');
};
var run_normalization = function (image_valData_after, image_valData_before, region, l_id) {
print(image_valData_before);
//1. calculate mean of all pixels per image
var mean_red_bands_after = calculate_mean(image_valData_after.select('B4', 'B3', 'B2', 'B8'), region).get('B4');
var mean_red_bands_before = calculate_mean(image_valData_before.select('B4', 'B3', 'B2', 'B8'), region).get('B4');
var mean_green_bands_after = calculate_mean(image_valData_after.select('B4', 'B3', 'B2', 'B8'), region).get('B3');
var mean_green_bands_before = calculate_mean(image_valData_before.select('B4', 'B3', 'B2', 'B8'), region).get('B3');
var mean_nir_bands_after = calculate_mean(image_valData_after.select('B4', 'B3', 'B2', 'B8'), region).get('B8');
var mean_nir_bands_before = calculate_mean(image_valData_before.select('B4', 'B3', 'B2', 'B8'), region).get('B8');
var mean_blue_bands_after = calculate_mean(image_valData_after.select('B4', 'B3', 'B2', 'B8'), region).get('B2');
var mean_blue_bands_before = calculate_mean(image_valData_before.select('B4', 'B3', 'B2', 'B8'), region).get('B2');
//2. calculate normalization factor
var normalisation_factor_red = ee.Number(mean_red_bands_after).divide(ee.Number(mean_red_bands_before));
var normalisation_factor_green = ee.Number(mean_green_bands_after).divide(ee.Number(mean_green_bands_before));
var normalisation_factor_blue = ee.Number(mean_blue_bands_after).divide(ee.Number(mean_blue_bands_before));
var normalisation_factor_nir = ee.Number(mean_nir_bands_after).divide(ee.Number(mean_nir_bands_before));
//3. Apply normalization factor to image pre-event
var image_valData_before_b2 = image_valData_before.select('B2').multiply(ee.Number(normalisation_factor_blue));
var image_valData_before_b3 = image_valData_before.select('B3').multiply(ee.Number(normalisation_factor_green));
var image_valData_before_b4 = image_valData_before.select('B4').multiply(ee.Number(normalisation_factor_red));
var image_valData_before_b8 = image_valData_before.select('B8').multiply(ee.Number(normalisation_factor_nir));
//4. Calculate NDVI Difference
var ndvi_image_post = calculate_ndvi(image_valData_after.select('B4'), image_valData_after.select('B8'));
var ndvi_image_pre = calculate_ndvi(image_valData_before_b4, image_valData_before_b8);
var ndvi_change = ndvi_image_pre.subtract(ndvi_image_post).rename('ndvi_change');
var brightness_image_post = calculate_brightness(image_valData_after.select('B2'), image_valData_after.select('B3'), image_valData_after.select('B4'));
var brightness_image_pre = calculate_brightness(image_valData_before_b2, image_valData_before_b3, image_valData_before_b4);
var brightness_change = brightness_image_pre.subtract(brightness_image_post).rename('brightness_change');
image_valData_before.evaluate(function (result) {
var band_ratio_image_pre = image_valData_before.select('B4').divide(image_valData_before.select('B3')).rename('rg'); //ration no normalized
var band_ratio_image_post = image_valData_after.select('B4').divide(image_valData_after.select('B3')).rename('rg');
subtract_band(band_ratio_image_pre, band_ratio_image_post, ndvi_change, brightness_change, region, l_id);
});
};
if (list_band_ratio_pre.length === min_images && list_band_ratio_post.length === min_images) {
for (var x = 0; x < list_band_ratio_post.length; x++) {
var image_band_ratio_post = list_band_ratio_post[x];
for (var y = 0; y < list_band_ratio_pre.length; y++) {
var image_band_ratio_pre = list_band_ratio_pre[y];
var l_id = image_band_ratio_pre.id;
if (image_band_ratio_post.id === l_id) {
var region = list_band_ratio_pre[y].region;
run_normalization(image_band_ratio_post.image, image_band_ratio_pre.image, region, l_id);
}
}
}
}
};
if (this.retrieve_done()) {
for (var i = 0; i < this.list_pre.length; i++) {
var description_pre = this.list_pre[i].description + 'band_ratio';
var image_pre = this.list_pre[i].image;
var region_pre = this.list_pre[i].region;
var band_ratio_image_pre_download = create_download_image(this.list_pre[i].id, image_pre, region_pre, description_pre);
list_band_ratio_pre.push(band_ratio_image_pre_download);
run_difference();
var description_post = this.list_post[i].description + 'band_ratio';
var image_post = this.list_post[i].image;
var region_post = this.list_post[i].region;
var band_ratio_image_post_download = create_download_image(this.list_post[i].id, image_post, region_post, description_post);
list_band_ratio_post.push(band_ratio_image_post_download);
run_difference();
}
}
}
};
};
var landslides = ee.FeatureCollection(fusion_table_id, 'latitude');
process_landslides_images(sentinel2, landslides, free_cloud_month_range, composite_image_search_range, download_prefix_description);