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landsat7_classification.js
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landsat7_classification.js
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/**
* Function to mask clouds based on the pixel_qa band of Landsat SR data.
* @param {ee.Image} image Input Landsat SR image
* @return {ee.Image} Cloudmasked Landsat image
*/
var cloudMaskL457 = function(image) {
var qa = image.select('pixel_qa');
// If the cloud bit (5) is set and the cloud confidence (7) is high
// or the cloud shadow bit is set (3), then it's a bad pixel.
var cloud = qa.bitwiseAnd(1 << 5)
.and(qa.bitwiseAnd(1 << 7))
.or(qa.bitwiseAnd(1 << 3));
// Remove edge pixels that don't occur in all bands
var mask2 = image.mask().reduce(ee.Reducer.min());
return image.updateMask(cloud.not()).updateMask(mask2);
};
//var year_list = ['2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010']
// var year_list = ['1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010',
// '2011','2012','2013','2014','2015','2016','2017','2018','2019','2020'];
var year_list = ['2015','2016','2017','2018','2019','2020']
var month_list = ['year_median','1half','2half'];
//var aoi_list =['Chandigarh','Hyderabad','Mumbai','Gurgaon','Delhi','Chennai','Bangalore','Kolkata'];
//var aoi_list =['Bangalore','Bangalore Rural','Panchkula','Delhi','Faridabad'];
var aoi_list = ['Kolkata']
var bands = ['B1','B2', 'B3', 'B4', 'B5', 'B6','B7'];
var india = ee.FeatureCollection('users/hariomahlawat/India_Boundary')
.geometry();
var india_image = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterBounds(india)
.filterDate('2019-01-01','2019-12-31')
.filter(ee.Filter.lt('CLOUD_COVER',1))
.map(cloudMaskL457)
.select(bands)
;
var india_image_training_median = india_image.median();
var india_image_training_min = india_image.min();
var india_image_training_max = india_image.max();
//Loading the training dataset and training the classifier
var ft = ee.FeatureCollection('users/hariomahlawat/IndiaSat');
function add_normalized_bands(image){
var ndvi = image.normalizedDifference(['B4', 'B3']).rename('NDVI'); //vegetaion index
var ndwi = image.normalizedDifference(['B2', 'B4']).rename('NDWI'); //water index
var ndbi = image.normalizedDifference(['B5', 'B4']).rename('NDBI'); //water index
return image.addBands(ndvi).addBands(ndwi).addBands(ndbi);
}
function add_all_bands(median_image, min_image, max_image){
return median_image.select('B1','B2','B3','B4','B5','B6','B7','NDVI','NDWI','NDBI')
.addBands(min_image.select('B1','B2','B3','NDVI','NDWI','NDBI'))
.addBands(max_image.select('B1','B2','B3','NDVI','NDWI','NDBI'));
}
india_image_training_median = add_normalized_bands(india_image_training_median)
india_image_training_min = add_normalized_bands(india_image_training_min)
india_image_training_max = add_normalized_bands(india_image_training_max)
var india_image_training = add_all_bands(india_image_training_median,
india_image_training_min,
india_image_training_max);
// Training the RF model.
var new_bands = ['B1','B2','B3','B4','B5','B6','B7','NDVI','NDWI','NDBI',
'B1_1','B2_1','B3_1','NDVI_1','NDWI_1',
'B1_2','B2_2','B3_2','NDVI_2','NDWI_2'
];
var training = india_image_training.sampleRegions(ft,['class'],30);
var trained = ee.Classifier.randomForest().train(training, 'class', new_bands);
//--------------Running the classifier for Area of Interest-----------------------------------------------
for (var i in aoi_list) {
var aoi_name = aoi_list[i];
for (var j in year_list)
{
for (var k in month_list)
{
var year = year_list[j];
var month = month_list[k]
var start_month = month;
var end_month = month;
var start_date = '01';
var end_date= '30';
if (month == '1half')
{
start_month = '01';
end_date = '30';
end_month = '06';
if (year == '1999')
{
start_month = '02';
}
else if (year == '2020')
{
end_month == '05'
}
}
else if (month == '2half')
{
start_month = '07';
end_date = '31';
end_month = '12';
}
else if (month == 'year_median')
{
start_month = '01';
end_date = '31';
end_month = '12';
if (year == '1999')
{
start_month = '02';
} else if (year == '2020')
{
end_month == '05'
}
}
else
{
start_month = month;
end_date = '30';
end_month = month;
}
var aoi = aoi_list[j];
var aoi = ee.FeatureCollection('users/hariomahlawat/india_district_boundaries')
.filter(ee.Filter.eq('Name',aoi_name));
var aoi_image_sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterBounds(aoi)
.filterDate(year + '-'+ start_month +'-'+ start_date, year + '-'+ end_month +'-'+ end_date)
.filter(ee.Filter.lt('CLOUD_COVER',10))
.map(cloudMaskL457)
.select(bands)
;
var aoi_image_median = aoi_image_sr.median();
var aoi_image_min = aoi_image_sr.min();
var aoi_image_mean = aoi_image_sr.mean();
var aoi_image_max = aoi_image_sr.max();
aoi_image_median = add_normalized_bands(aoi_image_median)
aoi_image_min = add_normalized_bands(aoi_image_min)
aoi_image_max = add_normalized_bands(aoi_image_max)
var aoi_image = add_all_bands(aoi_image_median,
aoi_image_min,
aoi_image_max)
print(aoi_name + ' - ' + year);
print(aoi_image);
var input = aoi_image;
input = input.clip(aoi);
input = input.classify(trained);
input = input.expression('LC',{'LC':input.select('classification')});
var str = aoi_name.replace(/\s/g,''); //remove spaces in the aoi name for naming the downloaded image
var misc = '_'+month
Export.image.toDrive({
image: input.clip(aoi),
description: 'landsat7_'+str + '_' + year+misc,
maxPixels: 1e9,
scale: 30,
folder: 'Landsat7_'+str,
region: aoi.geometry().bounds()
});
}
}
}