Estoy tratando de enmascarar una colección de landsat 7 y 8 utilizando el QA_imagery a través de la API python del motor de google earth pero no elimina los píxeles nublados. He probado en la página web usando javascript y funciona bien
// Import shape
var table = ee.FeatureCollection("users/italomoletto/LSat_ETr_Tiles");
// Load Landsat 8 surface reflectance data
var l8sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(ee.Filter.eq('WRS_PATH', 233))
.filter(ee.Filter.gt('WRS_ROW', 87)).filter(ee.Filter.lt('WRS_ROW', 91));
// Load Landsat 7 surface reflectance data
var l7sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(ee.Filter.eq('WRS_PATH', 233))
.filter(ee.Filter.gt('WRS_ROW', 87)).filter(ee.Filter.lt('WRS_ROW', 91));
// helper function to extract the QA bits
function getQABits(image, start, end, mascara) {
// Compute the bits we need to extract.
var pattern = 0;
for (var i = start; i <= end; i++) {
pattern += Math.pow(2, i);
}
// Return a single band image of the extracted QA bits, giving the band a new name.
return image.select([0], [mascara])
.bitwiseAnd(pattern)
.rightShift(start);
}
// A function to mask out cloudy pixels.
var maskQuality = function(image) {
// Select the QA band.
var QA = image.select('pixel_qa');
// Get the internal_cloud_algorithm_flag bit.
var sombra = getQABits(QA,3,3, 'cloud_shadow');
var nubes = getQABits(QA,5,5, 'cloud');
//var cloud_confidence = getQABits(QA,6,7, 'cloud_confidence');
var cirrus_detected = getQABits(QA,9,9, 'cirrus_detected');
//var cirrus_detected2 = getQABits(QA,8,8, 'cirrus_detected2')
// Return an image masking out cloudy areas.
return image.updateMask(sombra.eq(0)).updateMask(nubes.eq(0).updateMask(cirrus_detected.eq(0))
);
}
// Apply_cloud mask
var L8SR = l8sr.filterDate('2017-01-01', '2017-12-31')
.map(maskQuality).filterBounds(table)
var L7SR = l7sr.filterDate('2017-01-01', '2017-12-31')
.map(maskQuality).filterBounds(table)
//Export_imagery
var ExportCol = function(col, folder, scale, type,
nimg, maxPixels, region) {
type = type || "float";
nimg = nimg || 500;
scale = scale || 1000;
maxPixels = maxPixels || 1e10;
var colList = col.toList(nimg);
var n = colList.size().getInfo();
for (var i = 0; i < n; i++) {
var img = ee.Image(colList.get(i));
var id = img.id().getInfo();
region = region || img.geometry().bounds().getInfo()["coordinates"];
var imgtype = {"float":img.toFloat(),
"byte":img.toByte(),
"int":img.toInt(),
"double":img.toDouble()
}
Export.image.toDrive({
image:imgtype[type],
description: id,
folder: folder,
fileNamePrefix: id,
region: region,
scale: scale,
maxPixels: maxPixels})
}
}
//var ExportCol = function(col, folder, scale, type,
// nimg, maxPixels, region)
ExportCol(L8SR,'L8SR',100,"float",200,1e12,table)Este código funciona bien. Pero cuando lo aplico en la API de python la máscara de la nube no funciona
import ee
from ee import batch
ee.Initialize()
# Import shapefile
table = ee.FeatureCollection("users/italomoletto/LSat_ETr_Tiles")
#Load Landsat 8 surface reflectance data
l8sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(ee.Filter.eq('WRS_PATH', 233))\
.filter(ee.Filter.gt('WRS_ROW', 87)).filter(ee.Filter.lt('WRS_ROW', 91))
#Load Landsat 7 surface reflectance data
l7sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(ee.Filter.eq('WRS_PATH', 233))\
.filter(ee.Filter.gt('WRS_ROW', 87)).filter(ee.Filter.lt('WRS_ROW', 91))
def getQABits(image, start, end, mascara):
# Compute the bits we need to extract.
pattern = 0
for i in range(start,end-1):
pattern += 2**i
# Return a single band image of the extracted QA bits, giving the band a new name.
return image.select([0], [mascara]).bitwiseAnd(pattern).rightShift(start)
#A function to mask out cloudy pixels.
def maskQuality(image):
# Select the QA band.
QA = image.select('pixel_qa')
# Get the internal_cloud_algorithm_flag bit.
sombra = getQABits(QA,3,3,'cloud_shadow')
nubes = getQABits(QA,5,5,'cloud')
# var cloud_confidence = getQABits(QA,6,7, 'cloud_confidence')
cirrus_detected = getQABits(QA,9,9,'cirrus_detected')
#var cirrus_detected2 = getQABits(QA,8,8, 'cirrus_detected2')
#Return an image masking out cloudy areas.
return image.updateMask(sombra.eq(0)).updateMask(nubes.eq(0).updateMask(cirrus_detected.eq(0)))
L8SR = l8sr.filterDate('2017-01-01', '2017-12-31')\
.map(maskQuality).filterBounds(table)
L7SR = l7sr.filterDate('2017-01-01', '2017-12-31')\
.map(maskQuality).filterBounds(table)
llx = -71
lly = -42
urx = -73
ury = -41
geometry = [[llx,lly], [llx,ury], [urx,ury], [urx,lly]]
name_export='SR'
def ExportCol(col, folder, scale, typei, nimg, maxPixels, region):
#typei = "float"
#nimg = 500
#scale = 1000
#maxPixels = 1e10
colList = col.toList(nimg)
n = colList.size().getInfo()
for i in range(0,n):
img = ee.Image(colList.get(i))
id_img = img.id().getInfo()
#region = img.geometry().bounds().getInfo()["coordinates"]
imgtype = {"float":img.toFloat(),
"byte":img.toByte(),
"int":img.toInt(),
"double":img.toDouble()
}
task_config = {
'image': imgtype[typei],
'description': id_img,
'scale': scale,
'region': region,
'folder': folder,
'maxPixels': maxPixels,
'fileNamePrefix': id_img
}
task = batch.Export.image(img, name_export+id_img, task_config)
task.start()
#//var ExportCol = function(col, folder, scale, type,
#// nimg, maxPixels, region)
ExportCol(L8SR,'SR',100,"float",200,1e12,geometry)Este código exporta las imágenes pero no funciona la máscara de la nube, ¿algún consejo?
