You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
160 lines
4.9 KiB
Python
160 lines
4.9 KiB
Python
#==========================================================#
|
|
#==========================================================#
|
|
# Download L5, L7, L8, S2 images of a given area
|
|
#==========================================================#
|
|
#==========================================================#
|
|
|
|
|
|
|
|
#==========================================================#
|
|
# Initial settings
|
|
#==========================================================#
|
|
import os
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import pdb
|
|
import ee
|
|
|
|
# other modules
|
|
from osgeo import gdal, ogr, osr
|
|
from urllib.request import urlretrieve
|
|
import zipfile
|
|
from datetime import datetime
|
|
import pytz
|
|
import pickle
|
|
|
|
# import own modules
|
|
import functions.utils as utils
|
|
import functions.sds as sds
|
|
|
|
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
|
ee.Initialize()
|
|
|
|
#==========================================================#
|
|
# Location
|
|
#==========================================================#
|
|
|
|
## location (Narrabeen-Collaroy beach)
|
|
sitename = 'NARRA'
|
|
polygon = [[[151.301454, -33.700754],
|
|
[151.311453, -33.702075],
|
|
[151.307237, -33.739761],
|
|
[151.294220, -33.736329],
|
|
[151.301454, -33.700754]]];
|
|
|
|
|
|
# initialise metadata dictionnary (stores timestamps and georefencing accuracy of each image)
|
|
metadata = dict([])
|
|
|
|
# create directories
|
|
try:
|
|
os.makedirs(os.path.join(os.getcwd(), 'data',sitename))
|
|
except:
|
|
print('directory already exists')
|
|
|
|
#%%
|
|
#==========================================================#
|
|
#==========================================================#
|
|
# S2
|
|
#==========================================================#
|
|
#==========================================================#
|
|
|
|
|
|
|
|
# define filenames for images
|
|
suffix = '.tif'
|
|
filepath = os.path.join(os.getcwd(), 'data', sitename, 'S2')
|
|
try:
|
|
os.makedirs(os.path.join(filepath, '10m'))
|
|
os.makedirs(os.path.join(filepath, '20m'))
|
|
os.makedirs(os.path.join(filepath, '60m'))
|
|
except:
|
|
print('directory already exists')
|
|
|
|
#==========================================================#
|
|
# Select L2 collection
|
|
#==========================================================#
|
|
|
|
satname = 'S2'
|
|
input_col = ee.ImageCollection('COPERNICUS/S2')
|
|
|
|
# filter by location
|
|
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
|
|
n_img = flt_col.size().getInfo()
|
|
print('Number of images covering ' + sitename, n_img)
|
|
im_all = flt_col.getInfo().get('features')
|
|
|
|
#==========================================================#
|
|
# Main loop trough images
|
|
#==========================================================#
|
|
|
|
timestamps = []
|
|
acc_georef = []
|
|
all_names = []
|
|
im_epsg = []
|
|
for i in range(n_img):
|
|
|
|
# find each image in ee database
|
|
im = ee.Image(im_all[i].get('id'))
|
|
|
|
im_dic = im.getInfo()
|
|
im_bands = im_dic.get('bands')
|
|
t = im_dic['properties']['system:time_start']
|
|
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
|
|
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
|
|
|
|
# delete dimensions key from dictionnary, otherwise the entire image is extracted
|
|
for j in range(len(im_bands)): del im_bands[j]['dimensions']
|
|
|
|
# bands for S2
|
|
bands10 = [im_bands[1], im_bands[2], im_bands[3], im_bands[7]]
|
|
bands20 = [im_bands[11]]
|
|
bands60 = [im_bands[15]]
|
|
|
|
# filenames
|
|
filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + suffix
|
|
filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + suffix
|
|
filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + suffix
|
|
|
|
print(i)
|
|
if any(filename10 in _ for _ in all_names):
|
|
continue
|
|
all_names.append(filename10)
|
|
|
|
local_data = sds.download_tif(im, polygon, bands10, filepath)
|
|
os.rename(local_data, os.path.join(filepath, '10m', filename10))
|
|
|
|
local_data = sds.download_tif(im, polygon, bands20, filepath)
|
|
os.rename(local_data, os.path.join(filepath, '20m', filename20))
|
|
|
|
local_data = sds.download_tif(im, polygon, bands60, filepath)
|
|
os.rename(local_data, os.path.join(filepath, '60m', filename60))
|
|
|
|
timestamps.append(im_timestamp)
|
|
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
|
|
try:
|
|
if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED':
|
|
acc_georef.append(1)
|
|
else:
|
|
acc_georef.append(0)
|
|
except:
|
|
acc_georef.append(0)
|
|
|
|
|
|
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
|
|
timestamps_sorted = sorted(timestamps)
|
|
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
|
|
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
|
|
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
|
|
|
|
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg_sorted}
|
|
|
|
|
|
|
|
#%% save metadata
|
|
|
|
filepath = os.path.join(os.getcwd(), 'data', sitename)
|
|
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'wb') as f:
|
|
pickle.dump(metadata, f)
|
|
|
|
|