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390 lines
13 KiB
Python
390 lines
13 KiB
Python
7 years ago
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#==========================================================#
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#==========================================================#
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# Download L5, L7, L8, S2 images of a given area
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#==========================================================#
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#==========================================================#
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#==========================================================#
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# Initial settings
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#==========================================================#
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import pdb
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import ee
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# other modules
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from osgeo import gdal, ogr, osr
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from urllib.request import urlretrieve
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import zipfile
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from datetime import datetime
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import pytz
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import pickle
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# import own modules
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import functions.utils as utils
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import functions.sds as sds
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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#==========================================================#
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# Location
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#==========================================================#
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## location (Narrabeen-Collaroy beach)
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#polygon = [[[151.301454, -33.700754],
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# [151.311453, -33.702075],
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# [151.307237, -33.739761],
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# [151.294220, -33.736329],
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# [151.301454, -33.700754]]];
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# location (Tairua beach)
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sitename = 'TAIRUA'
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polygon = [[[175.835574, -36.982022],
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[175.888220, -36.980680],
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[175.893527, -37.029610],
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[175.833444, -37.031767],
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[175.835574, -36.982022]]];
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# initialise metadata dictionnary (stores timestamps and georefencing accuracy of each image)
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metadata = dict([])
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# create directories
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try:
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os.makedirs(os.path.join(os.getcwd(), 'data',sitename))
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except:
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print('directory already exists')
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#%%
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#==========================================================#
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#==========================================================#
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# L5
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#==========================================================#
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#==========================================================#
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# define filenames for images
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suffix = '.tif'
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filepath = os.path.join(os.getcwd(), 'data', sitename, 'L5', '30m')
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try:
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os.makedirs(filepath)
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except:
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print('directory already exists')
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#==========================================================#
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# Select L5 collection
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#==========================================================#
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satname = 'L5'
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input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
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n_img = flt_col.size().getInfo()
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print('Number of images covering ' + sitename, n_img)
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im_all = flt_col.getInfo().get('features')
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#==========================================================#
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# Main loop trough images
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#==========================================================#
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timestamps = []
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acc_georef = []
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all_names = []
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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timestamps.append(im_timestamp)
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im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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try:
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acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
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except:
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acc_georef.append(12)
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print('No geometric rmse model property')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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# bands for L5
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ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[7]]
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# filenames
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filename = im_date + '_' + satname + '_' + sitename + suffix
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print(i)
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if any(filename in _ for _ in all_names):
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filename = im_date + '_' + satname + '_' + sitename + '_dup' + suffix
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all_names.append(filename)
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local_data = sds.download_tif(im, polygon, ms_bands, filepath)
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os.rename(local_data, os.path.join(filepath, filename))
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# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
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timestamps_sorted = sorted(timestamps)
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idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
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acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
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metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
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#%%
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#==========================================================#
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#==========================================================#
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# L7&L8
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#==========================================================#
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#==========================================================#
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# define filenames for images
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suffix = '.tif'
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filepath = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8')
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filepath_pan = os.path.join(filepath, 'pan')
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filepath_ms = os.path.join(filepath, 'ms')
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try:
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os.makedirs(filepath_pan)
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os.makedirs(filepath_ms)
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except:
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print('directory already exists')
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#==========================================================#
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# Select L7 collection
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#==========================================================#
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satname = 'L7'
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input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA')
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
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n_img = flt_col.size().getInfo()
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print('Number of images covering ' + sitename, n_img)
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im_all = flt_col.getInfo().get('features')
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#==========================================================#
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# Main loop trough images
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#==========================================================#
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timestamps = []
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acc_georef = []
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all_names = []
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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timestamps.append(im_timestamp)
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im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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try:
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acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
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except:
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acc_georef.append(12)
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print('No geometric rmse model property')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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# bands for L7
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pan_band = [im_bands[8]]
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ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[9]]
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# filenames
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filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
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filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
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print(i)
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if any(filename_pan in _ for _ in all_names):
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filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
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filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
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all_names.append(filename_pan)
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local_data_pan = sds.download_tif(im, polygon, pan_band, filepath_pan)
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os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
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local_data_ms = sds.download_tif(im, polygon, ms_bands, filepath_ms)
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os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
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#==========================================================#
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# Select L8 collection
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#==========================================================#
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satname = 'L8'
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
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n_img = flt_col.size().getInfo()
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print('Number of images covering Narrabeen:', n_img)
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im_all = flt_col.getInfo().get('features')
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#==========================================================#
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# Main loop trough images
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#==========================================================#
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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timestamps.append(im_timestamp)
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im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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try:
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acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
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except:
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acc_georef.append(12)
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print('No geometric rmse model property')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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# bands for L8
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pan_band = [im_bands[7]]
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ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
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# filenames
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filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
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filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
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print(i)
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if any(filename_pan in _ for _ in all_names):
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filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
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filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
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all_names.append(filename_pan)
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local_data_pan = sds.download_tif(im, polygon, pan_band, filepath_pan)
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os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
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local_data_ms = sds.download_tif(im, polygon, ms_bands, filepath_ms)
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os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
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# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
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timestamps_sorted = sorted(timestamps)
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idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
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acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
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metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
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#%%
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#==========================================================#
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#==========================================================#
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# S2
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#==========================================================#
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#==========================================================#
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# define filenames for images
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suffix = '.tif'
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filepath = os.path.join(os.getcwd(), 'data', sitename, 'S2')
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try:
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os.makedirs(os.path.join(filepath, '10m'))
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os.makedirs(os.path.join(filepath, '20m'))
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os.makedirs(os.path.join(filepath, '60m'))
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except:
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print('directory already exists')
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#==========================================================#
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# Select L2 collection
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#==========================================================#
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satname = 'S2'
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input_col = ee.ImageCollection('COPERNICUS/S2')
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
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n_img = flt_col.size().getInfo()
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print('Number of images covering ' + sitename, n_img)
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im_all = flt_col.getInfo().get('features')
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#==========================================================#
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# Main loop trough images
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#==========================================================#
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timestamps = []
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acc_georef = []
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all_names = []
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
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timestamps.append(im_timestamp)
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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try:
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if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED':
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acc_georef.append(1)
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else:
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acc_georef.append(0)
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except:
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acc_georef.append(0)
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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# bands for S2
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bands10 = [im_bands[1], im_bands[2], im_bands[3], im_bands[7]]
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bands20 = [im_bands[11]]
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bands60 = [im_bands[15]]
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# filenames
|
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|
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filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + suffix
|
||
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filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + suffix
|
||
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filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + suffix
|
||
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|
||
|
|
print(i)
|
||
|
|
if any(filename10 in _ for _ in all_names):
|
||
|
|
filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + '_dup' + suffix
|
||
|
|
filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + '_dup' + suffix
|
||
|
|
filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + '_dup' + suffix
|
||
|
|
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))
|
||
|
|
|
||
|
|
# 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]
|
||
|
|
|
||
|
|
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#%% 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)
|
||
|
|
|
||
|
|
|