"""This module contains all the functions needed to download the satellite images from the Google Earth Engine Server Author: Kilian Vos, Water Research Laboratory, University of New South Wales """ # load modules import os import numpy as np import matplotlib.pyplot as plt import pdb # earth engine modules import ee from urllib.request import urlretrieve import zipfile import copy import gdal_merge # additional modules from datetime import datetime import pytz import pickle import skimage.morphology as morphology # own modules import SDS_preprocess, SDS_tools np.seterr(all='ignore') # raise/ignore divisions by 0 and nans # initialise connection with GEE server ee.Initialize() def download_tif(image, polygon, bandsId, filepath): """ Downloads a .TIF image from the ee server and stores it in a temp file Arguments: ----------- image: ee.Image Image object to be downloaded polygon: list polygon containing the lon/lat coordinates to be extracted longitudes in the first column and latitudes in the second column bandsId: list of dict list of bands to be downloaded filepath: location where the temporary file should be saved """ url = ee.data.makeDownloadUrl(ee.data.getDownloadId({ 'image': image.serialize(), 'region': polygon, 'bands': bandsId, 'filePerBand': 'false', 'name': 'data', })) local_zip, headers = urlretrieve(url) with zipfile.ZipFile(local_zip) as local_zipfile: return local_zipfile.extract('data.tif', filepath) def retrieve_images(inputs): """ Downloads all images from Landsat 5, Landsat 7, Landsat 8 and Sentinel-2 covering the area of interest and acquired between the specified dates. The downloaded images are in .TIF format and organised in subfolders, divided by satellite mission and pixel resolution. KV WRL 2018 Arguments: ----------- inputs: dict dictionnary that contains the following fields: 'sitename': str String containig the name of the site 'polygon': list polygon containing the lon/lat coordinates to be extracted longitudes in the first column and latitudes in the second column 'dates': list of str list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd' e.g. ['1987-01-01', '2018-01-01'] 'sat_list': list of str list that contains the names of the satellite missions to include e.g. ['L5', 'L7', 'L8', 'S2'] Returns: ----------- metadata: dict contains all the information about the satellite images that were downloaded """ # read inputs dictionnary sitename = inputs['sitename'] polygon = inputs['polygon'] dates = inputs['dates'] sat_list= inputs['sat_list'] # format in which the images are downloaded suffix = '.tif' # initialize 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('') #=============================================================================================# # download L5 images #=============================================================================================# if 'L5' in sat_list or 'Landsat5' in sat_list: satname = 'L5' # create a subfolder to store L5 images filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m') try: os.makedirs(filepath) except: print('') # Landsat 5 collection input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA') # filter by location and dates flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1]) # get all images in the filtered collection im_all = flt_col.getInfo().get('features') # remove very cloudy images (>95% cloud) cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] if np.any([_ > 95 for _ in cloud_cover]): idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] im_all_cloud = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_all_cloud = im_all n_img = len(im_all_cloud) # print how many images there are print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) # loop trough images timestamps = [] acc_georef = [] filenames = [] all_names = [] im_epsg = [] for i in range(n_img): # find each image in ee database im = ee.Image(im_all_cloud[i].get('id')) # read metadata im_dic = im.getInfo() # get bands im_bands = im_dic.get('bands') # get time of acquisition (UNIX time) t = im_dic['properties']['system:time_start'] # convert to datetime im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc) timestamps.append(im_timestamp) im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S') # get EPSG code of reference system im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) # get geometric accuracy try: acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) except: # default value of accuracy (RMSE = 12m) acc_georef.append(12) # 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 L5 ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[7]] # filenames for the images filename = im_date + '_' + satname + '_' + sitename + suffix # if two images taken at the same date add 'dup' in the name (duplicate) if any(filename in _ for _ in all_names): filename = im_date + '_' + satname + '_' + sitename + '_dup' + suffix all_names.append(filename) filenames.append(filename) # download .TIF image local_data = download_tif(im, polygon, ms_bands, filepath) # update filename try: os.rename(local_data, os.path.join(filepath, filename)) except: os.remove(os.path.join(filepath, filename)) os.rename(local_data, os.path.join(filepath, filename)) print(i+1, end='..') # sort timestamps and georef accuracy (downloaded 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] filenames_sorted = [filenames[j] for j in idx_sorted] im_epsg_sorted = [im_epsg[j] for j in idx_sorted] # save into dict metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg_sorted, 'filenames':filenames_sorted} print('\nFinished with ' + satname) #=============================================================================================# # download L7 images #=============================================================================================# if 'L7' in sat_list or 'Landsat7' in sat_list: satname = 'L7' # create subfolders (one for 30m multispectral bands and one for 15m pan bands) filepath = os.path.join(os.getcwd(), 'data', sitename, 'L7') filepath_pan = os.path.join(filepath, 'pan') filepath_ms = os.path.join(filepath, 'ms') try: os.makedirs(filepath_pan) os.makedirs(filepath_ms) except: print('') # landsat 7 collection input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA') # filter by location and dates flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1]) # get all images in the filtered collection im_all = flt_col.getInfo().get('features') # remove very cloudy images (>95% cloud) cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] if np.any([_ > 95 for _ in cloud_cover]): idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] im_all_cloud = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_all_cloud = im_all n_img = len(im_all_cloud) # print how many images there are print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) # loop trough images timestamps = [] acc_georef = [] filenames = [] all_names = [] im_epsg = [] for i in range(n_img): # find each image in ee database im = ee.Image(im_all_cloud[i].get('id')) # read metadata im_dic = im.getInfo() # get bands im_bands = im_dic.get('bands') # get time of acquisition (UNIX time) t = im_dic['properties']['system:time_start'] # convert to datetime im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc) timestamps.append(im_timestamp) im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S') # get EPSG code of reference system im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) # get geometric accuracy try: acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) except: # default value of accuracy (RMSE = 12m) acc_georef.append(12) # 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 L7 pan_band = [im_bands[8]] ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[9]] # filenames for the images filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix # if two images taken at the same date add 'dup' in the name if any(filename_pan in _ for _ in all_names): filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix all_names.append(filename_pan) filenames.append(filename_pan) # download .TIF image local_data_pan = download_tif(im, polygon, pan_band, filepath_pan) local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms) # update filename try: os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) except: os.remove(os.path.join(filepath_pan, filename_pan)) os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) try: os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) except: os.remove(os.path.join(filepath_ms, filename_ms)) os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) print(i+1, end='..') # 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] filenames_sorted = [filenames[j] for j in idx_sorted] im_epsg_sorted = [im_epsg[j] for j in idx_sorted] # save into dict metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg_sorted, 'filenames':filenames_sorted} print('\nFinished with ' + satname) #=============================================================================================# # download L8 images #=============================================================================================# if 'L8' in sat_list or 'Landsat8' in sat_list: satname = 'L8' # create subfolders (one for 30m multispectral bands and one for 15m pan bands) filepath = os.path.join(os.getcwd(), 'data', sitename, 'L8') filepath_pan = os.path.join(filepath, 'pan') filepath_ms = os.path.join(filepath, 'ms') try: os.makedirs(filepath_pan) os.makedirs(filepath_ms) except: print('') # landsat 8 collection input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # filter by location and dates flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1]) # get all images in the filtered collection im_all = flt_col.getInfo().get('features') # remove very cloudy images (>95% cloud) cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] if np.any([_ > 95 for _ in cloud_cover]): idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] im_all_cloud = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_all_cloud = im_all n_img = len(im_all_cloud) # print how many images there are print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) # loop trough images timestamps = [] acc_georef = [] filenames = [] all_names = [] im_epsg = [] for i in range(n_img): # find each image in ee database im = ee.Image(im_all_cloud[i].get('id')) # read metadata im_dic = im.getInfo() # get bands im_bands = im_dic.get('bands') # get time of acquisition (UNIX time) t = im_dic['properties']['system:time_start'] # convert to datetime im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc) timestamps.append(im_timestamp) im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S') # get EPSG code of reference system im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) # get geometric accuracy try: acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) except: # default value of accuracy (RMSE = 12m) acc_georef.append(12) # 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 L8 pan_band = [im_bands[7]] ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]] # filenames for the images filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix # if two images taken at the same date add 'dup' in the name if any(filename_pan in _ for _ in all_names): filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix all_names.append(filename_pan) filenames.append(filename_pan) # download .TIF image local_data_pan = download_tif(im, polygon, pan_band, filepath_pan) local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms) # update filename try: os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) except: os.remove(os.path.join(filepath_pan, filename_pan)) os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) try: os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) except: os.remove(os.path.join(filepath_ms, filename_ms)) os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) print(i+1, end='..') # 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] filenames_sorted = [filenames[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, 'filenames':filenames_sorted} print('\nFinished with ' + satname) #=============================================================================================# # download S2 images #=============================================================================================# if 'S2' in sat_list or 'Sentinel2' in sat_list: satname = 'S2' # create subfolders for the 10m, 20m and 60m multipectral bands 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('') # Sentinel2 collection input_col = ee.ImageCollection('COPERNICUS/S2') # filter by location and dates flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1]) # get all images in the filtered collection im_all = flt_col.getInfo().get('features') # remove duplicates in the collection (there are many in S2 collection) timestamps = [datetime.fromtimestamp(_['properties']['system:time_start']/1000, tz=pytz.utc) for _ in im_all] # utm zone projection utm_zones = np.array([int(_['bands'][0]['crs'][5:]) for _ in im_all]) utm_zone_selected = np.max(np.unique(utm_zones)) # find the images that were acquired at the same time but have different utm zones idx_all = np.arange(0,len(im_all),1) idx_covered = np.ones(len(im_all)).astype(bool) idx_delete = [] i = 0 while 1: same_time = np.abs([(timestamps[i]-_).total_seconds() for _ in timestamps]) < 60*60*24 idx_same_time = np.where(same_time)[0] same_utm = utm_zones == utm_zone_selected idx_temp = np.where([same_time[j] == True and same_utm[j] == False for j in idx_all])[0] idx_keep = idx_same_time[[_ not in idx_temp for _ in idx_same_time ]] # if more than 2 images with same date and same utm, drop the last ones if len(idx_keep) > 2: idx_temp = np.append(idx_temp,idx_keep[-(len(idx_keep)-2):]) for j in idx_temp: idx_delete.append(j) idx_covered[idx_same_time] = False if np.any(idx_covered): i = np.where(idx_covered)[0][0] else: break # update the collection by deleting all those images that have same timestamp and different # utm projection im_all_updated = [x for k,x in enumerate(im_all) if k not in idx_delete] # remove very cloudy images (>95% cloud) cloud_cover = [_['properties']['CLOUDY_PIXEL_PERCENTAGE'] for _ in im_all_updated] if np.any([_ > 95 for _ in cloud_cover]): idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] im_all_cloud = [x for k,x in enumerate(im_all_updated) if k not in idx_delete] else: im_all_cloud = im_all_updated n_img = len(im_all_cloud) # print how many images there are print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) # loop trough images timestamps = [] acc_georef = [] filenames = [] all_names = [] im_epsg = [] for i in range(n_img): # find each image in ee database im = ee.Image(im_all_cloud[i].get('id')) # read metadata im_dic = im.getInfo() # get bands im_bands = im_dic.get('bands') # get time of acquisition (UNIX time) t = im_dic['properties']['system:time_start'] # convert to datetime 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 for images filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + suffix filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + suffix filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + suffix # if two images taken at the same date skip the second image (they are the same) if any(filename10 in _ for _ in all_names): filename10 = filename10[:filename10.find('.')] + '_dup' + suffix filename20 = filename20[:filename20.find('.')] + '_dup' + suffix filename60 = filename60[:filename60.find('.')] + '_dup' + suffix all_names.append(filename10) filenames.append(filename10) # download .TIF image and update filename local_data = download_tif(im, polygon, bands10, os.path.join(filepath, '10m')) try: os.rename(local_data, os.path.join(filepath, '10m', filename10)) except: os.remove(os.path.join(filepath, '10m', filename10)) os.rename(local_data, os.path.join(filepath, '10m', filename10)) local_data = download_tif(im, polygon, bands20, os.path.join(filepath, '20m')) try: os.rename(local_data, os.path.join(filepath, '20m', filename20)) except: os.remove(os.path.join(filepath, '20m', filename20)) os.rename(local_data, os.path.join(filepath, '20m', filename20)) local_data = download_tif(im, polygon, bands60, os.path.join(filepath, '60m')) try: os.rename(local_data, os.path.join(filepath, '60m', filename60)) except: os.remove(os.path.join(filepath, '60m', filename60)) os.rename(local_data, os.path.join(filepath, '60m', filename60)) # save timestamp, epsg code and georeferencing accuracy (1 if passed 0 if not passed) timestamps.append(im_timestamp) im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) # Sentinel-2 products don't provide a georeferencing accuracy (RMSE as in Landsat) # but they have a flag indicating if the geometric quality control was passed or failed # if passed a value of 1 is stored if failed a value of -1 is stored in the metadata if 'GEOMETRIC_QUALITY_FLAG' in im_dic['properties'].keys(): if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED': acc_georef.append(1) else: acc_georef.append(-1) elif 'quality_check' in im_dic['properties'].keys(): if im_dic['properties']['quality_check'] == 'PASSED': acc_georef.append(1) else: acc_georef.append(-1) else: acc_georef.append(-1) print(i+1, end='..') # 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] filenames_sorted = [filenames[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, 'filenames':filenames_sorted} print('\nFinished with ' + satname) # merge overlapping images (necessary only if the polygon is at the boundary of an image) if 'S2' in metadata.keys(): metadata = merge_overlapping_images(metadata,inputs) # save metadata dict filepath = os.path.join(os.getcwd(), 'data', sitename) with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'wb') as f: pickle.dump(metadata, f) return metadata def merge_overlapping_images(metadata,inputs): """ When the area of interest is located at the boundary between 2 images, there will be overlap between the 2 images and both will be downloaded from Google Earth Engine. This function merges the 2 images, so that the area of interest is covered by only 1 image. KV WRL 2018 Arguments: ----------- metadata: dict contains all the information about the satellite images that were downloaded inputs: dict dictionnary that contains the following fields: 'sitename': str String containig the name of the site 'polygon': list polygon containing the lon/lat coordinates to be extracted longitudes in the first column and latitudes in the second column 'dates': list of str list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd' e.g. ['1987-01-01', '2018-01-01'] 'sat_list': list of str list that contains the names of the satellite missions to include e.g. ['L5', 'L7', 'L8', 'S2'] Returns: ----------- metadata: dict updated metadata with the information of the merged images """ # only for Sentinel-2 at this stage (could be implemented for Landsat as well) sat = 'S2' filepath = os.path.join(os.getcwd(), 'data', inputs['sitename']) # find the images that are overlapping (same date in S2 filenames) filenames = metadata[sat]['filenames'] filenames_copy = filenames.copy() # loop through all the filenames and find the pairs of overlapping images (same date and time of acquisition) pairs = [] for i,fn in enumerate(filenames): filenames_copy[i] = [] # find duplicate boolvec = [fn[:22] == _[:22] for _ in filenames_copy] if np.any(boolvec): idx_dup = np.where(boolvec)[0][0] if len(filenames[i]) > len(filenames[idx_dup]): pairs.append([idx_dup,i]) else: pairs.append([i,idx_dup]) msg = 'Merging %d pairs of overlapping images...' % len(pairs) print(msg) # for each pair of images, merge them into one complete image for i,pair in enumerate(pairs): print(i+1, end='..') fn_im = [] for index in range(len(pair)): # read image fn_im.append([os.path.join(filepath, 'S2', '10m', filenames[pair[index]]), os.path.join(filepath, 'S2', '20m', filenames[pair[index]].replace('10m','20m')), os.path.join(filepath, 'S2', '60m', filenames[pair[index]].replace('10m','60m'))]) im_ms, georef, cloud_mask, im_extra, imQA = SDS_preprocess.preprocess_single(fn_im[index], sat, False) # in Sentinel2 images close to the edge of the image there are some artefacts, # that are squares with constant pixel intensities. They need to be masked in the # raster (GEOTIFF). It can be done using the image standard deviation, which # indicates values close to 0 for the artefacts. # First mask the 10m bands if len(im_ms) > 0: im_std = SDS_tools.image_std(im_ms[:,:,0],1) im_binary = np.logical_or(im_std < 1e-6, np.isnan(im_std)) mask = morphology.dilation(im_binary, morphology.square(3)) for k in range(im_ms.shape[2]): im_ms[mask,k] = np.nan SDS_tools.mask_raster(fn_im[index][0], mask) # Then mask the 20m band im_std = SDS_tools.image_std(im_extra,1) im_binary = np.logical_or(im_std < 1e-6, np.isnan(im_std)) mask = morphology.dilation(im_binary, morphology.square(3)) im_extra[mask] = np.nan SDS_tools.mask_raster(fn_im[index][1], mask) else: continue # make a figure for quality control # plt.figure() # plt.subplot(221) # plt.imshow(im_ms[:,:,[2,1,0]]) # plt.title('imRGB') # plt.subplot(222) # plt.imshow(im20, cmap='gray') # plt.title('im20') # plt.subplot(223) # plt.imshow(imQA, cmap='gray') # plt.title('imQA') # plt.subplot(224) # plt.title(fn_im[index][0][-30:]) # merge masked 10m bands fn_merged = os.path.join(os.getcwd(), 'merged.tif') gdal_merge.main(['', '-o', fn_merged, '-n', '0', fn_im[0][0], fn_im[1][0]]) os.chmod(fn_im[0][0], 0o777) os.remove(fn_im[0][0]) os.chmod(fn_im[1][0], 0o777) os.remove(fn_im[1][0]) os.rename(fn_merged, fn_im[0][0]) # merge masked 20m band (SWIR band) fn_merged = os.path.join(os.getcwd(), 'merged.tif') gdal_merge.main(['', '-o', fn_merged, '-n', '0', fn_im[0][1], fn_im[1][1]]) os.chmod(fn_im[0][1], 0o777) os.remove(fn_im[0][1]) os.chmod(fn_im[1][1], 0o777) os.remove(fn_im[1][1]) os.rename(fn_merged, fn_im[0][1]) # merge QA band (60m band) fn_merged = os.path.join(os.getcwd(), 'merged.tif') gdal_merge.main(['', '-o', fn_merged, '-n', 'nan', fn_im[0][2], fn_im[1][2]]) os.chmod(fn_im[0][2], 0o777) os.remove(fn_im[0][2]) os.chmod(fn_im[1][2], 0o777) os.remove(fn_im[1][2]) os.rename(fn_merged, fn_im[0][2]) # update the metadata dict (delete all the duplicates) metadata2 = copy.deepcopy(metadata) filenames_copy = metadata2[sat]['filenames'] index_list = [] for i in range(len(filenames_copy)): if filenames_copy[i].find('dup') == -1: index_list.append(i) for key in metadata2[sat].keys(): metadata2[sat][key] = [metadata2[sat][key][_] for _ in index_list] return metadata2 def remove_cloudy_images(metadata,inputs,cloud_thresh): """ Deletes the .TIF file of images that have a cloud cover percentage that is above the cloud threshold. KV WRL 2018 Arguments: ----------- metadata: dict contains all the information about the satellite images that were downloaded inputs: dict dictionnary that contains the following fields: 'sitename': str String containig the name of the site 'polygon': list polygon containing the lon/lat coordinates to be extracted longitudes in the first column and latitudes in the second column 'dates': list of str list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd' e.g. ['1987-01-01', '2018-01-01'] 'sat_list': list of str list that contains the names of the satellite missions to include e.g. ['L5', 'L7', 'L8', 'S2'] cloud_thresh: float value between 0 and 1 indicating the maximum cloud fraction in the image that is accepted Returns: ----------- metadata: dict updated metadata with the information of the merged images """ # create a deep copy metadata2 = copy.deepcopy(metadata) for satname in metadata.keys(): # get the image filenames filepath = SDS_tools.get_filepath(inputs,satname) filenames = metadata[satname]['filenames'] # loop through images idx_good = [] for i in range(len(filenames)): # image filename fn = SDS_tools.get_filenames(filenames[i],filepath, satname) # preprocess image (cloud mask + pansharpening/downsampling) im_ms, georef, cloud_mask, im_extra, imQA = SDS_preprocess.preprocess_single(fn, satname, False) # calculate cloud cover cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))), (cloud_mask.shape[0]*cloud_mask.shape[1])) # skip image if cloud cover is above threshold if cloud_cover > cloud_thresh or cloud_cover == 1: # remove image files if satname == 'L5': os.chmod(fn, 0o777) os.remove(fn) else: for j in range(len(fn)): os.chmod(fn[j], 0o777) os.remove(fn[j]) else: idx_good.append(i) msg = '\n%d cloudy images were removed for %s.' % (len(filenames)-len(idx_good), satname) print(msg) # update the metadata dict (delete all cloudy images) for key in metadata2[satname].keys(): metadata2[satname][key] = [metadata2[satname][key][_] for _ in idx_good] return metadata2