"""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 from coastsat import gdal_merge # additional modules from datetime import datetime import pytz import pickle import skimage.morphology as morphology # own modules from coastsat 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, there are 5 pairs of lat/lon with the fifth point equal to the first point. e.g. [[[151.3, -33.7],[151.4, -33.7],[151.4, -33.8],[151.3, -33.8], [151.3, -33.7]]] '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'] 'filepath_data': str Filepath to the directory where the images are downloaded Returns: ----------- metadata: dict contains the information about the satellite images that were downloaded: filename, georeferencing accuracy and image coordinate reference system """ # read inputs dictionnary sitename = inputs['sitename'] polygon = inputs['polygon'] dates = inputs['dates'] sat_list= inputs['sat_list'] filepath_data = inputs['filepath'] # format in which the images are downloaded suffix = '.tif' # initialize metadata dictionnary (stores information about each image) metadata = dict([]) # create a new directory for this site if not os.path.exists(os.path.join(filepath_data,sitename)): os.makedirs(os.path.join(filepath_data,sitename)) print('Downloading images:') #=============================================================================================# # 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(filepath_data, sitename, satname, '30m') filepath_meta = os.path.join(filepath_data, sitename, satname, 'meta') if not os.path.exists(filepath): os.makedirs(filepath) if not os.path.exists(filepath_meta): os.makedirs(filepath_meta) # 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_col = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_col = im_all n_img = len(im_col) # print how many images there are print('%s: %d images'%(satname,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_col[i]['id']) # read metadata im_dic = im_col[i] # get bands im_bands = im_dic['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 if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys(): acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) else: acc_georef.append(12) # default value of accuracy (RMSE = 12m) # 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)) # write metadata in .txt file filename_txt = filename.replace('.tif','') metadict = {'filename':filename,'acc_georef':acc_georef[i], 'epsg':im_epsg[i]} with open(os.path.join(filepath_meta,filename_txt + '.txt'), 'w') as f: for key in metadict.keys(): f.write('%s\t%s\n'%(key,metadict[key])) print('\r%d%%' % (int(((i+1)/n_img)*100)), end='') print('') # sort metadata (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} #=============================================================================================# # 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(filepath_data, sitename, 'L7') filepath_pan = os.path.join(filepath, 'pan') filepath_ms = os.path.join(filepath, 'ms') filepath_meta = os.path.join(filepath, 'meta') if not os.path.exists(filepath_pan): os.makedirs(filepath_pan) if not os.path.exists(filepath_ms): os.makedirs(filepath_ms) if not os.path.exists(filepath_meta): os.makedirs(filepath_meta) # 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_col = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_col = im_all n_img = len(im_col) # print how many images there are print('%s: %d images'%(satname,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_col[i]['id']) # read metadata im_dic = im_col[i] # get bands im_bands = im_dic['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 if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys(): acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) else: acc_georef.append(12) # default value of accuracy (RMSE = 12m) # 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)) # write metadata in .txt file filename_txt = filename_pan.replace('_pan','').replace('.tif','') metadict = {'filename':filename_pan,'acc_georef':acc_georef[i], 'epsg':im_epsg[i]} with open(os.path.join(filepath_meta,filename_txt + '.txt'), 'w') as f: for key in metadict.keys(): f.write('%s\t%s\n'%(key,metadict[key])) print('\r%d%%' % (int(((i+1)/n_img)*100)), end='') print('') # sort metadata (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} #=============================================================================================# # 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(filepath_data, sitename, 'L8') filepath_pan = os.path.join(filepath, 'pan') filepath_ms = os.path.join(filepath, 'ms') filepath_meta = os.path.join(filepath, 'meta') if not os.path.exists(filepath_pan): os.makedirs(filepath_pan) if not os.path.exists(filepath_ms): os.makedirs(filepath_ms) if not os.path.exists(filepath_meta): os.makedirs(filepath_meta) # 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_col = [x for k,x in enumerate(im_all) if k not in idx_delete] else: im_col = im_all n_img = len(im_col) # print how many images there are print('%s: %d images'%(satname,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_col[i]['id']) # read metadata im_dic = im_col[i] # get bands im_bands = im_dic['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 if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys(): acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) else: acc_georef.append(12) # default value of accuracy (RMSE = 12m) # 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)) # write metadata in .txt file filename_txt = filename_pan.replace('_pan','').replace('.tif','') metadict = {'filename':filename_pan,'acc_georef':acc_georef[i], 'epsg':im_epsg[i]} with open(os.path.join(filepath_meta,filename_txt + '.txt'), 'w') as f: for key in metadict.keys(): f.write('%s\t%s\n'%(key,metadict[key])) print('\r%d%%' % (int(((i+1)/n_img)*100)), end='') print('') # sort metadata (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} #=============================================================================================# # 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(filepath_data, sitename, 'S2') if not os.path.exists(os.path.join(filepath, '10m')): os.makedirs(os.path.join(filepath, '10m')) if not os.path.exists(os.path.join(filepath, '20m')): os.makedirs(os.path.join(filepath, '20m')) if not os.path.exists(os.path.join(filepath, '60m')): os.makedirs(os.path.join(filepath, '60m')) filepath_meta = os.path.join(filepath, 'meta') if not os.path.exists(filepath_meta): os.makedirs(filepath_meta) # 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_col = [x for k,x in enumerate(im_all_updated) if k not in idx_delete] else: im_col = im_all_updated n_img = len(im_col) # print how many images there are print('%s: %d images'%(satname,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_col[i]['id']) # read metadata im_dic = im_col[i] # get bands im_bands = im_dic['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) # write metadata in .txt file filename_txt = filename10.replace('_10m','').replace('.tif','') metadict = {'filename':filename10,'acc_georef':acc_georef[i], 'epsg':im_epsg[i]} with open(os.path.join(filepath_meta,filename_txt + '.txt'), 'w') as f: for key in metadict.keys(): f.write('%s\t%s\n'%(key,metadict[key])) print('\r%d%%' % (int(((i+1)/n_img)*100)), end='') print('') # sort metadata (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} # 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(filepath_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, there are 5 pairs of lat/lon with the fifth point equal to the first point. e.g. [[[151.3, -33.7],[151.4, -33.7],[151.4, -33.8],[151.3, -33.8], [151.3, -33.7]]] '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'] 'filepath_data': str Filepath to the directory where the images are downloaded Returns: ----------- metadata_updated: dict updated metadata with the information of the merged images """ # only for Sentinel-2 at this stage (not sure if this is needed for Landsat images) sat = 'S2' filepath = os.path.join(inputs['filepath'], 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]) # for each pair of images, merge them into one complete image for i,pair in enumerate(pairs): 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')), os.path.join(filepath, 'S2', 'meta', filenames[pair[index]].replace('_10m','').replace('.tif','.txt'))]) im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata = 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]) # remove the metadata .txt file of the duplicate image os.chmod(fn_im[1][3], 0o777) os.remove(fn_im[1][3]) print('%d pairs of overlapping Sentinel-2 images were merged' % len(pairs)) # update the metadata dict (delete all the duplicates) metadata_updated = copy.deepcopy(metadata) filenames_copy = metadata_updated[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 metadata_updated[sat].keys(): metadata_updated[sat][key] = [metadata_updated[sat][key][_] for _ in index_list] return metadata_updated def get_metadata(inputs): """ Gets the metadata from the downloaded .txt files in the \meta folders. KV WRL 2018 Arguments: ----------- inputs: dict dictionnary that contains the following fields: 'sitename': str String containig the name of the site 'filepath_data': str Filepath to the directory where the images are downloaded Returns: ----------- metadata: dict contains the information about the satellite images that were downloaded: filename, georeferencing accuracy and image coordinate reference system """ # directory containing the images filepath = os.path.join(inputs['filepath'],inputs['sitename']) # initialize metadata dict metadata = dict([]) # loop through the satellite missions for satname in ['L5','L7','L8','S2']: # if a folder has been created for the given satellite mission if satname in os.listdir(filepath): # update the metadata dict metadata[satname] = {'filenames':[], 'acc_georef':[], 'epsg':[], 'dates':[]} # directory where the metadata .txt files are stored filepath_meta = os.path.join(filepath, satname, 'meta') # get the list of filenames and sort it chronologically filenames_meta = os.listdir(filepath_meta) filenames_meta.sort() # loop through the .txt files for im_meta in filenames_meta: # read them and extract the metadata info: filename, georeferencing accuracy # epsg code and date with open(os.path.join(filepath_meta, im_meta), 'r') as f: filename = f.readline().split('\t')[1].replace('\n','') acc_georef = float(f.readline().split('\t')[1].replace('\n','')) epsg = int(f.readline().split('\t')[1].replace('\n','')) date_str = filename[0:19] date = pytz.utc.localize(datetime(int(date_str[:4]),int(date_str[5:7]), int(date_str[8:10]),int(date_str[11:13]), int(date_str[14:16]),int(date_str[17:19]))) # store the information in the metadata dict metadata[satname]['filenames'].append(filename) metadata[satname]['acc_georef'].append(acc_georef) metadata[satname]['epsg'].append(epsg) metadata[satname]['dates'].append(date) # save a .pkl file containing the metadata dict with open(os.path.join(filepath, inputs['sitename'] + '_metadata' + '.pkl'), 'wb') as f: pickle.dump(metadata, f) return metadata