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.
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
#==========================================================#
|
|
# Shoreline extraction from satellite images
|
|
#==========================================================#
|
|
|
|
# load modules
|
|
import os
|
|
import pickle
|
|
import warnings
|
|
warnings.filterwarnings("ignore")
|
|
import matplotlib.pyplot as plt
|
|
|
|
import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools
|
|
|
|
# define the area of interest (longitude, latitude)
|
|
polygon = SDS_tools.coords_from_kml('NARRA.kml')
|
|
|
|
# define dates of interest
|
|
dates = ['2015-01-01', '2019-01-01']
|
|
|
|
# define satellite missions
|
|
sat_list = ['S2']
|
|
|
|
# give a name to the site
|
|
sitename = 'NARRA'
|
|
|
|
# put all the inputs into a dictionnary
|
|
inputs = {
|
|
'polygon': polygon,
|
|
'dates': dates,
|
|
'sat_list': sat_list,
|
|
'sitename': sitename
|
|
}
|
|
|
|
# download satellite images (also saves metadata.pkl)
|
|
metadata = SDS_download.get_images(inputs)
|
|
|
|
# if you have already downloaded the images, just load the metadata file
|
|
filepath = os.path.join(os.getcwd(), 'data', sitename)
|
|
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
|
|
metadata = pickle.load(f)
|
|
|
|
#%%
|
|
# settings needed to run the shoreline extraction
|
|
settings = {
|
|
|
|
# general parameters:
|
|
'cloud_thresh': 0.2, # threshold on maximum cloud cover
|
|
'output_epsg': 28356, # epsg code of spatial reference system desired for the output
|
|
|
|
# shoreline detection parameters:
|
|
'min_beach_size': 20, # minimum number of connected pixels for a beach
|
|
'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels
|
|
'min_length_sl': 200, # minimum length of shoreline perimeter to be kept
|
|
'max_dist_ref': 100, # max distance (in meters) allowed from a reference shoreline
|
|
|
|
# quality control:
|
|
'check_detection': True, # if True, shows each shoreline detection and lets the user
|
|
# decide which ones are correct and which ones are false due to
|
|
# the presence of clouds
|
|
# also add the inputs
|
|
'inputs': inputs
|
|
}
|
|
|
|
|
|
# preprocess images (cloud masking, pansharpening/down-sampling)
|
|
#SDS_preprocess.save_jpg(metadata, settings)
|
|
|
|
# create a reference shoreline (helps to identify outliers and false detections)
|
|
settings['refsl'] = SDS_preprocess.get_reference_sl_manual(metadata, settings)
|
|
#settings['refsl'] = SDS_preprocess.get_reference_sl_Australia(settings)
|
|
|
|
# extract shorelines from all images (also saves output.pkl)
|
|
output = SDS_shoreline.extract_shorelines(metadata, settings)
|
|
|
|
# plot shorelines
|
|
plt.figure()
|
|
plt.axis('equal')
|
|
plt.xlabel('Eastings [m]')
|
|
plt.ylabel('Northings [m]')
|
|
for satname in output.keys():
|
|
if satname == 'meta':
|
|
continue
|
|
for i in range(len(output[satname]['shoreline'])):
|
|
sl = output[satname]['shoreline'][i]
|
|
date = output[satname]['timestamp'][i]
|
|
plt.plot(sl[:, 0], sl[:, 1], '.', label=date.strftime('%d-%m-%Y'))
|
|
plt.legend() |