diff --git a/README.md b/README.md index 154ac367..ccf851ff 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ CoastSat is an open-source software toolkit written in Python that enables users Latest updates :arrow_forward: *(2024/10/02)* -CoastSat v3.0: integration with [FES2022 global tide model](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/release-fes22.html) to perform tidal correction within CoastSat. +CoastSat v3.0: integration with [FES2022 global tide model](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/release-fes22.html) to perform **tidal correction** and **beach slope estimation** within CoastSat. :arrow_forward: *(2024/08/29)* CoastSat v2.7: reverse compatibility for file downloads (pre v2.6) and removed Collection 1 (deprecated, throws an error) @@ -67,6 +67,7 @@ The toolbox has the following functionalities: 3. intersection of the 2D shorelines with user-defined shore-normal transects. 4. tidal correction using tide/water levels and an estimate of the beach slope. 5. post-processing of the shoreline time-series, despiking and seasonal averaging. +6. Beach slope estimation using satellite-derived shorelines and predicted tides ### Table of Contents diff --git a/example.py b/example.py index 59f4183b..42e1c923 100644 --- a/example.py +++ b/example.py @@ -21,7 +21,7 @@ from datetime import datetime, timedelta import pytz from pyproj import CRS -from coastsat import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects +from coastsat import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects, SDS_slope # region of interest (longitude, latitude in WGS84) polygon = [[[151.301454, -33.700754], @@ -246,7 +246,7 @@ df.to_csv(fn, sep=',') print('Time-series of the shoreline change along the transects saved as:\n%s'%fn) -#%% 4. Tidal correction +#%% 5. Tidal correction # For this example, we can use the FES2022 global tide model to predict tides at our beach for all the image times. # To setup FES2022, follow the instructions at https://github.com/kvos/CoastSat/blob/master/doc/FES2022_setup @@ -259,8 +259,6 @@ handlers = pyfes.load_config(config) ocean_tide = handlers['tide'] load_tide = handlers['radial'] -# load coastsat module to estimate slopes -from coastsat import SDS_slopes # get polygon centroid centroid = np.mean(polygon[0], axis=0) @@ -270,10 +268,10 @@ date_range = [pytz.utc.localize(datetime(2024,1,1)), pytz.utc.localize(datetime(2025,1,1))] timestep = 900 # seconds -dates_ts, tides_ts = SDS_slopes.compute_tide(centroid, date_range, timestep, ocean_tide, load_tide) +dates_ts, tides_ts = SDS_slope.compute_tide(centroid, date_range, timestep, ocean_tide, load_tide) # get tide levels corresponding to the time of image acquisition dates_sat = output['dates'] -tides_sat = SDS_slopes.compute_tide_dates(centroid, output['dates'], ocean_tide, load_tide) +tides_sat = SDS_slope.compute_tide_dates(centroid, output['dates'], ocean_tide, load_tide) # If you have measure tide levels you can also use those instead. # When using your own file make sure that the dates are in UTC time, as the CoastSat shorelines are also in UTC @@ -330,8 +328,9 @@ ax.text(0.5,0.95, key, bbox=dict(boxstyle="square", ec='k',fc='w'), ha='center', va='top', transform=ax.transAxes, fontsize=14) ax.legend() +fig.savefig(os.path.join(filepath,'%s_timeseries_corrected.jpg'%sitename),dpi=200) -#%% 5. Time-series post-processing +#%% 6. Time-series post-processing # load mapped shorelines from 1984 (mapped with the previous code) filename_output = os.path.join(os.getcwd(),'examples','NARRA_output.pkl') @@ -356,14 +355,14 @@ va='center', ha='right', bbox=dict(boxstyle="square", ec='k',fc='w')) # load long time-series (1984-2021) -filepath = os.path.join(os.getcwd(),'examples','NARRA_time_series_tidally_corrected.csv') -df = pd.read_csv(filepath, parse_dates=['dates']) +filepath_ts = os.path.join(os.getcwd(),'examples','NARRA_time_series_tidally_corrected.csv') +df = pd.read_csv(filepath_ts, parse_dates=['dates']) dates = [_.to_pydatetime() for _ in df['dates']] cross_distance = dict([]) for key in transects.keys(): cross_distance[key] = np.array(df[key]) -#%% 5.1 Remove outliers +#%% 6.1 Remove outliers # plot Otsu thresholds for the mapped shorelines fig,ax = plt.subplots(1,1,figsize=[12,5],tight_layout=True) @@ -374,7 +373,7 @@ ax.set(title='Otsu thresholds on MNDWI for the %d shorelines mapped'%len(output['shorelines']), ylim=[-0.6,0.2],ylabel='otsu threshold') ax.legend(loc='upper left') -fig.savefig(os.path.join(inputs['filepath'], inputs['sitename'], 'otsu_threhsolds.jpg')) +fig.savefig(os.path.join(inputs['filepath'], inputs['sitename'], '%s_otsu_threhsolds.jpg'%sitename), dpi=200) # remove outliers in the time-series (despiking) settings_outliers = {'otsu_threshold': [-.5,0], # min and max intensity threshold use for contouring the shoreline @@ -383,8 +382,11 @@ } cross_distance = SDS_transects.reject_outliers(cross_distance,output,settings_outliers) -#%% 5.2 Seasonal averaging +#%% 6.2 Seasonal averaging +fp_seasonal = os.path.join(filepath,'jpg_files','seasonal_timeseries') +if not os.path.exists(fp_seasonal): os.makedirs(fp_seasonal) +print('Outputs will be saved in %s'%fp_seasonal) # compute seasonal averages along each transect season_colors = {'DJF':'C3', 'MAM':'C1', 'JJA':'C2', 'SON':'C0'} for key in cross_distance.keys(): @@ -408,9 +410,13 @@ ax.plot(dict_seas[seas]['dates'], dict_seas[seas]['chainages'], 'o', mec='k', color=season_colors[seas], label=seas,ms=5) ax.legend(loc='lower left',ncol=6,markerscale=1.5,frameon=True,edgecolor='k',columnspacing=1) + fig.savefig(os.path.join(fp_seasonal,'%s_timeseries_seasonal.jpg'%sitename), dpi=200) + +#%% 6.3 Monthly averaging -#%% 5.3 Monthly averaging - +fp_monthly = os.path.join(filepath,'jpg_files','seasonal_timeseries') +if not os.path.exists(fp_seasonal): os.makedirs(fp_monthly) +print('Outputs will be saved in %s'%fp_monthly) # compute monthly averages along each transect month_colors = plt.get_cmap('tab20') for key in cross_distance.keys(): @@ -434,13 +440,126 @@ ax.plot(dict_month[month]['dates'], dict_month[month]['chainages'], 'o', mec='k', color=month_colors(k), label=month,ms=5) ax.legend(loc='lower left',ncol=7,markerscale=1.5,frameon=True,edgecolor='k',columnspacing=1) + fig.savefig(os.path.join(fp_monthly,'%s_timeseries_monthly.jpg'%sitename), dpi=200) + +#%% 7. Beach slope estimation + +# This section uses the same long-term time-series of shoreline change to demonstrate how to estimate the beach-face slope. +# For a more detailed tutorial visit https://github.com/kvos/CoastSat.slope + +# create folder to save outputs from slope estimation +fp_slopes = os.path.join(filepath,'slope_estimation') +if not os.path.exists(fp_slopes): + os.makedirs(fp_slopes) +print('Outputs will be saved in %s'%fp_slopes) + +# load mapped shorelines from 1984 using Landsat 5, 7 and 8 +filename_output = os.path.join(os.getcwd(),'examples','NARRA_output.pkl') +with open(filename_output, 'rb') as f: + output = pickle.load(f) +# remove duplicates (images taken on the same date by the same satellite) +output = SDS_tools.remove_duplicates(output) +# remove inaccurate georeferencing (set threshold to 10 m) +output = SDS_tools.remove_inaccurate_georef(output, 10) +# compute intersections +settings_transects = { # parameters for computing intersections + 'along_dist': 25, # along-shore distance to use for computing the intersection + 'min_points': 3, # minimum number of shoreline points to calculate an intersection + 'max_std': 15, # max std for points around transect + 'max_range': 30, # max range for points around transect + 'min_chainage': -100, # largest negative value along transect (landwards of transect origin) + 'multiple_inter': 'auto', # mode for removing outliers ('auto', 'nan', 'max') + 'auto_prc': 0.1, # percentage of the time that multiple intersects are present to use the max + } +cross_distance = SDS_transects.compute_intersection_QC(output, transects, settings_transects) +# remove outliers in the time-series (coastal despiking) +settings_outliers = {'max_cross_change': 40, # maximum cross-shore change observable between consecutive timesteps + 'otsu_threshold': [-.5,0], # min and max intensity threshold use for contouring the shoreline + 'plot_fig': False, # whether to plot the intermediate steps + } +cross_distance = SDS_transects.reject_outliers(cross_distance,output,settings_outliers) + +# plot time-series +SDS_slope.plot_cross_distance(output['dates'],cross_distance) + +# slope estimation settings +days_in_year = 365.2425 +seconds_in_day = 24*3600 +settings_slope = {'slope_min': 0.035, # minimum slope to trial + 'slope_max': 0.2, # maximum slope to trial + 'delta_slope': 0.005, # slope increment + 'n0': 50, # parameter for Nyquist criterium in Lomb-Scargle transforms + 'freq_cutoff': 1./(seconds_in_day*30), # 1 month frequency + 'delta_f': 100*1e-10, # deltaf for identifying peak tidal frequency band + 'prc_conf': 0.05, # percentage above minimum to define confidence bands in energy curve + 'plot_fig': True, # whether to plot the intermediary products during analysis + } +# range of slopes to test for +beach_slopes = SDS_slope.range_slopes(settings_slope['slope_min'], settings_slope['slope_max'], settings_slope['delta_slope']) + +# range of dates over which to perform the analysis (2 Landsat satellites) +settings_slope['date_range'] = [1999,2022] +# re-write in datetime objects (same as shoreline in UTC) +settings_slope['date_range'] = [pytz.utc.localize(datetime(settings_slope['date_range'][0],5,1)), + pytz.utc.localize(datetime(settings_slope['date_range'][1],1,1))] +# clip the time-series between 1999 and 2022 as we need at least 2 Landsat satellites +idx_dates = [np.logical_and(_>settings_slope['date_range'][0],_