diff --git a/NARRA.kml b/NARRA.kml
deleted file mode 100644
index ab57b18..0000000
--- a/NARRA.kml
+++ /dev/null
@@ -1,62 +0,0 @@
-
-
-
- NARRA
-
-
-
-
- normal
- #poly-000000-1200-77-nodesc-normal
-
-
- highlight
- #poly-000000-1200-77-nodesc-highlight
-
-
-
- Polygon 1
- #poly-000000-1200-77-nodesc
-
-
-
- 1
-
- 151.2957545,-33.7012561,0
- 151.297557,-33.7388075,0
- 151.312234,-33.7390216,0
- 151.311204,-33.701399,0
- 151.2957545,-33.7012561,0
-
-
-
-
-
-
-
diff --git a/README.md b/README.md
index 48cff50..07bbc64 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ The underlying approach and application of the CoastSat toolkit are described in
*Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (submitted). CoastSat: a Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery, Environmental Modelling and Software*.
There are two main steps:
-- assisted retrieval from from Google Earth Engine of all avaiable satellite images spanning the user-defined region of interest and time period
+- assisted retrieval from Google Earth Engine of all avaiable satellite images spanning the user-defined region of interest and time period
- automated extraction of shorelines from all the selected images using a sub-pixel resolution technique
@@ -105,7 +105,7 @@ jupyter notebook
```
A web browser window will open. Point to the directory where you downloaded/cloned this repository and click on `example_jupyter.ipynb`.
-The following sections guide the reader through the different functionalities of CoastSat with an example at Narrabeen-Collaroy beach (Australia). If you prefer to use Spyder or PyCharm or other integrated development environments (IDEs), a Python script is also included `main.py` in the repository.
+The following sections guide the reader through the different functionalities of CoastSat with an example at Narrabeen-Collaroy beach (Australia). If you prefer to use Spyder or PyCharm or other integrated development environments (IDEs), a Python script `main.py` is also included in the repository. If using `main.py` on Spyder, make sure that the Graphics Backend is set to **Automatic** and not **Inline** (as this mode doesn't allow to interact with the figures). To change this setting go under Preferences>IPython console>Graphics.
To run a Jupyter Notebook, place your cursor inside one of the code sections and then clikc on the 'run' button up in the top menu to run that section and progress forward (as shown in the animation below).
@@ -129,16 +129,16 @@ It is now time to map the sandy shorelines!
The following user-defined settings are required:
- `cloud_thresh`: threshold on maximum cloud cover that is acceptable on the images (value between 0 and 1 - this may require some initial experimentation)
-- `output_epsg`: epsg code defining the spatial reference system of the shoreline coordinates
+- `output_epsg`: epsg code defining the spatial reference system of the shoreline coordinates. It has to be a cartesion coordinate system (i.e. projected) and not a geographical coordinate system (in latitude and longitude angles).
- `check_detection`: if set to `True` allows the user to quality control each shoreline detection
See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. If the user wants to quality control the mapped shorelines and manually validate each detection, the parameter `check_detection` should be set to `True`.
-In addition, there are extra parameters (`min_beach_size`, `buffer_size`, `min_length_sl`) that can be tuned to optimise the shoreline detection (for Advanced users only). For the moment leave these parameters set to their default values, we will see later how they can be modified.
+In addition, there are extra parameters (`min_beach_size`, `buffer_size`, `min_length_sl`, `cloud_mask_issue`) that can be tuned to optimise the shoreline detection (for Advanced users only). For the moment leave these parameters set to their default values, we will see later how they can be modified.
An example of settings is provided here:
-
+
Once all the settings have been defined, the batch shoreline detection can be launched by calling:
```
@@ -162,6 +162,7 @@ As mentioned above, there are some additional parameters that can be modified to
- `min_beach_area`: minimum allowable object area (in metres^2) for the class 'sand'. During the image classification, some features (for example, building roofs) may be incorrectly labelled as sand. To correct this, all the objects classified as sand containing less than a certain number of connected pixels are removed from the sand class. The default value of `min_beach_area` is 4500 m^2, which corresponds to 20 connected pixels of 15 m^2. If you are looking at a very small beach (<20 connected pixels on the images), try decreasing the value of this parameter.
- `buffer_size`: radius (in metres) that defines the buffer around sandy pixels that is considered for the shoreline detection. The default value of `buffer_size` is 150 m. This parameter should be increased if you have a very wide (>150 m) surf zone or inter-tidal zone.
- `min_length_sl`: minimum length (in metres) of shoreline perimeter to be valid. This can be used to discard small features that are detected but do not correspond to the sand-water shoreline. The default value is 200 m. If the shoreline that you are trying to map is shorter than 200 m, decrease the value of this parameter.
+- `cloud_mask_issue`: the cloud mask algorithm applied to Landsat images by USGS, namely CFMASK, does have difficulties sometimes with very bright features such as beaches or white-water in the ocean. This may result in pixels corresponding to a beach being identified as clouds in the cloud mask (appear as black pixels on your images). If this issue seems to be present in a large proportion of images from your local beach, you can switch this parameter to `True` and CoastSat will remove from the cloud mask the pixels that form very thin linear features (as often these are beaches and not clouds). Only activate this parameter if you observe this very specific cloud mask issue, otherwise leave to the default value of `False`.
#### Reference shoreline
@@ -180,13 +181,25 @@ The maximum distance (in metres) allowed from the reference shoreline is defined
### 2.3 Shoreline change analysis
-This section shows how to obtain time-series of shoreline change along shore-normal transects.
-
-The user can draw shore-normal transects by calling:
+This section shows how to obtain time-series of shoreline change along shore-normal transects. Each transect is defined by two points, its origin and a second point that defines its orientation. The parameter `transect_length` determines how far (in metres) from the origin the transect will span. There are 3 options to define the coordinates of the transects:
+1. The user can interactively draw shore-normal transects along the beach:
```
-settings['transect_length'] = 500 # defines the length of the transects in metres
transects = SDS_transects.draw_transects(output, settings)
```
+2. Load the transect coordinates from a KML file:
+```
+transects = SDS_transects.load_transects_from_kml('transects.kml')
+```
+3. Create the transects by manually providing the coordinates of two points:
+```
+transects = dict([])
+transects['Transect 1'] = np.array([[342836, ,6269215], [343315, 6269071]])
+transects['Transect 2'] = np.array([[342482, 6268466], [342958, 6268310]])
+transects['Transect 3'] = np.array([[342185, 6267650], [342685, 6267641]])
+```
+
+**Note:** if you choose option 2 or 3, make sure that the points that you are providing are in the spatial reference system defined by `settings['output_epsg']`.
+
Once the shore-normal transects have been defined, the intersection between the 2D shorelines and the transects is computed with the following function:
```
settings['along_dist'] = 25
diff --git a/SDS_download.py b/SDS_download.py
index 519e0e8..3bdbe01 100644
--- a/SDS_download.py
+++ b/SDS_download.py
@@ -543,14 +543,20 @@ def retrieve_images(inputs):
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 faile a value of -1 is stored in the metadata
- try:
+ # 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)
- except:
+ 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)
@@ -564,7 +570,7 @@ def retrieve_images(inputs):
'epsg':im_epsg_sorted, 'filenames':filenames_sorted}
print('\nFinished with ' + satname)
- # merge overlapping images (only if polygon is at the edge of an image)
+ # 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)
@@ -643,7 +649,7 @@ def merge_overlapping_images(metadata,inputs):
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)
+ 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
@@ -772,7 +778,7 @@ def remove_cloudy_images(metadata,inputs,cloud_thresh):
# 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)
+ 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]))
diff --git a/SDS_preprocess.py b/SDS_preprocess.py
index 2d72e9d..8fbf1f4 100644
--- a/SDS_preprocess.py
+++ b/SDS_preprocess.py
@@ -28,7 +28,7 @@ import SDS_tools
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
-def create_cloud_mask(im_qa, satname):
+def create_cloud_mask(im_qa, satname, cloud_mask_issue):
"""
Creates a cloud mask using the information contained in the QA band.
@@ -40,6 +40,8 @@ def create_cloud_mask(im_qa, satname):
Image containing the QA band
satname: string
short name for the satellite (L5, L7, L8 or S2)
+ cloud_mask_issue: boolean
+ True if there is an issue with the cloud mask and sand pixels are being masked on the images
Returns:
-----------
@@ -58,9 +60,15 @@ def create_cloud_mask(im_qa, satname):
# find which pixels have bits corresponding to cloud values
cloud_mask = np.isin(im_qa, cloud_values)
- # remove isolated cloud pixels (there are some in the swash zone and they are not clouds)
+ # remove cloud pixels that form very thin features. These are beach or swash pixels that are
+ # erroneously identified as clouds by the CFMASK algorithm applied to the images by the USGS.
if sum(sum(cloud_mask)) > 0 and sum(sum(~cloud_mask)) > 0:
morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
+ if cloud_mask_issue:
+ elem = morphology.square(3) # use a square of width 3 pixels
+ cloud_mask = morphology.binary_opening(cloud_mask,elem) # perform image opening
+ # remove objects with less than 25 connected pixels
+ morphology.remove_small_objects(cloud_mask, min_size=25, connectivity=1, in_place=True)
return cloud_mask
@@ -209,7 +217,7 @@ def rescale_image_intensity(im, cloud_mask, prob_high):
return im_adj
-def preprocess_single(fn, satname):
+def preprocess_single(fn, satname, cloud_mask_issue):
"""
Reads the image and outputs the pansharpened/down-sampled multispectral bands, the
georeferencing vector of the image (coordinates of the upper left pixel), the cloud mask and
@@ -226,6 +234,8 @@ def preprocess_single(fn, satname):
resolution (30m and 15m for Landsat 7-8, 10m, 20m, 60m for Sentinel-2)
satname: str
name of the satellite mission (e.g., 'L5')
+ cloud_mask_issue: boolean
+ True if there is an issue with the cloud mask and sand pixels are being masked on the images
Returns:
-----------
@@ -262,7 +272,7 @@ def preprocess_single(fn, satname):
# create cloud mask
im_qa = im_ms[:,:,5]
im_ms = im_ms[:,:,:-1]
- cloud_mask = create_cloud_mask(im_qa, satname)
+ cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1)
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True,
@@ -314,7 +324,7 @@ def preprocess_single(fn, satname):
# create cloud mask
im_qa = im_ms[:,:,5]
- cloud_mask = create_cloud_mask(im_qa, satname)
+ cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5]
@@ -374,7 +384,7 @@ def preprocess_single(fn, satname):
# create cloud mask
im_qa = im_ms[:,:,5]
- cloud_mask = create_cloud_mask(im_qa, satname)
+ cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5]
@@ -456,7 +466,7 @@ def preprocess_single(fn, satname):
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im60 = np.stack(bands, 2)
imQA = im60[:,:,0]
- cloud_mask = create_cloud_mask(imQA, satname)
+ cloud_mask = create_cloud_mask(imQA, satname, cloud_mask_issue)
# resize the cloud mask using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True,
mode='constant')
@@ -551,10 +561,12 @@ def save_jpg(metadata, settings):
contains all the information about the satellite images that were downloaded
settings: dict
contains the following fields:
- 'cloud_thresh': float
+ cloud_thresh: float
value between 0 and 1 indicating the maximum cloud fraction in the image that is accepted
- 'sitename': string
+ sitename: string
name of the site (also name of the folder where the images are stored)
+ cloud_mask_issue: boolean
+ True if there is an issue with the cloud mask and sand pixels are being masked on the images
Returns:
-----------
@@ -582,7 +594,7 @@ def save_jpg(metadata, settings):
# image filename
fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
# read and preprocess image
- im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname)
+ im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname, settings['cloud_mask_issue'])
# calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1]))
@@ -592,6 +604,10 @@ def save_jpg(metadata, settings):
# save .jpg with date and satellite in the title
date = filenames[i][:10]
create_jpg(im_ms, cloud_mask, date, satname, filepath_jpg)
+
+ # print the location where the images have been saved
+ print('Satellite images saved as .jpg in ' + os.path.join(os.getcwd(), 'data', sitename,
+ 'jpg_files', 'preprocessed'))
def get_reference_sl_manual(metadata, settings):
"""
@@ -650,14 +666,14 @@ def get_reference_sl_manual(metadata, settings):
filepath = SDS_tools.get_filepath(settings['inputs'],satname)
filenames = metadata[satname]['filenames']
else:
- print('You cannot digitize the shoreline on L7 images, add another L8, S2 or L5 to your dataset.')
+ raise Exception('You cannot digitize the shoreline on L7 images, add another L8, S2 or L5 to your dataset.')
# loop trhough the images
for i in range(len(filenames)):
# read image
fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
- im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname)
+ im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname, settings['cloud_mask_issue'])
# calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1]))
@@ -684,7 +700,7 @@ def get_reference_sl_manual(metadata, settings):
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
# let user click on the image once
- pt_input = ginput(n=1, timeout=1000000, show_clicks=True)
+ pt_input = ginput(n=1, timeout=1e9, show_clicks=False)
pt_input = np.array(pt_input)
# if clicks next to , show another image
if pt_input[0][0] > im_ms.shape[1]/2:
@@ -694,102 +710,78 @@ def get_reference_sl_manual(metadata, settings):
# remove keep and skip buttons
keep_button.set_visible(False)
skip_button.set_visible(False)
- # update title (instructions)
- plt.title('Click points along the shoreline every ~500 m.\n' +
- 'Start at one end of the beach.\n' + 'When finished digitizing, click ',
- fontsize=14)
- plt.draw()
- # let user click on the shoreline
- pts = ginput(n=50000, timeout=1e9, show_clicks=True)
- pts_pix = np.array(pts)
-
- # interpolate between points and show the output to the user
- pts_pix_interp = np.expand_dims(np.array([np.nan, np.nan]),axis=0)
- for k in range(len(pts_pix)-1):
- if pts_pix[k,0] < pts_pix[k+1,0]:
- x = pts_pix[[k,k+1],0]
- y = pts_pix[[k,k+1],1]
- else:
- x = pts_pix[[k+1,k],0]
- y = pts_pix[[k+1,k],1]
- xvals = np.linspace(x[0],x[1],50)
- yinterp = np.interp(xvals,x,y)
- pts_pix_interp = np.append(pts_pix_interp,
- np.transpose(np.array([xvals,yinterp])), axis=0)
- pts_pix_interp = np.delete(pts_pix_interp,0,axis=0)
- plt.plot(pts_pix_interp[:,0], pts_pix_interp[:,1], 'r.', markersize=3)
- plt.title('Saving reference shoreline as ' + sitename + '_reference_shoreline.pkl ...')
- plt.draw()
- ginput(n=1, timeout=5, show_clicks=True)
- plt.close()
+ # create two new buttons
+ add_button = plt.text(0, 0.9, 'add', size=16, ha="left", va="top",
+ transform=plt.gca().transAxes,
+ bbox=dict(boxstyle="square", ec='k',fc='w'))
+ end_button = plt.text(1, 0.9, 'end', size=16, ha="right", va="top",
+ transform=plt.gca().transAxes,
+ bbox=dict(boxstyle="square", ec='k',fc='w'))
+ # add multiple reference shorelines (until user clicks on button)
+ pts_sl = np.expand_dims(np.array([np.nan, np.nan]),axis=0)
+ while 1:
+ add_button.set_visible(False)
+ end_button.set_visible(False)
+ # update title (instructions)
+ plt.title('Click points along the shoreline (enough points to capture the beach curvature).\n' +
+ 'Start at one end of the beach.\n' + 'When finished digitizing, click ',
+ fontsize=14)
+ plt.draw()
+ # let user click on the shoreline
+ pts = ginput(n=50000, timeout=1e9, show_clicks=True)
+ pts_pix = np.array(pts)
+ # convert pixel coordinates to world coordinates
+ pts_world = SDS_tools.convert_pix2world(pts_pix[:,[1,0]], georef)
+ # interpolate between points clicked by the user (1m resolution)
+ pts_world_interp = np.expand_dims(np.array([np.nan, np.nan]),axis=0)
+ for k in range(len(pts_world)-1):
+ pt_dist = np.linalg.norm(pts_world[k,:]-pts_world[k+1,:])
+ xvals = np.arange(0,pt_dist)
+ yvals = np.zeros(len(xvals))
+ pt_coords = np.zeros((len(xvals),2))
+ pt_coords[:,0] = xvals
+ pt_coords[:,1] = yvals
+ phi = 0
+ deltax = pts_world[k+1,0] - pts_world[k,0]
+ deltay = pts_world[k+1,1] - pts_world[k,1]
+ phi = np.pi/2 - np.math.atan2(deltax, deltay)
+ tf = transform.EuclideanTransform(rotation=phi, translation=pts_world[k,:])
+ pts_world_interp = np.append(pts_world_interp,tf(pt_coords), axis=0)
+ pts_world_interp = np.delete(pts_world_interp,0,axis=0)
+ # convert to pixel coordinates and plot
+ pts_pix_interp = SDS_tools.convert_world2pix(pts_world_interp, georef)
+ pts_sl = np.append(pts_sl, pts_world_interp, axis=0)
+ plt.plot(pts_pix_interp[:,0], pts_pix_interp[:,1], 'r--')
+ plt.plot(pts_pix_interp[0,0], pts_pix_interp[0,1],'ko')
+ plt.plot(pts_pix_interp[-1,0], pts_pix_interp[-1,1],'ko')
+ # update title and buttons
+ add_button.set_visible(True)
+ end_button.set_visible(True)
+ plt.title('click to digitize another shoreline or to finish and save the shoreline(s)',
+ fontsize=14)
+ plt.draw()
+ pt_input = ginput(n=1, timeout=1e9, show_clicks=False)
+ pt_input = np.array(pt_input)
+ # if user clicks on , save the points and break the loop
+ if pt_input[0][0] > im_ms.shape[1]/2:
+ add_button.set_visible(False)
+ end_button.set_visible(False)
+ plt.title('Reference shoreline saved as ' + sitename + '_reference_shoreline.pkl')
+ plt.draw()
+ ginput(n=1, timeout=5, show_clicks=False)
+ plt.close()
+ break
+ pts_sl = np.delete(pts_sl,0,axis=0)
+ # convert world coordinates to user-defined coordinates
- # convert image coordinates to world coordinates
- pts_world = SDS_tools.convert_pix2world(pts_pix_interp[:,[1,0]], georef)
image_epsg = metadata[satname]['epsg'][i]
- pts_coords = SDS_tools.convert_epsg(pts_world, image_epsg, settings['output_epsg'])
+ pts_coords = SDS_tools.convert_epsg(pts_sl, image_epsg, settings['output_epsg'])
# save the reference shoreline
filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_reference_shoreline.pkl'), 'wb') as f:
pickle.dump(pts_coords, f)
- print('Reference shoreline has been saved')
+ print('Reference shoreline has been saved in ' + filepath)
break
- return pts_coords
-
-def get_reference_sl_Australia(settings):
- """
- Automatically finds a reference shoreline from a high resolution coastline of Australia
- (Smartline from Geoscience Australia). It finds the points of the national coastline vector
- that are situated inside the area of interest (polygon).
-
- KV WRL 2018
-
- Arguments:
- -----------
- settings: dict
- contains the following fields:
- 'cloud_thresh': float
- value between 0 and 1 indicating the maximum cloud fraction in the image that is accepted
- 'sitename': string
- name of the site (also name of the folder where the images are stored)
- 'output_epsg': int
- epsg code of the desired spatial reference system
-
- Returns:
- -----------
- ref_sl: np.array
- coordinates of the reference shoreline found in the shapefile
-
- """
-
- # load high-resolution shoreline of Australia
- filename = os.path.join(os.getcwd(), 'data', 'shoreline_Australia.pkl')
- with open(filename, 'rb') as f:
- sl = pickle.load(f)
- # spatial reference system of this shoreline
- sl_epsg = 4283 # GDA94 geographic
-
- # only select the points that sit inside the area of interest (polygon)
- polygon = settings['inputs']['polygon']
- # spatial reference system of the polygon (latitudes and longitudes)
- polygon_epsg = 4326 # WGS84 geographic
- polygon = SDS_tools.convert_epsg(np.array(polygon[0]), polygon_epsg, sl_epsg)[:,:-1]
-
- # use matplotlib function Path
- path = mpltPath.Path(polygon)
- sl_inside = sl[np.where(path.contains_points(sl))]
-
- # convert to desired output coordinate system
- ref_sl = SDS_tools.convert_epsg(sl_inside, sl_epsg, settings['output_epsg'])[:,:-1]
-
- # make a figure for quality control
- plt.figure()
- plt.axis('equal')
- plt.xlabel('Eastings [m]')
- plt.ylabel('Northings [m]')
- plt.plot(ref_sl[:,0], ref_sl[:,1], 'r.')
- polygon = SDS_tools.convert_epsg(polygon, sl_epsg, settings['output_epsg'])[:,:-1]
- plt.plot(polygon[:,0], polygon[:,1], 'k-')
-
- return ref_sl
\ No newline at end of file
+ return pts_coords
\ No newline at end of file
diff --git a/SDS_shoreline.py b/SDS_shoreline.py
index 9c794d7..6de4fd5 100644
--- a/SDS_shoreline.py
+++ b/SDS_shoreline.py
@@ -163,40 +163,18 @@ def classify_image_NN(im_ms, im_extra, cloud_mask, min_beach_area, satname):
3D image containing a boolean image for each class (im_classif == label)
"""
-
- if satname == 'L5':
- # load classifier (without panchromatic band)
- clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_nopan.pkl'))
- # calculate features
- n_features = 9
- im_features = np.zeros((im_ms.shape[0], im_ms.shape[1], n_features))
- im_features[:,:,[0,1,2,3,4]] = im_ms
- im_features[:,:,5] = nd_index(im_ms[:,:,3], im_ms[:,:,1], cloud_mask) # (NIR-G)
- im_features[:,:,6] = nd_index(im_ms[:,:,3], im_ms[:,:,2], cloud_mask) # ND(NIR-R)
- im_features[:,:,7] = nd_index(im_ms[:,:,0], im_ms[:,:,2], cloud_mask) # ND(B-R)
- im_features[:,:,8] = nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask) # ND(SWIR-G)
- vec_features = im_features.reshape((im_ms.shape[0] * im_ms.shape[1], n_features))
-
- elif satname in ['L7','L8']:
- # load classifier (with panchromatic band)
- clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_withpan.pkl'))
- # calculate features
- n_features = 10
- im_features = np.zeros((im_ms.shape[0], im_ms.shape[1], n_features))
- im_features[:,:,[0,1,2,3,4]] = im_ms
- im_features[:,:,5] = im_extra
- im_features[:,:,6] = nd_index(im_ms[:,:,3], im_ms[:,:,1], cloud_mask) # (NIR-G)
- im_features[:,:,7] = nd_index(im_ms[:,:,3], im_ms[:,:,2], cloud_mask) # ND(NIR-R)
- im_features[:,:,8] = nd_index(im_ms[:,:,0], im_ms[:,:,2], cloud_mask) # ND(B-R)
- im_features[:,:,9] = nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask) # ND(SWIR-G)
- vec_features = im_features.reshape((im_ms.shape[0] * im_ms.shape[1], n_features))
- elif satname == 'S2':
+ if satname == 'S2':
# load classifier (special classifier for Sentinel-2 images)
clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_S2.pkl'))
- # calculate features
- vec_features = calculate_features(im_ms, cloud_mask, np.ones(cloud_mask.shape).astype(bool))
- vec_features[np.isnan(vec_features)] = 1e-9 # NaN values are create when std is too close to 0
+
+ else:
+ # load classifier (special classifier for Landsat images)
+ clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_Landsat.pkl'))
+
+ # calculate features
+ vec_features = calculate_features(im_ms, cloud_mask, np.ones(cloud_mask.shape).astype(bool))
+ vec_features[np.isnan(vec_features)] = 1e-9 # NaN values are create when std is too close to 0
# remove NaNs and cloudy pixels
vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
@@ -269,7 +247,7 @@ def find_wl_contours1(im_ndwi, cloud_mask):
return contours
-def find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size):
+def find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, is_reference_sl):
"""
New robust method for extracting shorelines. Incorporates the classification component to
refine the treshold and make it specific to the sand/water interface.
@@ -287,6 +265,8 @@ def find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size):
buffer_size: int
size of the buffer around the sandy beach over which the pixels are considered in the
thresholding algorithm.
+ is_reference_sl: boolean
+ True if there is a reference shoreline, False otherwise
Returns: -----------
contours_wi: list of np.arrays
@@ -339,8 +319,13 @@ def find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size):
im_wi_buffer[~im_buffer] = np.nan
im_mwi_buffer = np.copy(im_mwi)
im_mwi_buffer[~im_buffer] = np.nan
- contours_wi = measure.find_contours(im_wi_buffer, t_wi)
- contours_mwi = measure.find_contours(im_mwi_buffer, t_mwi)
+
+ if is_reference_sl: # if there is a reference_shoreline map the shoreline on the entire image
+ contours_wi = measure.find_contours(im_wi, t_wi)
+ contours_mwi = measure.find_contours(im_mwi, t_mwi)
+ else: # otherwise only map the shoreline along the sandy pixels
+ contours_wi = measure.find_contours(im_wi_buffer, t_wi)
+ contours_mwi = measure.find_contours(im_mwi_buffer, t_mwi)
# remove contour points that are NaNs (around clouds)
contours = contours_wi
@@ -585,20 +570,24 @@ def extract_shorelines(metadata, settings):
metadata: dict
contains all the information about the satellite images that were downloaded
- inputs: dict
+ settings: dict
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']
-
+ sitename: str
+ String containig the name of the site
+ cloud_mask_issue: boolean
+ True if there is an issue with the cloud mask and sand pixels are being masked on the images
+ buffer_size: int
+ size of the buffer (m) around the sandy beach over which the pixels are considered in the
+ thresholding algorithm
+ min_beach_area: int
+ minimum allowable object area (in metres^2) for the class 'sand'
+ cloud_thresh: float
+ value between 0 and 1 defining the maximum percentage of cloud cover allowed in the images
+ output_epsg: int
+ output spatial reference system as EPSG code
+ check_detection: boolean
+ True to show each invidual detection and let the user validate the mapped shoreline
+
Returns:
-----------
output: dict
@@ -646,7 +635,7 @@ def extract_shorelines(metadata, settings):
# get 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)
+ im_ms, georef, cloud_mask, im_extra, imQA = SDS_preprocess.preprocess_single(fn, satname, settings['cloud_mask_issue'])
# get image spatial reference system (epsg code) from metadata dict
image_epsg = metadata[satname]['epsg'][i]
# calculate cloud cover
@@ -671,8 +660,9 @@ def extract_shorelines(metadata, settings):
contours_mwi = find_wl_contours1(im_mndwi, cloud_mask)
else:
# use classification to refine threshold and extract sand/water interface
+ is_reference_sl = 'reference_shoreline' in settings.keys()
contours_wi, contours_mwi = find_wl_contours2(im_ms, im_labels,
- cloud_mask, buffer_size_pixels)
+ cloud_mask, buffer_size_pixels, is_reference_sl)
except:
continue
diff --git a/SDS_transects.py b/SDS_transects.py
index 2c7fd12..1ba275d 100644
--- a/SDS_transects.py
+++ b/SDS_transects.py
@@ -14,6 +14,8 @@ import skimage.transform as transform
from pylab import ginput
import pickle
import simplekml
+import json
+from osgeo import ogr
def find_indices(lst, condition):
"imitation of MATLAB find function"
@@ -106,7 +108,11 @@ def draw_transects(output, settings):
origin = pts[0]
except:
fig1.gca().set_title('Transect locations', fontsize=16)
- fig1.savefig(os.path.join(filepath, sitename + 'transects.jpg'), dpi=200)
+ fig1.savefig(os.path.join(filepath, 'jpg_files', sitename + '_transect_locations.jpg'), dpi=200)
+ plt.title('Transects saved as ' + sitename + '_transects.pkl and ' + sitename + '_transects.kml ')
+ plt.draw()
+ ginput(n=1, timeout=5, show_clicks=True)
+ plt.close(fig1)
break
counter = counter + 1
# create the transect using the origin, orientation and length
@@ -134,9 +140,53 @@ def draw_transects(output, settings):
newline.coords = transects[key]
newline.description = 'user-defined cross-shore transect'
kml.save(os.path.join(filepath, sitename + '_transects.kml'))
+ print('Transect locations saved in ' + filepath)
return transects
+def load_transects_from_kml(filename):
+ """
+ Reads transect coordinates from a KML file.
+
+ Arguments:
+ -----------
+ filename: str
+ contains the path and filename of the KML file to be loaded
+
+ Returns:
+ -----------
+ transects: dict
+ contains the X and Y coordinates of each transect.
+
+ """
+
+ # set driver
+ drv = ogr.GetDriverByName('KML')
+ # read file
+ file = drv.Open(filename)
+ layer = file.GetLayer()
+ feature = layer.GetNextFeature()
+ # initialise transects dictionnary
+ transects = dict([])
+
+ while feature:
+
+ f_dict = json.loads(feature.ExportToJson())
+
+ # raise an exception if the KML file contains other features that LineString geometries
+ if not f_dict['geometry']['type'] == 'LineString':
+ raise Exception('The KML file you provided does not contain LineString geometries. Modify your KML file and try again.')
+ # store the name of the feature and coordinates in the transects dictionnary
+ else:
+ name = f_dict['properties']['Name']
+ coords = np.array(f_dict['geometry']['coordinates'])[:,:-1]
+ transects[name] = coords
+ feature = layer.GetNextFeature()
+
+ print('%d transects have been loaded' % len(transects.keys()))
+
+ return transects
+
def compute_intersection(output, transects, settings):
"""
Computes the intersection between the 2D mapped shorelines and the transects, to generate
@@ -146,10 +196,13 @@ def compute_intersection(output, transects, settings):
-----------
output: dict
contains the extracted shorelines and corresponding dates.
+ transects: dict
+ contains the X and Y coordinates of the transects (first and last point needed for each
+ transect).
settings: dict
contains parameters defining :
along_dist: alongshore distance to caluclate the intersection (median of points
- within this distance).
+ within this distance).
Returns:
-----------
diff --git a/classifiers/NN_4classes_nopan.pkl b/classifiers/NN_4classes_nopan.pkl
deleted file mode 100644
index 2a780fd..0000000
Binary files a/classifiers/NN_4classes_nopan.pkl and /dev/null differ
diff --git a/classifiers/NN_4classes_withpan.pkl b/classifiers/NN_4classes_withpan.pkl
deleted file mode 100644
index 375d597..0000000
Binary files a/classifiers/NN_4classes_withpan.pkl and /dev/null differ
diff --git a/example_jupyter.ipynb b/example_jupyter.ipynb
index 5ec5ef8..81e241e 100644
--- a/example_jupyter.ipynb
+++ b/example_jupyter.ipynb
@@ -24,6 +24,7 @@
"outputs": [],
"source": [
"import os\n",
+ "import numpy as np\n",
"import pickle\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
@@ -124,7 +125,8 @@
" # [ONLY FOR ADVANCED USERS] shoreline detection parameters:\n",
" 'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach\n",
" 'buffer_size': 150, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection\n",
- " 'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid \n",
+ " 'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid\n",
+ " 'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images \n",
"}"
]
},
@@ -218,7 +220,7 @@
"source": [
"## 4. Shoreline analysis\n",
"\n",
- "Shows how to plot the mapped shorelines and draw shore-normal transects and compute time-series of cross-shore distance along the transects."
+ "In this section we show how to compute time-series of cross-shore distance along user-defined shore-normal transects."
]
},
{
@@ -243,7 +245,25 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Create shore-normal transects along the beach"
+ "The shore-normal transects are defined by two points, the origin of the transect and a second point that defines its orientaion. The parameter *transect_length* determines how far from the origin the transects span."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "settings['transect_length'] = 500 # defines the length of the transects in metres"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are 3 options to define the coordinates of the shore-normal transects:\n",
+ "\n",
+ "**Option 1**: the user can interactively draw the shore-normal transects along the beach by calling:"
]
},
{
@@ -253,7 +273,6 @@
"outputs": [],
"source": [
"%matplotlib qt\n",
- "settings['transect_length'] = 500 # defines the length of the transects in metres\n",
"transects = SDS_transects.draw_transects(output, settings)"
]
},
@@ -261,7 +280,43 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Intersect the transects with the 2D shorelines to obtain time-series of cross-shore distance"
+ "**Option 2**: the user can load the transect coordinates (make sure the spatial reference system is the same as defined by *output_epsg* previously) from a .kml file by calling:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kml_file = 'NARRA_transects.kml'\n",
+ "transects = SDS_transects.load_transects_from_kml(kml_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Option 3**: manually provide the coordinates of the transects as shown in the example below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transects = dict([])\n",
+ "transects['Transect 1'] = np.array([[342836, 6269215], [343315, 6269071]])\n",
+ "transects['Transect 2'] = np.array([[342482, 6268466], [342958, 6268310]])\n",
+ "transects['Transect 3'] = np.array([[342185, 6267650], [342685, 6267641]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, intersect the transects with the 2D shorelines to obtain time-series of cross-shore distance"
]
},
{
diff --git a/main.py b/main.py
index 314f505..928d96b 100644
--- a/main.py
+++ b/main.py
@@ -8,14 +8,15 @@
# load modules
import os
+import numpy as np
import pickle
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects
-# region of interest (longitude, latitude), can also be loaded from a .kml polygon
-polygon = SDS_tools.coords_from_kml('NARRA.kml')
+# region of interest (longitude, latitude in WGS84), can be loaded from a .kml polygon
+polygon = SDS_tools.coords_from_kml('NARRA_polygon.kml')
#polygon = [[[151.301454, -33.700754],
# [151.311453, -33.702075],
# [151.307237, -33.739761],
@@ -23,7 +24,7 @@ polygon = SDS_tools.coords_from_kml('NARRA.kml')
# [151.301454, -33.700754]]]
# date range
-dates = ['2017-12-01', '2018-06-01']
+dates = ['2017-12-01', '2018-02-01']
# satellite missions
sat_list = ['L8','S2']
@@ -42,12 +43,12 @@ inputs = {
#%% 2. Retrieve images
# retrieve satellite images from GEE
-#metadata = SDS_download.retrieve_images(inputs)
+metadata = SDS_download.retrieve_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)
+#filepath = os.path.join(os.getcwd(), 'data', sitename)
+#with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
+# metadata = pickle.load(f)
#%% 3. Batch shoreline detection
@@ -65,11 +66,12 @@ settings = {
# [ONLY FOR ADVANCED USERS] shoreline detection parameters:
'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach
'buffer_size': 150, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection
- 'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid
+ 'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid
+ 'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images
}
# [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling)
-#SDS_preprocess.save_jpg(metadata, settings)
+SDS_preprocess.save_jpg(metadata, settings)
# [OPTIONAL] create a reference shoreline (helps to identify outliers and false detections)
settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings)
@@ -96,22 +98,40 @@ fig.set_size_inches([15.76, 8.52])
#%% 4. Shoreline analysis
-# if you have already mapped the shorelines, just load them
+# if you have already mapped the shorelines, load the output.pkl file
filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
output = pickle.load(f)
-# create shore-normal transects along the beach
-settings['transect_length'] = 500
-transects = SDS_transects.draw_transects(output, settings)
+# now we have to define cross-shore transects over which to quantify the shoreline changes
+# each transect is defined by two points, its origin and a second point that defines its orientation
+# the parameter transect length determines how far from the origin the transect will span
+settings['transect_length'] = 500
+
+# there are 3 options to create the transects:
+# - option 1: draw the shore-normal transects along the beach
+# - option 2: load the transect coordinates from a .kml file
+# - option 3: create the transects manually by providing the coordinates
+# option 1: draw origin of transect first and then a second point to define the orientation
+transects = SDS_transects.draw_transects(output, settings)
+
+# option 2: load the transects from a KML file
+#kml_file = 'NARRA_transects.kml'
+#transects = SDS_transects.load_transects_from_kml(kml_file)
+
+# option 3: create the transects by manually providing the coordinates of two points
+#transects = dict([])
+#transects['Transect 1'] = np.array([[342836, 6269215], [343315, 6269071]])
+#transects['Transect 2'] = np.array([[342482, 6268466], [342958, 6268310]])
+#transects['Transect 3'] = np.array([[342185, 6267650], [342685, 6267641]])
+
# intersect the transects with the 2D shorelines to obtain time-series of cross-shore distance
settings['along_dist'] = 25
cross_distance = SDS_transects.compute_intersection(output, transects, settings)
# plot the time-series
from matplotlib import gridspec
-import numpy as np
fig = plt.figure()
gs = gridspec.GridSpec(len(cross_distance),1)
gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05)
diff --git a/requirements_linux64.txt b/requirements_linux64.txt
index 46630d2..f0a32bd 100644
--- a/requirements_linux64.txt
+++ b/requirements_linux64.txt
@@ -2,166 +2,214 @@
# $ conda create --name --file
# platform: linux-64
@EXPLICIT
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-3.0.0-1.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2018.03.07-0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2018.11.29-ha4d7672_0.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2019.1-144.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-3.0.0-1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/poppler-data-0.4.9-0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h470a237_2.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/expat-2.2.6-he6710b0_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/freexl-1.0.5-h470a237_2.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/geos-3.6.2-heeff764_2.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/giflib-5.1.4-h470a237_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/gmp-6.1.2-h6c8ec71_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/icu-58.2-h9c2bf20_1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h470a237_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/json-c-0.13.1-h1bed415_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.2.1-hd88cf55_4.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libgcc-7.2.0-h69d50b8_2.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libsodium-1.0.16-h1bed415_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h470a237_2.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.13-h1bed415_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/mkl-2018.0.3-1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.1-he6710b0_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/openssl-1.0.2p-h14c3975_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-7.3.0-hdf63c60_0.tar.bz2
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