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# CoastSat # CoastSat
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2779294.svg)](https://doi.org/10.5281/zenodo.2779294)
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-series of shoreline position at any coastline worldwide from 30+ years (and growing) of publicly available satellite imagery. CoastSat is an open-source software toolkit written in Python that enables users to obtain time-series of shoreline position at any coastline worldwide from 30+ years (and growing) of publicly available satellite imagery.
![Alt text](https://github.com/kvos/CoastSat/blob/development/classifiers/doc/example.gif?raw=true) ![Alt text](https://github.com/kvos/CoastSat/blob/development/examples/doc/example.gif)
The underlying approach and application of the CoastSat toolkit are described in detail in: The underlying approach and application of the CoastSat toolkit are described in detail 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*. *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: There are three main steps:
- assisted retrieval 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 available 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 - automated extraction of shorelines from all the selected images using a sub-pixel resolution technique
- intersection of the 2D shorelines with user-defined shore-normal transects
### Description ### Description
@ -21,82 +24,47 @@ The shoreline detection algorithm implemented in CoastSat is optimised for sandy
## 1. Installation ## 1. Installation
CoastSat requires the following Python packages to run: ### 1.1 Create an environment with Anaconda
```
conda-forge: python=3.6 | matplotlib | scikit-image | scikit-learn | gdal | earthengine-api | oauth2client | spyder | jupyter | simplekml
PyPi: shapely
```
If you are not a regular Python user and are not sure how to install these packages from *conda-forge* and *PyPi*, the section below explains how to install them step-by-step using Anaconda. More experinced Python users can proceed to install these packages and go directly to section **1.2 Activating Google Earth Engine Python API**.
### 1.1 Installing the packages (Anaconda) To run the toolbox you first need to install the required Python packages in an environment. To do this we will use **Anaconda**, which can be downloaded freely [here](https://www.anaconda.com/download/).
If Anaconda is not already installed on your PC, it can be freely downloaded at https://www.anaconda.com/download/. Once you have it installed on your PC, open the Anaconda prompt (in Mac and Linux, open a terminal window) and use the `cd` command (change directory) to go the folder where you have downloaded this repository.
Open the *Anaconda prompt* (in Mac and Linux, open a terminal window) and select the folder where you have downloaded/cloned this repository.
Create a new environment named *coastsat*: Create a new environment named `coastsat` with all the required packages:
``` ```
conda create -n coastsat conda env create -f environment.yml -n coastsat
``` ```
Activate the new environment: All the required packages have now been installed in an environment called `coastsat`. Now, activate the new environment:
``` ```
conda activate coastsat conda activate coastsat
``` ```
On Linux systems, type `source activate coastsat` instead. To confirm that you have successfully activated CoastSat, your terminal command line prompt should now start with (coastsat).
To confrim that you have successfully activated CoastSat, your terminal command line prompt should now start with (coastsat).
Now you need to populate the environment with the packages needed to run the CoastSat toolkit. All the necessary packages are contained in three platform specific files: `requirements_win64.txt`, `requirements_osx64.txt`, `requirements_linux64.txt`. To install the package for your pc platform, run one of the following commands, depending on which platform you are operating:
#### Windows 64 bits (win64) **In case errors are raised:**: you should create a new environment and manually install the required packages, which are listed in the environment.yml file. The following [link](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands) shows how to create and manage an environment with Anaconda.
``` ### 1.2 Activate Google Earth Engine Python API
conda install --name coastsat --file requirements_win64.txt
```
#### Mac 64 bits (osx64)
```
conda install --name coastsat --file requirements_osx64.txt
```
#### Linux 64 bits (linux64)
```
conda install --name coastsat --file requirements_linux64.txt
```
This might take a few minutes... once it is finished, run the following command:
```
pip install shapely
```
All the packages have now been install in an environment called `coastsat`.
### 1.2 Activating Google Earth Engine Python API
Go to https://earthengine.google.com and sign up to Google Earth Engine (GEE). Go to https://earthengine.google.com and sign up to Google Earth Engine (GEE).
![gee_capture](https://user-images.githubusercontent.com/7217258/49348457-a9271300-f6f9-11e8-8c0b-407383940e94.jpg) ![gee_capture](https://user-images.githubusercontent.com/7217258/49348457-a9271300-f6f9-11e8-8c0b-407383940e94.jpg)
Once you have created a Google Earth Engine account, go back to Anaconda and link your GEE credentials to the Python API: Once you have created a Google Earth Engine account, go back to the Anaconda prompt and link your GEE credentials to the Python API:
``` ```
earthengine authenticate earthengine authenticate
``` ```
A web browser will open. Login with your GEE credentials, read and accept the terms and conditions, and copy the authorization code into the Anaconda terminal. A web browser will open, login with your GEE credential and accept the terms and conditions. Then copy the authorization code into the Anaconda terminal.
Now you are ready to start using the CoastSat toolbox! Now you are ready to start using the CoastSat toolbox!
## 2. Usage **Note**: remember to always activate the environment with `conda activate coastsat` each time you are preparing to use the toolbox.
**Note**: remeber to always activate the `coastsat` environment with `conda activate coastsat` each time you are preparing to use it. ## 2. Usage
Your terminal command line prompt should always start with (coastsat) to confirm that it is activated.
An example of how to run the software in a Jupyter Notebook is provided in the repository (`example_jupyter.ipynb`). To run this, first activate your `coastsat` environment with `conda activate coastsat` (if not already active), and then type: An example of how to run the software in a Jupyter Notebook is provided in the repository (`example_jupyter.ipynb`). To run this, first activate your `coastsat` environment with `conda activate coastsat` (if not already active), and then type:
@ -104,66 +72,65 @@ An example of how to run the software in a Jupyter Notebook is provided in the r
jupyter notebook jupyter notebook
``` ```
A web browser window will open. Point to the directory where you downloaded/cloned this repository and click on `example_jupyter.ipynb`. A web browser window will open. Point to the directory where you downloaded 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 `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.
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**, **PyCharm** or other integrated development environments (IDEs), a Python script named `example.py` is also included in the repository.
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). If using `example.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 click on the `run` button up in the top menu to run that section and progress forward (as shown in the animation below).
![example_jupyter](https://user-images.githubusercontent.com/7217258/49705486-8dc88480-fc72-11e8-8300-c342baaf54eb.gif) ![example_jupyter](https://user-images.githubusercontent.com/7217258/49705486-8dc88480-fc72-11e8-8300-c342baaf54eb.gif)
### 2.1 Retrieval of the satellite images ### 2.1 Retrieval of the satellite images
To retrieve from the GEE server the avaiable satellite images cropped around the required region of coasltine for the particular time period of interest, the following user-defined variables are required: To retrieve from the GEE server the avaiable satellite images cropped around the user-defined region of coasltine for the particular time period of interest, the following variables are required:
- `polygon`: the coordinates of the region of interest (longitude/latitude pairs) - `polygon`: the coordinates of the region of interest (longitude/latitude pairs in WGS84)
- `dates`: dates over which the images will be retrieved (e.g., `dates = ['2017-12-01', '2018-01-01']`) - `dates`: dates over which the images will be retrieved (e.g., `dates = ['2017-12-01', '2018-01-01']`)
- `sat_list`: satellite missions to consider (e.g., `sat_list = ['L5', 'L7', 'L8', 'S2']` for Landsat 5, 7, 8 and Sentinel-2 collections) - `sat_list`: satellite missions to consider (e.g., `sat_list = ['L5', 'L7', 'L8', 'S2']` for Landsat 5, 7, 8 and Sentinel-2 collections)
- `sitename`: name of the site (user-defined name of the subfolder where the images and other accompanying files will be stored) - `sitename`: name of the site (this is the name of the subfolder where the images and other accompanying files will be stored)
- `filepath`: filepath to the directory where the data will be stored
The call `metadata = SDS_download.retrieve_images(inputs)` will launch the retrieval of the images and store them as .TIF files (under *.data\sitename*). The metadata contains the exact time of acquisition (UTC) and geometric accuracy of each downloaded image and is saved as `metadata_sitename.pkl`. If the images have already been downloaded previously and the user only wants to run the shoreline detection, the metadata can be loaded directly from this file. The screenshot below shows an example where all the images of Collaroy-Narrrabeen (Australia) acquired in December 2017 are retrieved. The call `metadata = SDS_download.retrieve_images(inputs)` will launch the retrieval of the images and store them as .TIF files (under *filepath\sitename*). The metadata contains the exact time of acquisition (in UTC time) and geometric accuracy of each downloaded image and is saved as `metadata_sitename.pkl`. If the images have already been downloaded previously and the user only wants to run the shoreline detection, the metadata can be loaded directly by running `metadata = SDS_download.get_metadata(inputs)`.
![retrieval](https://user-images.githubusercontent.com/7217258/49353105-0037e280-f710-11e8-9454-c03ce6116c54.PNG) The screenshot below shows an example of inputs that will retrieve all the images of Collaroy-Narrrabeen (Australia) acquired by Sentinel-2 in December 2017.
### 2.2 Shoreline detection ![doc1](https://user-images.githubusercontent.com/7217258/56278746-20f65700-614a-11e9-8715-ba5b8f938063.PNG)
It is now time to map the sandy shorelines! **Note:** The area of the polygon should not exceed 100 km2, so for very long beaches split it into multiple smaller polygons.
### 2.2 Shoreline detection
The following user-defined settings are required: To map the shorelines, the following user-defined settings are needed:
- `cloud_thresh`: threshold on maximum cloud cover that is acceptable on the images (value between 0 and 1 - this may require some initial experimentation) - `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. It has to be a cartesion coordinate system (i.e. projected) and not a geographical coordinate system (in latitude and longitude angles). - `output_epsg`: epsg code defining the spatial reference system of the shoreline coordinates. It has to be a cartesian coordinate system (i.e. projected) and not a geographical coordinate system (in latitude and longitude angles). See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system.
- `check_detection`: if set to `True` allows the user to quality control each shoreline detection - `check_detection`: if set to `True` the user can quality control each shoreline detection interactively.
- `save_figure`: if set to `True` a figure of each mapped shoreline is saved (under *filepath/sitename/jpg_files/detection*). Note that this may slow down the process.
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`. The setting `check_detection` is recommended when using the tool for the first time as it will show the user how CoastSat is mapping the shorelines.
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. There are additional parameters (`min_beach_size`, `buffer_size`, `min_length_sl`, `cloud_mask_issue` and `dark sand`) 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: An example of settings is provided here:
![settings_v2](https://user-images.githubusercontent.com/7217258/52684207-876bc700-2f99-11e9-9e5c-086f523bcdc2.PNG) ![doc2](https://user-images.githubusercontent.com/7217258/56278918-7a5e8600-614a-11e9-9184-77b69427b834.PNG)
Once all the settings have been defined, the batch shoreline detection can be launched by calling: Once all the settings have been defined, the batch shoreline detection can be launched by calling:
``` ```
output = SDS_shoreline.extract_shorelines(metadata, settings) output = SDS_shoreline.extract_shorelines(metadata, settings)
``` ```
When `check_detection` is set to `True`, a figure like the one below appears and asks the user to manually accept/reject each detection by clicking on `keep` or `skip`. When `check_detection` is set to `True`, a figure like the one below appears and asks the user to manually accept/reject each detection by pressing the `right arrow` (⇨) to `keep` the shoreline or `left arrow` (⇦) to `skip` the mapped shoreline. The user can break the loop at any time by pressing `escape` (nothing will be saved though).
![Alt text](https://github.com/kvos/CoastSat/blob/development/classifiers/doc/batch_detection.gif?raw=true) ![Alt text](https://github.com/kvos/CoastSat/blob/development/examples/doc/batch_detection.gif)
Once all the shorelines have been mapped, the output is available in two different formats (saved under *.\data\sitename*): Once all the shorelines have been mapped, the output is available in two different formats (saved under *.\data\sitename*):
- `sitename_output.pkl`: contains a list with the shoreline coordinates and the exact timestamp at which the image was captured (UTC time) as well as the geometric accuracy and the cloud cover of each indivdual image. This list can be manipulated with Python, a snippet of code to plot the results is provided in the main script. - `sitename_output.pkl`: contains a list with the shoreline coordinates, the exact timestamp at which the image was captured (UTC time), the geometric accuracy and the cloud cover of each individual image. This list can be manipulated with Python, a snippet of code to plot the results is provided in the example script.
- `sitename_output.kml`: this output can be visualised in a GIS software (e.g., QGIS, ArcGIS). - `sitename_output.geojson`: this output can be visualised in a GIS software (e.g., QGIS, ArcGIS).
The figure below shows how the satellite-derived shorelines can be opened in a GIS software (QGIS) using the `.kml` output. The figure below shows how the satellite-derived shorelines can be opened in a GIS software (QGIS) using the `.geojson` output. Note that the coordinates in the `.geojson` file are in the spatial reference system defined by the `output_epsg`.
![gis_output](https://user-images.githubusercontent.com/7217258/49361401-15bd0480-f730-11e8-88a8-a127f87ca64a.jpeg) ![gis_output](https://user-images.githubusercontent.com/7217258/49361401-15bd0480-f730-11e8-88a8-a127f87ca64a.jpeg)
#### Advanced shoreline detection parameters
As mentioned above, there are some additional parameters that can be modified to optimise the shoreline detection:
- `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 #### Reference shoreline
There is also an option to manually digitize a reference shoreline before running the batch shoreline detection on all the images. This reference shoreline helps to reject outliers and false detections when mapping shorelines as it only considers as valid shorelines the points that are within a distance from this reference shoreline. There is also an option to manually digitize a reference shoreline before running the batch shoreline detection on all the images. This reference shoreline helps to reject outliers and false detections when mapping shorelines as it only considers as valid shorelines the points that are within a distance from this reference shoreline.
@ -177,18 +144,27 @@ This function allows the user to click points along the shoreline on one of the
![ref_shoreline](https://user-images.githubusercontent.com/7217258/49710753-94b1c000-fc8f-11e8-9b6c-b5e96aadc5c9.gif) ![ref_shoreline](https://user-images.githubusercontent.com/7217258/49710753-94b1c000-fc8f-11e8-9b6c-b5e96aadc5c9.gif)
The maximum distance (in metres) allowed from the reference shoreline is defined by the parameter `max_dist_ref`. This parameter is set to a default value of 100 m. If you think that your shoreline will move more than 100 m, please change this parameter to an appropriate distance. This may be the case for large nourishments or eroding/accreting coastlines. The maximum distance (in metres) allowed from the reference shoreline is defined by the parameter `max_dist_ref`. This parameter is set to a default value of 100 m. If you think that 100m buffer from the reference shoreline will not capture the shoreline variability at your site, increase the value of this parameter. This may be the case for large nourishments or eroding/accreting coastlines.
#### Advanced shoreline detection parameters
As mentioned above, there are some additional parameters that can be modified to optimise the shoreline detection:
- `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 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 to calculate the sand/water threshold. 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 actual 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 and appear as masked 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`.
- `dark_sand`: if your beach has dark sand (grey/black sand beaches), you can set this parameter to `True` and the classifier will be able to pick up the dark sand. At this stage this option is only available for Landsat images (soon for Sentinel-2 as well).
### 2.3 Shoreline change analysis ### 2.3 Shoreline change analysis
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: 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 length and orientation. There are 3 options to define the coordinates of the transects:
1. The user can interactively draw shore-normal transects along the beach: 1. Interactively draw shore-normal transects along the mapped shorelines:
``` ```
transects = SDS_transects.draw_transects(output, settings) transects = SDS_transects.draw_transects(output, settings)
``` ```
2. Load the transect coordinates from a KML file: 2. Load the transect coordinates from a .geojson file:
``` ```
transects = SDS_transects.load_transects_from_kml('transects.kml') transects = SDS_tools.transects_from_geojson(path_to_geojson_file)
``` ```
3. Create the transects by manually providing the coordinates of two points: 3. Create the transects by manually providing the coordinates of two points:
``` ```
@ -205,13 +181,22 @@ Once the shore-normal transects have been defined, the intersection between the
settings['along_dist'] = 25 settings['along_dist'] = 25
cross_distance = SDS_transects.compute_intersection(output, transects, settings) cross_distance = SDS_transects.compute_intersection(output, transects, settings)
``` ```
The parameter `along_dist` defines the along-shore distance around the transect over which shoreline points are selected to compute the intersection. The default value is 25 m, which means that the intersection is computed as the median of the points located within 25 m of the transect (50 m alongshore-median). The parameter `along_dist` defines the along-shore distance around the transect over which shoreline points are selected to compute the intersection. The default value is 25 m, which means that the intersection is computed as the median of the points located within 25 m of the transect (50 m alongshore-median). This helps to smooth out localised water levels in the swash zone.
An example is illustrated below: An example is shown in the animation below:
![transects](https://user-images.githubusercontent.com/7217258/49990925-8b985a00-ffd3-11e8-8c54-57e4bf8082dd.gif) ![transects](https://user-images.githubusercontent.com/7217258/49990925-8b985a00-ffd3-11e8-8c54-57e4bf8082dd.gif)
## Issues
Having a problem? Post an issue in the [Issues page](https://github.com/kvos/coastsat/issues) (please do not email).
## Contributing
1. Fork the repository (https://github.com/kvos/coastsat/fork).
A fork is a copy on which you can make your changes.
2. Create a new branch on your fork
3. Commit your changes and push them to your branch
4. When the branch is ready to be merged, create a Pull Request
## Issues and Contributions Check the following link for more information on how to make a clean pull request: https://gist.github.com/MarcDiethelm/7303312).
Having a problem or looking to contribute to the code? Please see the [Issues page](https://github.com/kvos/coastsat/issues). If you like the repo put a star on it!

372
SDS_download.py → coastsat/SDS_download.py
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@ -15,7 +15,7 @@ import ee
from urllib.request import urlretrieve from urllib.request import urlretrieve
import zipfile import zipfile
import copy import copy
import gdal_merge from coastsat import gdal_merge
# additional modules # additional modules
from datetime import datetime from datetime import datetime
@ -24,7 +24,7 @@ import pickle
import skimage.morphology as morphology import skimage.morphology as morphology
# own modules # own modules
import SDS_preprocess, SDS_tools from coastsat import SDS_preprocess, SDS_tools
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
@ -77,19 +77,25 @@ def retrieve_images(inputs):
'sitename': str 'sitename': str
String containig the name of the site String containig the name of the site
'polygon': list 'polygon': list
polygon containing the lon/lat coordinates to be extracted polygon containing the lon/lat coordinates to be extracted,
longitudes in the first column and latitudes in the second column 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 'dates': list of str
list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd' list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd'
e.g. ['1987-01-01', '2018-01-01'] e.g. ['1987-01-01', '2018-01-01']
'sat_list': list of str 'sat_list': list of str
list that contains the names of the satellite missions to include list that contains the names of the satellite missions to include
e.g. ['L5', 'L7', 'L8', 'S2'] e.g. ['L5', 'L7', 'L8', 'S2']
'filepath_data': str
Filepath to the directory where the images are downloaded
Returns: Returns:
----------- -----------
metadata: dict metadata: dict
contains all the information about the satellite images that were downloaded contains the information about the satellite images that were downloaded: filename,
georeferencing accuracy and image coordinate reference system
""" """
@ -98,19 +104,20 @@ def retrieve_images(inputs):
polygon = inputs['polygon'] polygon = inputs['polygon']
dates = inputs['dates'] dates = inputs['dates']
sat_list= inputs['sat_list'] sat_list= inputs['sat_list']
filepath_data = inputs['filepath']
# format in which the images are downloaded # format in which the images are downloaded
suffix = '.tif' suffix = '.tif'
# initialize metadata dictionnary (stores timestamps and georefencing accuracy of each image) # initialize metadata dictionnary (stores information about each image)
metadata = dict([]) metadata = dict([])
# create directories # create a new directory for this site
try: if not os.path.exists(os.path.join(filepath_data,sitename)):
os.makedirs(os.path.join(os.getcwd(), 'data',sitename)) os.makedirs(os.path.join(filepath_data,sitename))
except:
print('')
print('Downloading images:')
#=============================================================================================# #=============================================================================================#
# download L5 images # download L5 images
#=============================================================================================# #=============================================================================================#
@ -119,12 +126,13 @@ def retrieve_images(inputs):
satname = 'L5' satname = 'L5'
# create a subfolder to store L5 images # create a subfolder to store L5 images
filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m') filepath = os.path.join(filepath_data, sitename, satname, '30m')
try: filepath_meta = os.path.join(filepath_data, sitename, satname, 'meta')
if not os.path.exists(filepath):
os.makedirs(filepath) os.makedirs(filepath)
except: if not os.path.exists(filepath_meta):
print('') os.makedirs(filepath_meta)
# Landsat 5 collection # Landsat 5 collection
input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA') input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
# filter by location and dates # filter by location and dates
@ -135,12 +143,12 @@ def retrieve_images(inputs):
cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all]
if np.any([_ > 95 for _ in cloud_cover]): if np.any([_ > 95 for _ in cloud_cover]):
idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] 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] im_col = [x for k,x in enumerate(im_all) if k not in idx_delete]
else: else:
im_all_cloud = im_all im_col = im_all
n_img = len(im_all_cloud) n_img = len(im_col)
# print how many images there are # print how many images there are
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) print('%s: %d images'%(satname,n_img))
# loop trough images # loop trough images
timestamps = [] timestamps = []
@ -151,11 +159,11 @@ def retrieve_images(inputs):
for i in range(n_img): for i in range(n_img):
# find each image in ee database # find each image in ee database
im = ee.Image(im_all_cloud[i].get('id')) im = ee.Image(im_col[i]['id'])
# read metadata # read metadata
im_dic = im.getInfo() im_dic = im_col[i]
# get bands # get bands
im_bands = im_dic.get('bands') im_bands = im_dic['bands']
# get time of acquisition (UNIX time) # get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start'] t = im_dic['properties']['system:time_start']
# convert to datetime # convert to datetime
@ -165,11 +173,10 @@ def retrieve_images(inputs):
# get EPSG code of reference system # get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy # get geometric accuracy
try: if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys():
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except: else:
# default value of accuracy (RMSE = 12m) acc_georef.append(12) # default value of accuracy (RMSE = 12m)
acc_georef.append(12)
# delete dimensions key from dictionnary, otherwise the entire image is extracted # delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions'] for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L5 # bands for L5
@ -189,10 +196,18 @@ def retrieve_images(inputs):
except: except:
os.remove(os.path.join(filepath, filename)) os.remove(os.path.join(filepath, filename))
os.rename(local_data, 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(i+1, end='..') print('\r%d%%' % (int(((i+1)/n_img)*100)), end='')
print('')
# sort timestamps and georef accuracy (downloaded images are sorted by date in directory) # sort metadata (downloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps) timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__) idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted] acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
@ -200,10 +215,7 @@ def retrieve_images(inputs):
im_epsg_sorted = [im_epsg[j] for j in idx_sorted] im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
# save into dict # save into dict
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted, 'filenames':filenames_sorted} 'epsg':im_epsg_sorted, 'filenames':filenames_sorted}
print('\nFinished with ' + satname)
#=============================================================================================# #=============================================================================================#
# download L7 images # download L7 images
@ -213,15 +225,17 @@ def retrieve_images(inputs):
satname = 'L7' satname = 'L7'
# create subfolders (one for 30m multispectral bands and one for 15m pan bands) # create subfolders (one for 30m multispectral bands and one for 15m pan bands)
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L7') filepath = os.path.join(filepath_data, sitename, 'L7')
filepath_pan = os.path.join(filepath, 'pan') filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms') filepath_ms = os.path.join(filepath, 'ms')
try: filepath_meta = os.path.join(filepath, 'meta')
if not os.path.exists(filepath_pan):
os.makedirs(filepath_pan) os.makedirs(filepath_pan)
if not os.path.exists(filepath_ms):
os.makedirs(filepath_ms) os.makedirs(filepath_ms)
except: if not os.path.exists(filepath_meta):
print('') os.makedirs(filepath_meta)
# landsat 7 collection # landsat 7 collection
input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA') input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA')
# filter by location and dates # filter by location and dates
@ -232,12 +246,12 @@ def retrieve_images(inputs):
cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all]
if np.any([_ > 95 for _ in cloud_cover]): if np.any([_ > 95 for _ in cloud_cover]):
idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] 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] im_col = [x for k,x in enumerate(im_all) if k not in idx_delete]
else: else:
im_all_cloud = im_all im_col = im_all
n_img = len(im_all_cloud) n_img = len(im_col)
# print how many images there are # print how many images there are
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) print('%s: %d images'%(satname,n_img))
# loop trough images # loop trough images
timestamps = [] timestamps = []
@ -248,11 +262,11 @@ def retrieve_images(inputs):
for i in range(n_img): for i in range(n_img):
# find each image in ee database # find each image in ee database
im = ee.Image(im_all_cloud[i].get('id')) im = ee.Image(im_col[i]['id'])
# read metadata # read metadata
im_dic = im.getInfo() im_dic = im_col[i]
# get bands # get bands
im_bands = im_dic.get('bands') im_bands = im_dic['bands']
# get time of acquisition (UNIX time) # get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start'] t = im_dic['properties']['system:time_start']
# convert to datetime # convert to datetime
@ -262,11 +276,10 @@ def retrieve_images(inputs):
# get EPSG code of reference system # get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy # get geometric accuracy
try: if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys():
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except: else:
# default value of accuracy (RMSE = 12m) acc_georef.append(12) # default value of accuracy (RMSE = 12m)
acc_georef.append(12)
# delete dimensions key from dictionnary, otherwise the entire image is extracted # delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions'] for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L7 # bands for L7
@ -295,10 +308,18 @@ def retrieve_images(inputs):
except: except:
os.remove(os.path.join(filepath_ms, filename_ms)) os.remove(os.path.join(filepath_ms, filename_ms))
os.rename(local_data_ms, 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('')
print(i+1, end='..') # sort metadata (dowloaded images are sorted by date in directory)
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps) timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__) idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted] acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
@ -306,9 +327,7 @@ def retrieve_images(inputs):
im_epsg_sorted = [im_epsg[j] for j in idx_sorted] im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
# save into dict # save into dict
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted, 'filenames':filenames_sorted} 'epsg':im_epsg_sorted, 'filenames':filenames_sorted}
print('\nFinished with ' + satname)
#=============================================================================================# #=============================================================================================#
# download L8 images # download L8 images
@ -318,14 +337,16 @@ def retrieve_images(inputs):
satname = 'L8' satname = 'L8'
# create subfolders (one for 30m multispectral bands and one for 15m pan bands) # create subfolders (one for 30m multispectral bands and one for 15m pan bands)
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L8') filepath = os.path.join(filepath_data, sitename, 'L8')
filepath_pan = os.path.join(filepath, 'pan') filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms') filepath_ms = os.path.join(filepath, 'ms')
try: filepath_meta = os.path.join(filepath, 'meta')
if not os.path.exists(filepath_pan):
os.makedirs(filepath_pan) os.makedirs(filepath_pan)
if not os.path.exists(filepath_ms):
os.makedirs(filepath_ms) os.makedirs(filepath_ms)
except: if not os.path.exists(filepath_meta):
print('') os.makedirs(filepath_meta)
# landsat 8 collection # landsat 8 collection
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
@ -337,12 +358,12 @@ def retrieve_images(inputs):
cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all] cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all]
if np.any([_ > 95 for _ in cloud_cover]): if np.any([_ > 95 for _ in cloud_cover]):
idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] 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] im_col = [x for k,x in enumerate(im_all) if k not in idx_delete]
else: else:
im_all_cloud = im_all im_col = im_all
n_img = len(im_all_cloud) n_img = len(im_col)
# print how many images there are # print how many images there are
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) print('%s: %d images'%(satname,n_img))
# loop trough images # loop trough images
timestamps = [] timestamps = []
@ -353,11 +374,11 @@ def retrieve_images(inputs):
for i in range(n_img): for i in range(n_img):
# find each image in ee database # find each image in ee database
im = ee.Image(im_all_cloud[i].get('id')) im = ee.Image(im_col[i]['id'])
# read metadata # read metadata
im_dic = im.getInfo() im_dic = im_col[i]
# get bands # get bands
im_bands = im_dic.get('bands') im_bands = im_dic['bands']
# get time of acquisition (UNIX time) # get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start'] t = im_dic['properties']['system:time_start']
# convert to datetime # convert to datetime
@ -367,11 +388,10 @@ def retrieve_images(inputs):
# get EPSG code of reference system # get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy # get geometric accuracy
try: if 'GEOMETRIC_RMSE_MODEL' in im_dic['properties'].keys():
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL']) acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except: else:
# default value of accuracy (RMSE = 12m) acc_georef.append(12) # default value of accuracy (RMSE = 12m)
acc_georef.append(12)
# delete dimensions key from dictionnary, otherwise the entire image is extracted # delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions'] for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L8 # bands for L8
@ -400,10 +420,18 @@ def retrieve_images(inputs):
except: except:
os.remove(os.path.join(filepath_ms, filename_ms)) os.remove(os.path.join(filepath_ms, filename_ms))
os.rename(local_data_ms, 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(i+1, end='..') print('\r%d%%' % (int(((i+1)/n_img)*100)), end='')
print('')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory) # sort metadata (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps) timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__) idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted] acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
@ -412,7 +440,6 @@ def retrieve_images(inputs):
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted, 'filenames':filenames_sorted} 'epsg':im_epsg_sorted, 'filenames':filenames_sorted}
print('\nFinished with ' + satname)
#=============================================================================================# #=============================================================================================#
# download S2 images # download S2 images
@ -422,14 +449,17 @@ def retrieve_images(inputs):
satname = 'S2' satname = 'S2'
# create subfolders for the 10m, 20m and 60m multipectral bands # create subfolders for the 10m, 20m and 60m multipectral bands
filepath = os.path.join(os.getcwd(), 'data', sitename, 'S2') filepath = os.path.join(filepath_data, sitename, 'S2')
try: if not os.path.exists(os.path.join(filepath, '10m')):
os.makedirs(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')) os.makedirs(os.path.join(filepath, '20m'))
if not os.path.exists(os.path.join(filepath, '60m')):
os.makedirs(os.path.join(filepath, '60m')) os.makedirs(os.path.join(filepath, '60m'))
except: filepath_meta = os.path.join(filepath, 'meta')
print('') if not os.path.exists(filepath_meta):
os.makedirs(filepath_meta)
# Sentinel2 collection # Sentinel2 collection
input_col = ee.ImageCollection('COPERNICUS/S2') input_col = ee.ImageCollection('COPERNICUS/S2')
# filter by location and dates # filter by location and dates
@ -471,13 +501,13 @@ def retrieve_images(inputs):
cloud_cover = [_['properties']['CLOUDY_PIXEL_PERCENTAGE'] for _ in im_all_updated] cloud_cover = [_['properties']['CLOUDY_PIXEL_PERCENTAGE'] for _ in im_all_updated]
if np.any([_ > 95 for _ in cloud_cover]): if np.any([_ > 95 for _ in cloud_cover]):
idx_delete = np.where([_ > 95 for _ in cloud_cover])[0] 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] im_col = [x for k,x in enumerate(im_all_updated) if k not in idx_delete]
else: else:
im_all_cloud = im_all_updated im_col = im_all_updated
n_img = len(im_all_cloud) n_img = len(im_col)
# print how many images there are # print how many images there are
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img) print('%s: %d images'%(satname,n_img))
# loop trough images # loop trough images
timestamps = [] timestamps = []
@ -488,11 +518,11 @@ def retrieve_images(inputs):
for i in range(n_img): for i in range(n_img):
# find each image in ee database # find each image in ee database
im = ee.Image(im_all_cloud[i].get('id')) im = ee.Image(im_col[i]['id'])
# read metadata # read metadata
im_dic = im.getInfo() im_dic = im_col[i]
# get bands # get bands
im_bands = im_dic.get('bands') im_bands = im_dic['bands']
# get time of acquisition (UNIX time) # get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start'] t = im_dic['properties']['system:time_start']
# convert to datetime # convert to datetime
@ -537,7 +567,7 @@ def retrieve_images(inputs):
except: except:
os.remove(os.path.join(filepath, '60m', filename60)) os.remove(os.path.join(filepath, '60m', filename60))
os.rename(local_data, 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) # save timestamp, epsg code and georeferencing accuracy (1 if passed 0 if not passed)
timestamps.append(im_timestamp) timestamps.append(im_timestamp)
im_epsg.append(int(im_dic['bands'][0]['crs'][5:])) im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
@ -556,10 +586,17 @@ def retrieve_images(inputs):
acc_georef.append(-1) acc_georef.append(-1)
else: else:
acc_georef.append(-1) 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(i+1, end='..') print('\r%d%%' % (int(((i+1)/n_img)*100)), end='')
print('')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory) # sort metadata (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps) timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__) idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted] acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
@ -568,14 +605,13 @@ def retrieve_images(inputs):
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted, 'filenames':filenames_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) # merge overlapping images (necessary only if the polygon is at the boundary of an image)
if 'S2' in metadata.keys(): if 'S2' in metadata.keys():
metadata = merge_overlapping_images(metadata,inputs) metadata = merge_overlapping_images(metadata,inputs)
# save metadata dict # save metadata dict
filepath = os.path.join(os.getcwd(), 'data', sitename) filepath = os.path.join(filepath_data, sitename)
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'wb') as f: with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'wb') as f:
pickle.dump(metadata, f) pickle.dump(metadata, f)
@ -599,30 +635,34 @@ def merge_overlapping_images(metadata,inputs):
'sitename': str 'sitename': str
String containig the name of the site String containig the name of the site
'polygon': list 'polygon': list
polygon containing the lon/lat coordinates to be extracted polygon containing the lon/lat coordinates to be extracted,
longitudes in the first column and latitudes in the second column 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 'dates': list of str
list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd' list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd'
e.g. ['1987-01-01', '2018-01-01'] e.g. ['1987-01-01', '2018-01-01']
'sat_list': list of str 'sat_list': list of str
list that contains the names of the satellite missions to include list that contains the names of the satellite missions to include
e.g. ['L5', 'L7', 'L8', 'S2'] e.g. ['L5', 'L7', 'L8', 'S2']
'filepath_data': str
Filepath to the directory where the images are downloaded
Returns: Returns:
----------- -----------
metadata: dict metadata_updated: dict
updated metadata with the information of the merged images updated metadata with the information of the merged images
""" """
# only for Sentinel-2 at this stage (could be implemented for Landsat as well) # only for Sentinel-2 at this stage (not sure if this is needed for Landsat images)
sat = 'S2' sat = 'S2'
filepath = os.path.join(os.getcwd(), 'data', inputs['sitename']) filepath = os.path.join(inputs['filepath'], inputs['sitename'])
# find the images that are overlapping (same date in S2 filenames) # find the images that are overlapping (same date in S2 filenames)
filenames = metadata[sat]['filenames'] filenames = metadata[sat]['filenames']
filenames_copy = filenames.copy() filenames_copy = filenames.copy()
# loop through all the filenames and find the pairs of overlapping images (same date and time of acquisition) # loop through all the filenames and find the pairs of overlapping images (same date and time of acquisition)
pairs = [] pairs = []
for i,fn in enumerate(filenames): for i,fn in enumerate(filenames):
@ -635,21 +675,18 @@ def merge_overlapping_images(metadata,inputs):
pairs.append([idx_dup,i]) pairs.append([idx_dup,i])
else: else:
pairs.append([i,idx_dup]) 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 each pair of images, merge them into one complete image
for i,pair in enumerate(pairs): for i,pair in enumerate(pairs):
print(i+1, end='..')
fn_im = [] fn_im = []
for index in range(len(pair)): for index in range(len(pair)):
# read image # read image
fn_im.append([os.path.join(filepath, 'S2', '10m', filenames[pair[index]]), 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', '20m', filenames[pair[index]].replace('10m','20m')),
os.path.join(filepath, 'S2', '60m', filenames[pair[index]].replace('10m','60m'))]) 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) 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, # 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 # that are squares with constant pixel intensities. They need to be masked in the
@ -715,91 +752,84 @@ def merge_overlapping_images(metadata,inputs):
os.remove(fn_im[0][2]) os.remove(fn_im[0][2])
os.chmod(fn_im[1][2], 0o777) os.chmod(fn_im[1][2], 0o777)
os.remove(fn_im[1][2]) os.remove(fn_im[1][2])
os.rename(fn_merged, fn_im[0][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) # update the metadata dict (delete all the duplicates)
metadata2 = copy.deepcopy(metadata) metadata_updated = copy.deepcopy(metadata)
filenames_copy = metadata2[sat]['filenames'] filenames_copy = metadata_updated[sat]['filenames']
index_list = [] index_list = []
for i in range(len(filenames_copy)): for i in range(len(filenames_copy)):
if filenames_copy[i].find('dup') == -1: if filenames_copy[i].find('dup') == -1:
index_list.append(i) index_list.append(i)
for key in metadata2[sat].keys(): for key in metadata_updated[sat].keys():
metadata2[sat][key] = [metadata2[sat][key][_] for _ in index_list] metadata_updated[sat][key] = [metadata_updated[sat][key][_] for _ in index_list]
return metadata2 return metadata_updated
def remove_cloudy_images(metadata,inputs,cloud_thresh): def get_metadata(inputs):
""" """
Deletes the .TIF file of images that have a cloud cover percentage that is above the cloud Gets the metadata from the downloaded .txt files in the \meta folders.
threshold.
KV WRL 2018 KV WRL 2018
Arguments: Arguments:
----------- -----------
metadata: dict
contains all the information about the satellite images that were downloaded
inputs: dict inputs: dict
dictionnary that contains the following fields: dictionnary that contains the following fields:
'sitename': str 'sitename': str
String containig the name of the site String containig the name of the site
'polygon': list 'filepath_data': str
polygon containing the lon/lat coordinates to be extracted Filepath to the directory where the images are downloaded
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: Returns:
----------- -----------
metadata: dict metadata: dict
updated metadata with the information of the merged images 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)
# create a deep copy return metadata
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

155
SDS_preprocess.py → coastsat/SDS_preprocess.py
Unescape Escape View File

@ -18,17 +18,18 @@ import sklearn.decomposition as decomposition
import skimage.exposure as exposure import skimage.exposure as exposure
# other modules # other modules
from osgeo import gdal, ogr, osr from osgeo import gdal
from pylab import ginput from pylab import ginput
import pickle import pickle
import matplotlib.path as mpltPath import geopandas as gpd
from shapely import geometry
# own modules # own modules
import SDS_tools from coastsat import SDS_tools
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
def create_cloud_mask(im_qa, satname, cloud_mask_issue): def create_cloud_mask(im_QA, satname, cloud_mask_issue):
""" """
Creates a cloud mask using the information contained in the QA band. Creates a cloud mask using the information contained in the QA band.
@ -36,7 +37,7 @@ def create_cloud_mask(im_qa, satname, cloud_mask_issue):
Arguments: Arguments:
----------- -----------
im_qa: np.array im_QA: np.array
Image containing the QA band Image containing the QA band
satname: string satname: string
short name for the satellite (L5, L7, L8 or S2) short name for the satellite (L5, L7, L8 or S2)
@ -58,12 +59,13 @@ def create_cloud_mask(im_qa, satname, cloud_mask_issue):
cloud_values = [1024, 2048] # 1024 = dense cloud, 2048 = cirrus clouds cloud_values = [1024, 2048] # 1024 = dense cloud, 2048 = cirrus clouds
# find which pixels have bits corresponding to cloud values # find which pixels have bits corresponding to cloud values
cloud_mask = np.isin(im_qa, cloud_values) cloud_mask = np.isin(im_QA, cloud_values)
# remove cloud pixels that form very thin features. These are beach or swash pixels that are # 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. # 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: 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) morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
if cloud_mask_issue: if cloud_mask_issue:
elem = morphology.square(3) # use a square of width 3 pixels elem = morphology.square(3) # use a square of width 3 pixels
cloud_mask = morphology.binary_opening(cloud_mask,elem) # perform image opening cloud_mask = morphology.binary_opening(cloud_mask,elem) # perform image opening
@ -249,8 +251,10 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_extra : np.array im_extra : np.array
2D array containing the 20m resolution SWIR band for Sentinel-2 and the 15m resolution 2D array containing the 20m resolution SWIR band for Sentinel-2 and the 15m resolution
panchromatic band for Landsat 7 and Landsat 8. This field is empty for Landsat 5. panchromatic band for Landsat 7 and Landsat 8. This field is empty for Landsat 5.
imQA: np.array im_QA: np.array
2D array containing the QA band, from which the cloud_mask can be computed. 2D array containing the QA band, from which the cloud_mask can be computed.
im_nodata: np.array
2D array with True where no data values (-inf) are located
""" """
@ -270,9 +274,9 @@ def preprocess_single(fn, satname, cloud_mask_issue):
ncols = im_ms.shape[1]*2 ncols = im_ms.shape[1]*2
# create cloud mask # create cloud mask
im_qa = im_ms[:,:,5] im_QA = im_ms[:,:,5]
im_ms = im_ms[:,:,:-1] im_ms = im_ms[:,:,:-1]
cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue) cloud_mask = create_cloud_mask(im_QA, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1) # resize the image using bilinear interpolation (order 1)
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True, im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True,
@ -289,16 +293,14 @@ def preprocess_single(fn, satname, cloud_mask_issue):
georef[3] = georef[3] - 7.5 georef[3] = georef[3] - 7.5
# check if -inf or nan values on any band and add to cloud mask # check if -inf or nan values on any band and add to cloud mask
im_nodata = np.zeros(cloud_mask.shape).astype(bool)
for k in range(im_ms.shape[2]): for k in range(im_ms.shape[2]):
im_inf = np.isin(im_ms[:,:,k], -np.inf) im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k]) im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
im_nodata = np.logical_or(im_nodata, im_inf)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# no extra image for Landsat 5 (they are all 30 m bands) # no extra image for Landsat 5 (they are all 30 m bands)
im_extra = [] im_extra = []
imQA = im_qa
#=============================================================================================# #=============================================================================================#
# L7 images # L7 images
@ -323,8 +325,8 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_ms = np.stack(bands, 2) im_ms = np.stack(bands, 2)
# create cloud mask # create cloud mask
im_qa = im_ms[:,:,5] im_QA = im_ms[:,:,5]
cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue) cloud_mask = create_cloud_mask(im_QA, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1) # resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5] im_ms = im_ms[:,:,:5]
@ -334,6 +336,7 @@ def preprocess_single(fn, satname, cloud_mask_issue):
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True,
mode='constant').astype('bool_') mode='constant').astype('bool_')
# check if -inf or nan values on any band and eventually add those pixels to cloud mask # check if -inf or nan values on any band and eventually add those pixels to cloud mask
im_nodata = np.zeros(cloud_mask.shape).astype(bool)
for k in range(im_ms.shape[2]+1): for k in range(im_ms.shape[2]+1):
if k == 5: if k == 5:
im_inf = np.isin(im_pan, -np.inf) im_inf = np.isin(im_pan, -np.inf)
@ -342,10 +345,8 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_inf = np.isin(im_ms[:,:,k], -np.inf) im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k]) im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
im_nodata = np.logical_or(im_nodata, im_inf)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# pansharpen Green, Red, NIR (where there is overlapping with pan band in L7) # pansharpen Green, Red, NIR (where there is overlapping with pan band in L7)
try: try:
im_ms_ps = pansharpen(im_ms[:,:,[1,2,3]], im_pan, cloud_mask) im_ms_ps = pansharpen(im_ms[:,:,[1,2,3]], im_pan, cloud_mask)
@ -358,7 +359,6 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_ms = im_ms_ps.copy() im_ms = im_ms_ps.copy()
# the extra image is the 15m panchromatic band # the extra image is the 15m panchromatic band
im_extra = im_pan im_extra = im_pan
imQA = im_qa
#=============================================================================================# #=============================================================================================#
# L8 images # L8 images
@ -383,8 +383,8 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_ms = np.stack(bands, 2) im_ms = np.stack(bands, 2)
# create cloud mask # create cloud mask
im_qa = im_ms[:,:,5] im_QA = im_ms[:,:,5]
cloud_mask = create_cloud_mask(im_qa, satname, cloud_mask_issue) cloud_mask = create_cloud_mask(im_QA, satname, cloud_mask_issue)
# resize the image using bilinear interpolation (order 1) # resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5] im_ms = im_ms[:,:,:5]
@ -394,6 +394,7 @@ def preprocess_single(fn, satname, cloud_mask_issue):
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True,
mode='constant').astype('bool_') mode='constant').astype('bool_')
# check if -inf or nan values on any band and eventually add those pixels to cloud mask # check if -inf or nan values on any band and eventually add those pixels to cloud mask
im_nodata = np.zeros(cloud_mask.shape).astype(bool)
for k in range(im_ms.shape[2]+1): for k in range(im_ms.shape[2]+1):
if k == 5: if k == 5:
im_inf = np.isin(im_pan, -np.inf) im_inf = np.isin(im_pan, -np.inf)
@ -402,9 +403,7 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_inf = np.isin(im_ms[:,:,k], -np.inf) im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k]) im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
im_nodata = np.logical_or(im_nodata, im_inf)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# pansharpen Blue, Green, Red (where there is overlapping with pan band in L8) # pansharpen Blue, Green, Red (where there is overlapping with pan band in L8)
try: try:
@ -417,7 +416,6 @@ def preprocess_single(fn, satname, cloud_mask_issue):
im_ms = im_ms_ps.copy() im_ms = im_ms_ps.copy()
# the extra image is the 15m panchromatic band # the extra image is the 15m panchromatic band
im_extra = im_pan im_extra = im_pan
imQA = im_qa
#=============================================================================================# #=============================================================================================#
# S2 images # S2 images
@ -438,7 +436,7 @@ def preprocess_single(fn, satname, cloud_mask_issue):
georef = [] georef = []
# skip the image by giving it a full cloud_mask # skip the image by giving it a full cloud_mask
cloud_mask = np.ones((im10.shape[0],im10.shape[1])).astype('bool') cloud_mask = np.ones((im10.shape[0],im10.shape[1])).astype('bool')
return im_ms, georef, cloud_mask, [], [] return im_ms, georef, cloud_mask, [], [], []
# size of 10m bands # size of 10m bands
nrows = im10.shape[0] nrows = im10.shape[0]
@ -465,28 +463,29 @@ def preprocess_single(fn, satname, cloud_mask_issue):
data = gdal.Open(fn60, gdal.GA_ReadOnly) data = gdal.Open(fn60, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)] bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im60 = np.stack(bands, 2) im60 = np.stack(bands, 2)
imQA = im60[:,:,0] im_QA = im60[:,:,0]
cloud_mask = create_cloud_mask(imQA, satname, cloud_mask_issue) cloud_mask = create_cloud_mask(im_QA, satname, cloud_mask_issue)
# resize the cloud mask using nearest neighbour interpolation (order 0) # resize the cloud mask using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True, cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True,
mode='constant') mode='constant')
# check if -inf or nan values on any band and add to cloud mask # check if -inf or nan values on any band and add to cloud mask
im_nodata = np.zeros(cloud_mask.shape).astype(bool)
for k in range(im_ms.shape[2]): for k in range(im_ms.shape[2]):
im_inf = np.isin(im_ms[:,:,k], -np.inf) im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k]) im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
im_nodata = np.logical_or(im_nodata, im_inf)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# the extra image is the 20m SWIR band # the extra image is the 20m SWIR band
im_extra = im20 im_extra = im20
return im_ms, georef, cloud_mask, im_extra, imQA return im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata
def create_jpg(im_ms, cloud_mask, date, satname, filepath): def create_jpg(im_ms, cloud_mask, date, satname, filepath):
""" """
Saves a .jpg file with the RGB image as well as the NIR and SWIR1 grayscale images. Saves a .jpg file with the RGB image as well as the NIR and SWIR1 grayscale images.
This functions can be modified to obtain different visualisations of the multispectral images.
KV WRL 2018 KV WRL 2018
@ -509,10 +508,10 @@ def create_jpg(im_ms, cloud_mask, date, satname, filepath):
# rescale image intensity for display purposes # rescale image intensity for display purposes
im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9) im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
im_NIR = rescale_image_intensity(im_ms[:,:,3], cloud_mask, 99.9) # im_NIR = rescale_image_intensity(im_ms[:,:,3], cloud_mask, 99.9)
im_SWIR = rescale_image_intensity(im_ms[:,:,4], cloud_mask, 99.9) # im_SWIR = rescale_image_intensity(im_ms[:,:,4], cloud_mask, 99.9)
# make figure # make figure (just RGB)
fig = plt.figure() fig = plt.figure()
fig.set_size_inches([18,9]) fig.set_size_inches([18,9])
fig.set_tight_layout(True) fig.set_tight_layout(True)
@ -575,13 +574,12 @@ def save_jpg(metadata, settings):
sitename = settings['inputs']['sitename'] sitename = settings['inputs']['sitename']
cloud_thresh = settings['cloud_thresh'] cloud_thresh = settings['cloud_thresh']
filepath_data = settings['inputs']['filepath']
# create subfolder to store the jpg files # create subfolder to store the jpg files
filepath_jpg = os.path.join(os.getcwd(), 'data', sitename, 'jpg_files', 'preprocessed') filepath_jpg = os.path.join(filepath_data, sitename, 'jpg_files', 'preprocessed')
try: if not os.path.exists(filepath_jpg):
os.makedirs(filepath_jpg) os.makedirs(filepath_jpg)
except:
print('')
# loop through satellite list # loop through satellite list
for satname in metadata.keys(): for satname in metadata.keys():
@ -594,7 +592,7 @@ def save_jpg(metadata, settings):
# image filename # image filename
fn = SDS_tools.get_filenames(filenames[i],filepath, satname) fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
# read and preprocess image # read and preprocess image
im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname, settings['cloud_mask_issue']) im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata = preprocess_single(fn, satname, settings['cloud_mask_issue'])
# calculate cloud cover # calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))), cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1])) (cloud_mask.shape[0]*cloud_mask.shape[1]))
@ -602,14 +600,14 @@ def save_jpg(metadata, settings):
if cloud_cover > cloud_thresh or cloud_cover == 1: if cloud_cover > cloud_thresh or cloud_cover == 1:
continue continue
# save .jpg with date and satellite in the title # save .jpg with date and satellite in the title
date = filenames[i][:10] date = filenames[i][:19]
create_jpg(im_ms, cloud_mask, date, satname, filepath_jpg) create_jpg(im_ms, cloud_mask, date, satname, filepath_jpg)
# print the location where the images have been saved # print the location where the images have been saved
print('Satellite images saved as .jpg in ' + os.path.join(os.getcwd(), 'data', sitename, print('Satellite images saved as .jpg in ' + os.path.join(filepath_data, sitename,
'jpg_files', 'preprocessed')) 'jpg_files', 'preprocessed'))
def get_reference_sl_manual(metadata, settings): def get_reference_sl(metadata, settings):
""" """
Allows the user to manually digitize a reference shoreline that is used seed the shoreline Allows the user to manually digitize a reference shoreline that is used seed the shoreline
detection algorithm. The reference shoreline helps to detect the outliers, making the shoreline detection algorithm. The reference shoreline helps to detect the outliers, making the shoreline
@ -638,9 +636,10 @@ def get_reference_sl_manual(metadata, settings):
""" """
sitename = settings['inputs']['sitename'] sitename = settings['inputs']['sitename']
filepath_data = settings['inputs']['filepath']
# check if reference shoreline already exists in the corresponding folder # check if reference shoreline already exists in the corresponding folder
filepath = os.path.join(os.getcwd(), 'data', sitename) filepath = os.path.join(filepath_data, sitename)
filename = sitename + '_reference_shoreline.pkl' filename = sitename + '_reference_shoreline.pkl'
if filename in os.listdir(filepath): if filename in os.listdir(filepath):
print('Reference shoreline already exists and was loaded') print('Reference shoreline already exists and was loaded')
@ -673,21 +672,26 @@ def get_reference_sl_manual(metadata, settings):
# read image # read image
fn = SDS_tools.get_filenames(filenames[i],filepath, satname) fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
im_ms, georef, cloud_mask, im_extra, imQA = preprocess_single(fn, satname, settings['cloud_mask_issue']) im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata = preprocess_single(fn, satname, settings['cloud_mask_issue'])
# calculate cloud cover # calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))), cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1])) (cloud_mask.shape[0]*cloud_mask.shape[1]))
# skip image if cloud cover is above threshold # skip image if cloud cover is above threshold
if cloud_cover > settings['cloud_thresh']: if cloud_cover > settings['cloud_thresh']:
continue continue
# rescale image intensity for display purposes # rescale image intensity for display purposes
im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9) im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
# plot the image RGB on a figure # plot the image RGB on a figure
fig = plt.figure() fig = plt.figure()
fig.set_size_inches([18,9]) fig.set_size_inches([18,9])
fig.set_tight_layout(True) fig.set_tight_layout(True)
plt.axis('off') plt.axis('off')
plt.imshow(im_RGB) plt.imshow(im_RGB)
# decide if the image if good enough for digitizing the shoreline # decide if the image if good enough for digitizing the shoreline
plt.title('click <keep> if image is clear enough to digitize the shoreline.\n' + plt.title('click <keep> if image is clear enough to digitize the shoreline.\n' +
'If not (too cloudy) click on <skip> to get another image', fontsize=14) 'If not (too cloudy) click on <skip> to get another image', fontsize=14)
@ -699,13 +703,16 @@ def get_reference_sl_manual(metadata, settings):
bbox=dict(boxstyle="square", ec='k',fc='w')) bbox=dict(boxstyle="square", ec='k',fc='w'))
mng = plt.get_current_fig_manager() mng = plt.get_current_fig_manager()
mng.window.showMaximized() mng.window.showMaximized()
# let user click on the image once # let user click on the image once
pt_input = ginput(n=1, timeout=1e9, show_clicks=False) pt_input = ginput(n=1, timeout=1e9, show_clicks=False)
pt_input = np.array(pt_input) pt_input = np.array(pt_input)
# if clicks next to <skip>, show another image # if clicks next to <skip>, show another image
if pt_input[0][0] > im_ms.shape[1]/2: if pt_input[0][0] > im_ms.shape[1]/2:
plt.close() plt.close()
continue continue
else: else:
# remove keep and skip buttons # remove keep and skip buttons
keep_button.set_visible(False) keep_button.set_visible(False)
@ -716,9 +723,11 @@ def get_reference_sl_manual(metadata, settings):
bbox=dict(boxstyle="square", ec='k',fc='w')) bbox=dict(boxstyle="square", ec='k',fc='w'))
end_button = plt.text(1, 0.9, 'end', size=16, ha="right", va="top", end_button = plt.text(1, 0.9, 'end', size=16, ha="right", va="top",
transform=plt.gca().transAxes, transform=plt.gca().transAxes,
bbox=dict(boxstyle="square", ec='k',fc='w')) bbox=dict(boxstyle="square", ec='k',fc='w'))
# add multiple reference shorelines (until user clicks on <end> button) # add multiple reference shorelines (until user clicks on <end> button)
pts_sl = np.expand_dims(np.array([np.nan, np.nan]),axis=0) pts_sl = np.expand_dims(np.array([np.nan, np.nan]),axis=0)
geoms = []
while 1: while 1:
add_button.set_visible(False) add_button.set_visible(False)
end_button.set_visible(False) end_button.set_visible(False)
@ -727,11 +736,13 @@ def get_reference_sl_manual(metadata, settings):
'Start at one end of the beach.\n' + 'When finished digitizing, click <ENTER>', 'Start at one end of the beach.\n' + 'When finished digitizing, click <ENTER>',
fontsize=14) fontsize=14)
plt.draw() plt.draw()
# let user click on the shoreline # let user click on the shoreline
pts = ginput(n=50000, timeout=1e9, show_clicks=True) pts = ginput(n=50000, timeout=1e9, show_clicks=True)
pts_pix = np.array(pts) pts_pix = np.array(pts)
# convert pixel coordinates to world coordinates # convert pixel coordinates to world coordinates
pts_world = SDS_tools.convert_pix2world(pts_pix[:,[1,0]], georef) pts_world = SDS_tools.convert_pix2world(pts_pix[:,[1,0]], georef)
# interpolate between points clicked by the user (1m resolution) # interpolate between points clicked by the user (1m resolution)
pts_world_interp = np.expand_dims(np.array([np.nan, np.nan]),axis=0) pts_world_interp = np.expand_dims(np.array([np.nan, np.nan]),axis=0)
for k in range(len(pts_world)-1): for k in range(len(pts_world)-1):
@ -748,39 +759,65 @@ def get_reference_sl_manual(metadata, settings):
tf = transform.EuclideanTransform(rotation=phi, translation=pts_world[k,:]) 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.append(pts_world_interp,tf(pt_coords), axis=0)
pts_world_interp = np.delete(pts_world_interp,0,axis=0) pts_world_interp = np.delete(pts_world_interp,0,axis=0)
# save as geometry (to create .geojson file later)
geoms.append(geometry.LineString(pts_world_interp))
# convert to pixel coordinates and plot # convert to pixel coordinates and plot
pts_pix_interp = SDS_tools.convert_world2pix(pts_world_interp, georef) pts_pix_interp = SDS_tools.convert_world2pix(pts_world_interp, georef)
pts_sl = np.append(pts_sl, pts_world_interp, axis=0) 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], pts_pix_interp[:,1], 'r--')
plt.plot(pts_pix_interp[0,0], pts_pix_interp[0,1],'ko') 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') plt.plot(pts_pix_interp[-1,0], pts_pix_interp[-1,1],'ko')
# update title and buttons # update title and buttons
add_button.set_visible(True) add_button.set_visible(True)
end_button.set_visible(True) end_button.set_visible(True)
plt.title('click <add> to digitize another shoreline or <end> to finish and save the shoreline(s)', plt.title('click <add> to digitize another shoreline or <end> to finish and save the shoreline(s)',
fontsize=14) fontsize=14)
plt.draw() plt.draw()
# let the user click again (<add> another shoreline or <end>)
pt_input = ginput(n=1, timeout=1e9, show_clicks=False) pt_input = ginput(n=1, timeout=1e9, show_clicks=False)
pt_input = np.array(pt_input) pt_input = np.array(pt_input)
# if user clicks on <end>, save the points and break the loop # if user clicks on <end>, save the points and break the loop
if pt_input[0][0] > im_ms.shape[1]/2: if pt_input[0][0] > im_ms.shape[1]/2:
add_button.set_visible(False) add_button.set_visible(False)
end_button.set_visible(False) end_button.set_visible(False)
plt.title('Reference shoreline saved as ' + sitename + '_reference_shoreline.pkl') plt.title('Reference shoreline saved as ' + sitename + '_reference_shoreline.pkl and ' + sitename + '_reference_shoreline.geojson')
plt.draw() plt.draw()
ginput(n=1, timeout=5, show_clicks=False) ginput(n=1, timeout=3, show_clicks=False)
plt.close() plt.close()
break break
pts_sl = np.delete(pts_sl,0,axis=0) pts_sl = np.delete(pts_sl,0,axis=0)
# convert world coordinates to user-defined coordinates # convert world image coordinates to user-defined coordinate system
image_epsg = metadata[satname]['epsg'][i] image_epsg = metadata[satname]['epsg'][i]
pts_coords = SDS_tools.convert_epsg(pts_sl, image_epsg, settings['output_epsg']) pts_coords = SDS_tools.convert_epsg(pts_sl, image_epsg, settings['output_epsg'])
# save the reference shoreline # save the reference shoreline as .pkl
filepath = os.path.join(os.getcwd(), 'data', sitename) filepath = os.path.join(filepath_data, sitename)
with open(os.path.join(filepath, sitename + '_reference_shoreline.pkl'), 'wb') as f: with open(os.path.join(filepath, sitename + '_reference_shoreline.pkl'), 'wb') as f:
pickle.dump(pts_coords, f) pickle.dump(pts_coords, f)
# also store as .geojson in case user wants to drag-and-drop on GIS for verification
for k,line in enumerate(geoms):
gdf = gpd.GeoDataFrame(geometry=gpd.GeoSeries(line))
gdf.index = [k]
gdf.loc[k,'name'] = 'reference shoreline ' + str(k+1)
# store into geodataframe
if k == 0:
gdf_all = gdf
else:
gdf_all = gdf_all.append(gdf)
gdf_all.crs = {'init':'epsg:'+str(image_epsg)}
# convert from image_epsg to user-defined coordinate system
gdf_all = gdf_all.to_crs({'init': 'epsg:'+str(settings['output_epsg'])})
# save as geojson
gdf_all.to_file(os.path.join(filepath, sitename + '_reference_shoreline.geojson'),
driver='GeoJSON', encoding='utf-8')
print('Reference shoreline has been saved in ' + filepath) print('Reference shoreline has been saved in ' + filepath)
break break

669
SDS_shoreline.py → coastsat/SDS_shoreline.py
View File

File diff suppressed because it is too large Load Diff

385
SDS_tools.py → coastsat/SDS_tools.py
Unescape Escape View File

@ -10,11 +10,16 @@ import matplotlib.pyplot as plt
import pdb import pdb
# other modules # other modules
from osgeo import gdal, ogr, osr from osgeo import gdal, osr
import geopandas as gpd
from shapely import geometry
import skimage.transform as transform import skimage.transform as transform
import simplekml
from scipy.ndimage.filters import uniform_filter from scipy.ndimage.filters import uniform_filter
###################################################################################################
# COORDINATES CONVERSION FUNCTIONS
###################################################################################################
def convert_pix2world(points, georef): def convert_pix2world(points, georef):
""" """
Converts pixel coordinates (row,columns) to world projected coordinates Converts pixel coordinates (row,columns) to world projected coordinates
@ -54,8 +59,7 @@ def convert_pix2world(points, georef):
points_converted = tform(tmp) points_converted = tform(tmp)
else: else:
print('invalid input type') raise Exception('invalid input type')
raise
return points_converted return points_converted
@ -139,64 +143,118 @@ def convert_epsg(points, epsg_in, epsg_out):
elif type(points) is np.ndarray: elif type(points) is np.ndarray:
points_converted = np.array(coordTransform.TransformPoints(points)) points_converted = np.array(coordTransform.TransformPoints(points))
else: else:
print('invalid input type') raise Exception('invalid input type')
raise
return points_converted return points_converted
def coords_from_kml(fn): ###################################################################################################
""" # IMAGE ANALYSIS FUNCTIONS
Extracts coordinates from a .kml file. ###################################################################################################
def nd_index(im1, im2, cloud_mask):
"""
Computes normalised difference index on 2 images (2D), given a cloud mask (2D).
KV WRL 2018 KV WRL 2018
Arguments: Arguments:
----------- -----------
fn: str im1, im2: np.array
filepath + filename of the kml file to be read Images (2D) with which to calculate the ND index
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
Returns: ----------- Returns: -----------
polygon: list im_nd: np.array
coordinates extracted from the .kml file Image (2D) containing the ND index
"""
"""
# reshape the cloud mask
vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
# initialise with NaNs
vec_nd = np.ones(len(vec_mask)) * np.nan
# reshape the two images
vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
# compute the normalised difference index
temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
vec1[~vec_mask] + vec2[~vec_mask])
vec_nd[~vec_mask] = temp
# reshape into image
im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
return im_nd
# read .kml file def image_std(image, radius):
with open(fn) as kmlFile: """
doc = kmlFile.read() Calculates the standard deviation of an image, using a moving window of specified radius.
# parse to find coordinates field
str1 = '<coordinates>' Arguments:
str2 = '</coordinates>' -----------
subdoc = doc[doc.find(str1)+len(str1):doc.find(str2)] image: np.array
coordlist = subdoc.split('\n') 2D array containing the pixel intensities of a single-band image
# read coordinates radius: int
polygon = [] radius defining the moving window used to calculate the standard deviation. For example,
for i in range(1,len(coordlist)-1): radius = 1 will produce a 3x3 moving window.
polygon.append([float(coordlist[i].split(',')[0]), float(coordlist[i].split(',')[1])])
return [polygon] Returns:
-----------
win_std: np.array
2D array containing the standard deviation of the image
"""
# convert to float
image = image.astype(float)
# first pad the image
image_padded = np.pad(image, radius, 'reflect')
# window size
win_rows, win_cols = radius*2 + 1, radius*2 + 1
# calculate std
win_mean = uniform_filter(image_padded, (win_rows, win_cols))
win_sqr_mean = uniform_filter(image_padded**2, (win_rows, win_cols))
win_var = win_sqr_mean - win_mean**2
win_std = np.sqrt(win_var)
# remove padding
win_std = win_std[radius:-radius, radius:-radius]
def save_kml(coords, epsg): return win_std
def mask_raster(fn, mask):
""" """
Saves coordinates with specified spatial reference system into a .kml file in WGS84. Masks a .tif raster using GDAL.
KV WRL 2018
Arguments: Arguments:
----------- -----------
coords: np.array fn: str
coordinates (2 columns) to be converted into a .kml file filepath + filename of the .tif raster
mask: np.array
array of boolean where True indicates the pixels that are to be masked
Returns: Returns:
----------- -----------
Saves 'coords.kml' in the current folder. overwrites the .tif file directly
""" """
kml = simplekml.Kml() # open raster
coords_wgs84 = convert_epsg(coords, epsg, 4326) raster = gdal.Open(fn, gdal.GA_Update)
kml.newlinestring(name='coords', coords=coords_wgs84) # mask raster
kml.save('coords.kml') for i in range(raster.RasterCount):
out_band = raster.GetRasterBand(i+1)
out_data = out_band.ReadAsArray()
out_band.SetNoDataValue(0)
no_data_value = out_band.GetNoDataValue()
out_data[mask] = no_data_value
out_band.WriteArray(out_data)
# close dataset and flush cache
raster = None
###################################################################################################
# UTILITIES
###################################################################################################
def get_filepath(inputs,satname): def get_filepath(inputs,satname):
""" """
@ -230,38 +288,26 @@ def get_filepath(inputs,satname):
""" """
sitename = inputs['sitename'] sitename = inputs['sitename']
filepath_data = inputs['filepath']
# access the images # access the images
if satname == 'L5': if satname == 'L5':
# access downloaded Landsat 5 images # access downloaded Landsat 5 images
filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m') filepath = os.path.join(filepath_data, sitename, satname, '30m')
elif satname == 'L7': elif satname == 'L7':
# access downloaded Landsat 7 images # access downloaded Landsat 7 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'pan') filepath_pan = os.path.join(filepath_data, sitename, 'L7', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'ms') filepath_ms = os.path.join(filepath_data, sitename, 'L7', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms] filepath = [filepath_pan, filepath_ms]
elif satname == 'L8': elif satname == 'L8':
# access downloaded Landsat 8 images # access downloaded Landsat 8 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'pan') filepath_pan = os.path.join(filepath_data, sitename, 'L8', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'ms') filepath_ms = os.path.join(filepath_data, sitename, 'L8', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms] filepath = [filepath_pan, filepath_ms]
elif satname == 'S2': elif satname == 'S2':
# access downloaded Sentinel 2 images # access downloaded Sentinel 2 images
filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m') filepath10 = os.path.join(filepath_data, sitename, satname, '10m')
filenames10 = os.listdir(filepath10) filepath20 = os.path.join(filepath_data, sitename, satname, '20m')
filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m') filepath60 = os.path.join(filepath_data, sitename, satname, '60m')
filenames20 = os.listdir(filepath20)
filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
filenames60 = os.listdir(filepath60)
if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
raise 'error: not the same amount of files for 10, 20 and 60 m bands'
filepath = [filepath10, filepath20, filepath60] filepath = [filepath10, filepath20, filepath60]
return filepath return filepath
@ -302,106 +348,169 @@ def get_filenames(filename, filepath, satname):
os.path.join(filepath[2], filename60)] os.path.join(filepath[2], filename60)]
return fn return fn
def image_std(image, radius): def merge_output(output):
""" """
Calculates the standard deviation of an image, using a moving window of specified radius. Function to merge the output dictionnary, which has one key per satellite mission into a
dictionnary containing all the shorelines and dates ordered chronologically.
Arguments: Arguments:
----------- -----------
image: np.array output: dict
2D array containing the pixel intensities of a single-band image contains the extracted shorelines and corresponding dates, organised by satellite mission
radius: int
radius defining the moving window used to calculate the standard deviation. For example,
radius = 1 will produce a 3x3 moving window.
Returns: Returns:
----------- -----------
win_std: np.array output_all: dict
2D array containing the standard deviation of the image contains the extracted shorelines in a single list sorted by date
""" """
# convert to float # initialize output dict
image = image.astype(float) output_all = dict([])
# first pad the image satnames = list(output.keys())
image_padded = np.pad(image, radius, 'reflect') for key in output[satnames[0]].keys():
# window size output_all[key] = []
win_rows, win_cols = radius*2 + 1, radius*2 + 1 # create extra key for the satellite name
# calculate std output_all['satname'] = []
win_mean = uniform_filter(image_padded, (win_rows, win_cols)) # fill the output dict
win_sqr_mean = uniform_filter(image_padded**2, (win_rows, win_cols)) for satname in list(output.keys()):
win_var = win_sqr_mean - win_mean**2 for key in output[satnames[0]].keys():
win_std = np.sqrt(win_var) output_all[key] = output_all[key] + output[satname][key]
# remove padding output_all['satname'] = output_all['satname'] + [_ for _ in np.tile(satname,
win_std = win_std[radius:-radius, radius:-radius] len(output[satname]['dates']))]
# sort chronologically
idx_sorted = sorted(range(len(output_all['dates'])), key=output_all['dates'].__getitem__)
for key in output_all.keys():
output_all[key] = [output_all[key][i] for i in idx_sorted]
return win_std return output_all
def mask_raster(fn, mask): ###################################################################################################
# CONVERSIONS FROM DICT TO GEODATAFRAME AND READ/WRITE GEOJSON
###################################################################################################
def polygon_from_kml(fn):
""" """
Masks a .tif raster using GDAL. Extracts coordinates from a .kml file.
KV WRL 2018
Arguments: Arguments:
----------- -----------
fn: str fn: str
filepath + filename of the .tif raster filepath + filename of the kml file to be read
mask: np.array
array of boolean where True indicates the pixels that are to be masked Returns: -----------
polygon: list
Returns: coordinates extracted from the .kml file
-----------
overwrites the .tif file directly
""" """
# open raster
raster = gdal.Open(fn, gdal.GA_Update)
# mask raster
for i in range(raster.RasterCount):
out_band = raster.GetRasterBand(i+1)
out_data = out_band.ReadAsArray()
out_band.SetNoDataValue(0)
no_data_value = out_band.GetNoDataValue()
out_data[mask] = no_data_value
out_band.WriteArray(out_data)
# close dataset and flush cache
raster = None
def merge_output(output): # read .kml file
with open(fn) as kmlFile:
doc = kmlFile.read()
# parse to find coordinates field
str1 = '<coordinates>'
str2 = '</coordinates>'
subdoc = doc[doc.find(str1)+len(str1):doc.find(str2)]
coordlist = subdoc.split('\n')
# read coordinates
polygon = []
for i in range(1,len(coordlist)-1):
polygon.append([float(coordlist[i].split(',')[0]), float(coordlist[i].split(',')[1])])
return [polygon]
def transects_from_geojson(filename):
""" """
Function to merge the output dictionnary, which has one key per satellite mission into a Reads transect coordinates from a .geojson file.
dictionnary containing all the shorelines and dates ordered chronologically.
Arguments: Arguments:
----------- -----------
output: dict filename: str
contains the extracted shorelines and corresponding dates. contains the path and filename of the geojson file to be loaded
Returns: Returns:
----------- -----------
output_all_sorted: dict transects: dict
contains the extracted shorelines sorted by date in a single list contains the X and Y coordinates of each transect.
""" """
output_all = {'dates':[], 'shorelines':[], 'geoaccuracy':[], 'satname':[], 'image_filename':[]}
for satname in list(output.keys()): gdf = gpd.read_file(filename)
if satname == 'meta': transects = dict([])
for i in gdf.index:
transects[gdf.loc[i,'name']] = np.array(gdf.loc[i,'geometry'].coords)
print('%d transects have been loaded' % len(transects.keys()))
return transects
def output_to_gdf(output):
"""
Saves the mapped shorelines as a gpd.GeoDataFrame
KV WRL 2018
Arguments:
-----------
output: dict
contains the coordinates of the mapped shorelines + attributes
Returns: -----------
gdf_all: gpd.GeoDataFrame
"""
# loop through the mapped shorelines
for i in range(len(output['shorelines'])):
# skip if there shoreline is empty
if len(output['shorelines'][i]) == 0:
continue continue
output_all['dates'] = output_all['dates'] + output[satname]['timestamp'] # save the geometry + attributes
output_all['shorelines'] = output_all['shorelines'] + output[satname]['shoreline'] geom = geometry.LineString(output['shorelines'][i])
output_all['geoaccuracy'] = output_all['geoaccuracy'] + output[satname]['geoaccuracy'] gdf = gpd.GeoDataFrame(geometry=gpd.GeoSeries(geom))
output_all['satname'] = output_all['satname'] + [_ for _ in np.tile(satname, gdf.index = [i]
len(output[satname]['timestamp']))] gdf.loc[i,'date'] = output['dates'][i].strftime('%Y-%m-%d %H:%M:%S')
output_all['image_filename'] = output_all['image_filename'] + output[satname]['filename'] gdf.loc[i,'satname'] = output['satname'][i]
gdf.loc[i,'geoaccuracy'] = output['geoaccuracy'][i]
gdf.loc[i,'cloud_cover'] = output['cloud_cover'][i]
# store into geodataframe
if i == 0:
gdf_all = gdf
else:
gdf_all = gdf_all.append(gdf)
return gdf_all
def transects_to_gdf(transects):
"""
Saves the shore-normal transects as a gpd.GeoDataFrame
# sort chronologically KV WRL 2018
output_all_sorted = {'dates':[], 'shorelines':[], 'geoaccuracy':[], 'satname':[], 'image_filename':[]}
idx_sorted = sorted(range(len(output_all['dates'])), key=output_all['dates'].__getitem__)
output_all_sorted['dates'] = [output_all['dates'][i] for i in idx_sorted]
output_all_sorted['shorelines'] = [output_all['shorelines'][i] for i in idx_sorted]
output_all_sorted['geoaccuracy'] = [output_all['geoaccuracy'][i] for i in idx_sorted]
output_all_sorted['satname'] = [output_all['satname'][i] for i in idx_sorted]
output_all_sorted['image_filename'] = [output_all['image_filename'][i] for i in idx_sorted]
return output_all_sorted Arguments:
-----------
transects: dict
contains the coordinates of the transects
Returns: -----------
gdf_all: gpd.GeoDataFrame
"""
# loop through the mapped shorelines
for i,key in enumerate(list(transects.keys())):
# save the geometry + attributes
geom = geometry.LineString(transects[key])
gdf = gpd.GeoDataFrame(geometry=gpd.GeoSeries(geom))
gdf.index = [i]
gdf.loc[i,'name'] = key
# store into geodataframe
if i == 0:
gdf_all = gdf
else:
gdf_all = gdf_all.append(gdf)
return gdf_all

121
SDS_transects.py → coastsat/SDS_transects.py
Unescape Escape View File

@ -12,15 +12,10 @@ import pdb
# other modules # other modules
import skimage.transform as transform import skimage.transform as transform
from pylab import ginput from pylab import ginput
import pickle import geopandas as gpd
import simplekml
import json
from osgeo import ogr
def find_indices(lst, condition):
"imitation of MATLAB find function"
return [i for i, elem in enumerate(lst) if condition(elem)]
# own modules
from coastsat import SDS_tools
def create_transect(origin, orientation, length): def create_transect(origin, orientation, length):
""" """
@ -66,19 +61,16 @@ def draw_transects(output, settings):
output: dict output: dict
contains the extracted shorelines and corresponding dates. contains the extracted shorelines and corresponding dates.
settings: dict settings: dict
contains parameters defining : contains the inputs
transect_length: length of the transect in metres
Returns: Returns:
----------- -----------
transects: dict transects: dict
contains the X and Y coordinates of all the transects drawn. These are also saved contains the X and Y coordinates of all the transects drawn. These are also saved
as a .pkl and .kml (+ a .jpg figure showing the location of the transects) as a .geojson (+ a .jpg figure showing the location of the transects)
""" """
sitename = settings['inputs']['sitename'] sitename = settings['inputs']['sitename']
length = settings['transect_length'] filepath = os.path.join(settings['inputs']['filepath'], sitename)
filepath = os.path.join(os.getcwd(), 'data', sitename)
# plot all shorelines # plot all shorelines
fig1 = plt.figure() fig1 = plt.figure()
@ -103,90 +95,47 @@ def draw_transects(output, settings):
counter = 0 counter = 0
# loop until user breaks it by click <enter> # loop until user breaks it by click <enter>
while 1: while 1:
try: # let user click two points
pts = ginput(n=2, timeout=1e9) pts = ginput(n=2, timeout=1e9)
if len(pts) > 0:
origin = pts[0] origin = pts[0]
except: else:
fig1.gca().set_title('Transect locations', fontsize=16) fig1.gca().set_title('Transect locations', fontsize=16)
fig1.savefig(os.path.join(filepath, 'jpg_files', sitename + '_transect_locations.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.title('Transect coordinates saved as ' + sitename + '_transects.geojson')
plt.draw() plt.draw()
ginput(n=1, timeout=5, show_clicks=True) ginput(n=1, timeout=3, show_clicks=True)
plt.close(fig1) plt.close(fig1)
break break
counter = counter + 1 counter = counter + 1
# create the transect using the origin, orientation and length transect = np.array([pts[0], pts[1]])
temp = np.array(pts[1]) - np.array(origin)
phi = np.arctan2(temp[1], temp[0]) # alternative of making the transect the origin, orientation and length
orientation = -(phi*180/np.pi - 90) # temp = np.array(pts[1]) - np.array(origin)
transect = create_transect(origin, orientation, length) # phi = np.arctan2(temp[1], temp[0])
# orientation = -(phi*180/np.pi - 90)
# length = np.linalg.norm(temp)
# transect = create_transect(origin, orientation, length)
transects[str(counter)] = transect transects[str(counter)] = transect
# plot the transects on the figure # plot the transects on the figure
ax1.plot(transect[:,0], transect[:,1], 'b.', markersize=4) ax1.plot(transect[:,0], transect[:,1], 'b-', lw=2.5)
ax1.plot(transect[0,0], transect[0,1], 'rx', markersize=10) ax1.plot(transect[0,0], transect[0,1], 'rx', markersize=10)
ax1.text(transect[-1,0], transect[-1,1], str(counter), size=16, ax1.text(transect[-1,0], transect[-1,1], str(counter), size=16,
bbox=dict(boxstyle="square", ec='k',fc='w')) bbox=dict(boxstyle="square", ec='k',fc='w'))
plt.draw() plt.draw()
# save as transects.pkl
with open(os.path.join(filepath, sitename + '_transects.pkl'), 'wb') as f:
pickle.dump(transects, f)
# save as transects.kml (for GIS) # save as transects.geojson (for GIS)
kml = simplekml.Kml() gdf = SDS_tools.transects_to_gdf(transects)
for key in transects.keys(): # set projection
newline = kml.newlinestring(name=key) gdf.crs = {'init':'epsg:'+str(settings['output_epsg'])}
newline.coords = transects[key] # save as geojson
newline.description = 'user-defined cross-shore transect' gdf.to_file(os.path.join(filepath, sitename + '_transects.geojson'), driver='GeoJSON', encoding='utf-8')
kml.save(os.path.join(filepath, sitename + '_transects.kml'))
print('Transect locations saved in ' + filepath) print('Transect locations saved in ' + filepath)
return transects 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): def compute_intersection(output, transects, settings):
""" """
Computes the intersection between the 2D mapped shorelines and the transects, to generate Computes the intersection between the 2D mapped shorelines and the transects, to generate
@ -240,12 +189,18 @@ def compute_intersection(output, transects, settings):
d_origin = np.array([np.linalg.norm(sl[k,:] - p1) for k in range(len(sl))]) d_origin = np.array([np.linalg.norm(sl[k,:] - p1) for k in range(len(sl))])
# find the shoreline points that are close to the transects and to the origin # find the shoreline points that are close to the transects and to the origin
# the distance to the origin is hard-coded here to 1 km # the distance to the origin is hard-coded here to 1 km
logic_close = np.logical_and(d_line <= along_dist, d_origin <= 1000) idx_dist = np.logical_and(d_line <= along_dist, d_origin <= 1000)
idx_close = find_indices(logic_close, lambda e: e == True) # find the shoreline points that are in the direction of the transect (within 90 degrees)
idx_points_all.append(idx_close) temp_sl = sl - np.array(transects[key][0,:])
phi_sl = np.array([np.arctan2(temp_sl[k,1], temp_sl[k,0]) for k in range(len(temp_sl))])
diff_angle = (phi - phi_sl)
idx_angle = np.abs(diff_angle) < np.pi/2
# combine the transects that are close in distance and close in orientation
idx_close = np.where(np.logical_and(idx_dist,idx_angle))[0]
idx_points_all.append(idx_close)
# in case there are no shoreline points close to the transect # in case there are no shoreline points close to the transect
if not idx_close: if len(idx_close) == 0:
chainage_mtx[i,j,:] = np.tile(np.nan,(1,6)) chainage_mtx[i,j,:] = np.tile(np.nan,(1,6))
else: else:
# change of base to shore-normal coordinate system # change of base to shore-normal coordinate system

0
coastsat/__init__.py
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0
gdal_merge.py → coastsat/gdal_merge.py
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18
environment.yml
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@ -0,0 +1,18 @@
channels:
- defaults
- conda-forge
dependencies:
- python=3.7
- numpy=1.16.3
- matplotlib=3.0.3
- earthengine-api=0.1.173
- gdal=2.3.3
- pandas=0.24.2
- geopandas=0.4.1
- pytz=2019.1
- scikit-image=0.15.0
- scikit-learn=0.20.3
- shapely=1.6.4
- scipy=1.2.1
- spyder=3.3.4
- notebook=5.7.8

60
main.py → example.py
Unescape Escape View File

@ -13,31 +13,37 @@ import pickle
import warnings import warnings
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects from coastsat import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects
# region of interest (longitude, latitude in WGS84), can be loaded from a .kml polygon # region of interest (longitude, latitude in WGS84)
polygon = SDS_tools.coords_from_kml('NARRA_polygon.kml') polygon = [[[151.301454, -33.700754],
#polygon = [[[151.301454, -33.700754], [151.311453, -33.702075],
# [151.311453, -33.702075], [151.307237, -33.739761],
# [151.307237, -33.739761], [151.294220, -33.736329],
# [151.294220, -33.736329], [151.301454, -33.700754]]]
# [151.301454, -33.700754]]] # can also be loaded from a .kml polygon
#kml_polygon = os.path.join(os.getcwd(), 'examples', 'NARRA_polygon.kml')
#polygon = SDS_tools.polygon_from_kml(kml_polygon)
# date range # date range
dates = ['2017-12-01', '2018-02-01'] dates = ['2017-12-01', '2018-01-01']
# satellite missions # satellite missions
sat_list = ['L8','S2'] sat_list = ['S2']
# name of the site # name of the site
sitename = 'NARRA' sitename = 'NARRA'
# filepath where data will be stored
filepath_data = os.path.join(os.getcwd(), 'data')
# put all the inputs into a dictionnary # put all the inputs into a dictionnary
inputs = { inputs = {
'polygon': polygon, 'polygon': polygon,
'dates': dates, 'dates': dates,
'sat_list': sat_list, 'sat_list': sat_list,
'sitename': sitename 'sitename': sitename,
'filepath': filepath_data
} }
#%% 2. Retrieve images #%% 2. Retrieve images
@ -46,35 +52,33 @@ inputs = {
metadata = SDS_download.retrieve_images(inputs) metadata = SDS_download.retrieve_images(inputs)
# if you have already downloaded the images, just load the metadata file # if you have already downloaded the images, just load the metadata file
#filepath = os.path.join(os.getcwd(), 'data', sitename) metadata = SDS_download.get_metadata(inputs)
#with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
# metadata = pickle.load(f)
#%% 3. Batch shoreline detection #%% 3. Batch shoreline detection
# settings for the shoreline extraction # settings for the shoreline extraction
settings = { settings = {
# general parameters: # general parameters:
'cloud_thresh': 0.2, # threshold on maximum cloud cover 'cloud_thresh': 0.5, # threshold on maximum cloud cover
'output_epsg': 28356, # epsg code of spatial reference system desired for the output 'output_epsg': 28356, # epsg code of spatial reference system desired for the output
# quality control: # quality control:
'check_detection': True, # if True, shows each shoreline detection to the user for validation 'check_detection': True, # if True, shows each shoreline detection to the user for validation
'save_figure': True, # if True, saves a figure showing the mapped shoreline for each image
# add the inputs defined previously # add the inputs defined previously
'inputs': inputs, 'inputs': inputs,
# [ONLY FOR ADVANCED USERS] shoreline detection parameters: # [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 '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 '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 'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images
'dark_sand': False, # only switch to True if your site has dark sand (e.g. black sand beach)
} }
# [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling) # [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) # [OPTIONAL] create a reference shoreline (helps to identify outliers and false detections)
settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings) settings['reference_shoreline'] = SDS_preprocess.get_reference_sl(metadata, settings)
# set the max distance (in meters) allowed from the reference shoreline for a detected shoreline to be valid # set the max distance (in meters) allowed from the reference shoreline for a detected shoreline to be valid
settings['max_dist_ref'] = 100 settings['max_dist_ref'] = 100
@ -99,14 +103,12 @@ fig.set_size_inches([15.76, 8.52])
#%% 4. Shoreline analysis #%% 4. Shoreline analysis
# if you have already mapped the shorelines, load the output.pkl file # if you have already mapped the shorelines, load the output.pkl file
filepath = os.path.join(os.getcwd(), 'data', sitename) filepath = os.path.join(inputs['filepath'], sitename)
with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f: with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
output = pickle.load(f) output = pickle.load(f)
# now we have to define cross-shore transects over which to quantify the shoreline changes # 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 # 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: # there are 3 options to create the transects:
# - option 1: draw the shore-normal transects along the beach # - option 1: draw the shore-normal transects along the beach
@ -116,9 +118,9 @@ settings['transect_length'] = 500
# option 1: draw origin of transect first and then a second point to define the orientation # option 1: draw origin of transect first and then a second point to define the orientation
transects = SDS_transects.draw_transects(output, settings) transects = SDS_transects.draw_transects(output, settings)
# option 2: load the transects from a KML file # option 2: load the transects from a .geojson file
#kml_file = 'NARRA_transects.kml' #geojson_file = os.path.join(os.getcwd(), 'examples', 'NARRA_transects.geojson')
#transects = SDS_transects.load_transects_from_kml(kml_file) #transects = SDS_tools.transects_from_geojson(geojson_file)
# option 3: create the transects by manually providing the coordinates of two points # option 3: create the transects by manually providing the coordinates of two points
#transects = dict([]) #transects = dict([])
@ -136,11 +138,11 @@ fig = plt.figure()
gs = gridspec.GridSpec(len(cross_distance),1) gs = gridspec.GridSpec(len(cross_distance),1)
gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05) gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05)
for i,key in enumerate(cross_distance.keys()): for i,key in enumerate(cross_distance.keys()):
if np.all(np.isnan(cross_distance[key])):
continue
ax = fig.add_subplot(gs[i,0]) ax = fig.add_subplot(gs[i,0])
ax.grid(linestyle=':', color='0.5') ax.grid(linestyle=':', color='0.5')
ax.set_ylim([-50,50]) ax.set_ylim([-50,50])
if not i == len(cross_distance.keys()):
ax.set_xticks = []
ax.plot(output['dates'], cross_distance[key]- np.nanmedian(cross_distance[key]), '-^', markersize=6) ax.plot(output['dates'], cross_distance[key]- np.nanmedian(cross_distance[key]), '-^', markersize=6)
ax.set_ylabel('distance [m]', fontsize=12) ax.set_ylabel('distance [m]', fontsize=12)
ax.text(0.5,0.95,'Transect ' + key, bbox=dict(boxstyle="square", ec='k',fc='w'), ha='center', ax.text(0.5,0.95,'Transect ' + key, bbox=dict(boxstyle="square", ec='k',fc='w'), ha='center',

73
example_jupyter.ipynb
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@ -8,9 +8,10 @@
"\n", "\n",
"This software is described in *Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (submitted). CoastSat: a Google Earth Engine-enabled software to extract shorelines from publicly available satellite imagery, Environmental Modelling and Software*. It enables the users to extract time-series of shoreline change over the last 30+ years at their site of interest.\n", "This software is described in *Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (submitted). CoastSat: a Google Earth Engine-enabled software to extract shorelines from publicly available satellite imagery, Environmental Modelling and Software*. It enables the users to extract time-series of shoreline change over the last 30+ years at their site of interest.\n",
"\n", "\n",
"There are two main steps:\n", "There are three main steps:\n",
"- retrieval of the satellite images of the region of interest from Google Earth Engine\n", "- retrieval of the satellite images of the region of interest from Google Earth Engine\n",
"- extraction of the shorelines from the images using a sub-pixel resolution technique\n", "- extraction of the shorelines from the images using a sub-pixel resolution technique\n",
"- intersection of the 2D shorelines with shore-normal transects\n",
"\n", "\n",
"## 1. Initial settings\n", "## 1. Initial settings\n",
"\n", "\n",
@ -29,7 +30,7 @@
"import warnings\n", "import warnings\n",
"warnings.filterwarnings(\"ignore\")\n", "warnings.filterwarnings(\"ignore\")\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects" "from coastsat import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects"
] ]
}, },
{ {
@ -38,7 +39,9 @@
"source": [ "source": [
"## 2. Retrieval of the images from GEE\n", "## 2. Retrieval of the images from GEE\n",
"\n", "\n",
"Define the region of interest (`polygon`), the date range (`dates`) and the satellite missions (`sat_list`) from which you wish to retrieve the satellite images. The images will be cropped on the Google Earth Engine server and only the region of interest will be downloaded as a .TIF file. The files will be organised in the local directory under *.\\data\\sitename*." "Define the region of interest (`polygon`), the date range (`dates`) and the satellite missions (`sat_list`) from which you wish to retrieve the satellite images. The images will be cropped on the Google Earth Engine server and only the region of interest will be downloaded as a .tif file. The files will stored in the directory defined in `filepath`.\n",
"\n",
"Make sure the area of your ROI is smaller than 100 km2 (if larger split it into smaller ROIs)."
] ]
}, },
{ {
@ -59,8 +62,10 @@
"sat_list = ['S2']\n", "sat_list = ['S2']\n",
"# name of the site\n", "# name of the site\n",
"sitename = 'NARRA'\n", "sitename = 'NARRA'\n",
"# directory where the data will be stored\n",
"filepath = os.path.join(os.getcwd(), 'data')\n",
"# put all the inputs into a dictionnary\n", "# put all the inputs into a dictionnary\n",
"inputs = {'polygon': polygon, 'dates': dates, 'sat_list': sat_list, 'sitename': sitename}" "inputs = {'polygon': polygon, 'dates': dates, 'sat_list': sat_list, 'sitename': sitename, 'filepath':filepath}"
] ]
}, },
{ {
@ -92,9 +97,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"filepath = os.path.join(os.getcwd(), 'data', sitename)\n", "metadata = SDS_download.get_metadata(inputs) "
"with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:\n",
" metadata = pickle.load(f) "
] ]
}, },
{ {
@ -103,7 +106,11 @@
"source": [ "source": [
"## 3. Shoreline extraction\n", "## 3. Shoreline extraction\n",
"\n", "\n",
"Maps the position of the shoreline on the satellite images. The user can define the cloud threhold (`cloud_thresh`) and select the spatial reference system in which he would like to output the coordinates of the mapped shorelines (`output_epsg`). See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. To quality control each shoreline detection and manually validate the mapped shorelines, the user has the option to set the parameter `check_detection` to **True**. The other parameters are for advanced users only and are described in the last section of the README." "This section maps the position of the shoreline on the satellite images. The user can define the cloud threhold (`cloud_thresh`) and select the spatial reference system in which to output the coordinates of the mapped shorelines (`output_epsg`). See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. Make sure that your are using cartesian coordinates and not spherical coordinates (lat,lon) like WGS84. \n",
"\n",
"To quality control each shoreline detection and manually validate the mapped shorelines, the user has the option to set the parameter `check_detection` to **True**. To save a figure for each mapped shoreline set `save_figure` to **True**. \n",
"\n",
"The other parameters are for advanced users only and are described in the README."
] ]
}, },
{ {
@ -114,19 +121,19 @@
"source": [ "source": [
"settings = { \n", "settings = { \n",
" # general parameters:\n", " # general parameters:\n",
" 'cloud_thresh': 0.2, # threshold on maximum cloud cover\n", " 'cloud_thresh': 0.5, # threshold on maximum cloud cover\n",
" 'output_epsg': 28356, # epsg code of spatial reference system desired for the output \n", " 'output_epsg': 28356, # epsg code of spatial reference system desired for the output \n",
" # quality control:\n", " # quality control:\n",
" 'check_detection': True, # if True, shows each shoreline detection to the user for validation\n", " 'check_detection': True, # if True, shows each shoreline detection to the user for validation\n",
"\n", " 'save_figure': True, # if True, saves a figure showing the mapped shoreline for each image\n",
" # add the inputs defined previously\n", " # add the inputs defined previously\n",
" 'inputs': inputs,\n", " 'inputs': inputs,\n",
" \n",
" # [ONLY FOR ADVANCED USERS] shoreline detection parameters:\n", " # [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", " '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", " '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", " 'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images \n",
" 'dark_sand': False, # only switch to True if your site has dark sand (e.g. black sand beach)\n",
"}" "}"
] ]
}, },
@ -152,7 +159,7 @@
"metadata": {}, "metadata": {},
"source": [ "source": [
"### [OPTIONAL] Digitize a reference shoreline\n", "### [OPTIONAL] Digitize a reference shoreline\n",
"Creates a reference shoreline which helps to identify outliers and false detections. The reference shoreline is manually digitised by the user on one of the images. The parameter `max_dist_ref` defines the maximum distance from the reference shoreline (in metres) at which a valid detected shoreline can be. If you think that you shoreline will move more than the default value of 100 m, please change this parameter to an appropriate distance." "Creates a reference shoreline which helps to identify outliers and false detections. The reference shoreline is manually digitised by the user on one of the images. The parameter `max_dist_ref` defines the maximum distance from the reference shoreline (in metres) at which a valid detected shoreline can be. If you think that the default value of 100 m will not capture the full shoreline variability of your site, increase this value to an appropriate distance."
] ]
}, },
{ {
@ -162,7 +169,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib qt\n", "%matplotlib qt\n",
"settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings)\n", "settings['reference_shoreline'] = SDS_preprocess.get_reference_sl(metadata, settings)\n",
"settings['max_dist_ref'] = 100 # max distance (in meters) allowed from the reference shoreline" "settings['max_dist_ref'] = 100 # max distance (in meters) allowed from the reference shoreline"
] ]
}, },
@ -171,7 +178,7 @@
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Batch shoreline detection\n", "### Batch shoreline detection\n",
"Extracts the shorelines from the images. The mapped shorelines are saved into `output.pkl` (under *./data/sitename*) which contains the shoreline coordinates for each date in the spatial reference system specified by the user in `'output_epsg'`." "Extracts the 2D shorelines from the images in the spatial reference system specified by the user in `'output_epsg'`. The mapped shorelines are saved into `output.pkl` (under *./data/sitename*) and `output.geojson` to use in GIS softwares."
] ]
}, },
{ {
@ -190,7 +197,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Simple plot of the mapped shorelines" "Simple plot of the mapped shorelines. The coordinates are stored in the output dictionnary together with the exact dates in UTC time, the georeferencing accuracy and the cloud cover."
] ]
}, },
{ {
@ -227,7 +234,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"**If you have already mapped the shorelines**, just load the output file by only running the section below" "**If you have already mapped the shorelines**, just load the output file (`output.pkl`) by running the section below"
] ]
}, },
{ {
@ -236,27 +243,11 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"filepath = os.path.join(os.getcwd(), 'data', sitename)\n", "filepath = os.path.join(inputs['filepath'], sitename)\n",
"with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:\n", "with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:\n",
" output = pickle.load(f) " " output = pickle.load(f) "
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -280,7 +271,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"**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:" "**Option 2**: the user can load the transect coordinates (make sure the spatial reference system is the same as defined previously by the parameter *output_epsg*) from a .geojson file by calling:"
] ]
}, },
{ {
@ -289,8 +280,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"kml_file = 'NARRA_transects.kml'\n", "geojson_file = os.path.join(os.getcwd(), 'examples', 'NARRA_transects.geojson')\n",
"transects = SDS_transects.load_transects_from_kml(kml_file)" "transects = SDS_tools.transects_from_geojson(geojson_file)"
] ]
}, },
{ {
@ -349,14 +340,14 @@
"gs = gridspec.GridSpec(len(cross_distance),1)\n", "gs = gridspec.GridSpec(len(cross_distance),1)\n",
"gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05)\n", "gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05)\n",
"for i,key in enumerate(cross_distance.keys()):\n", "for i,key in enumerate(cross_distance.keys()):\n",
" if np.all(np.isnan(cross_distance[key])):\n",
" continue\n",
" ax = fig.add_subplot(gs[i,0])\n", " ax = fig.add_subplot(gs[i,0])\n",
" ax.grid(linestyle=':', color='0.5')\n", " ax.grid(linestyle=':', color='0.5')\n",
" ax.set_ylim([-50,50])\n", " ax.set_ylim([-50,50])\n",
" if not i == len(cross_distance.keys()):\n",
" ax.set_xticks = []\n",
" ax.plot(output['dates'], cross_distance[key]- np.nanmedian(cross_distance[key]), '-^', markersize=6)\n", " ax.plot(output['dates'], cross_distance[key]- np.nanmedian(cross_distance[key]), '-^', markersize=6)\n",
" ax.set_ylabel('distance [m]', fontsize=12)\n", " ax.set_ylabel('distance [m]', fontsize=12)\n",
" ax.text(0.5,0.95,'Transect ' + key, bbox=dict(boxstyle=\"square\", ec='k',fc='w'), ha='center',\n", " ax.text(0.5,0.95, key, bbox=dict(boxstyle=\"square\", ec='k',fc='w'), ha='center',\n",
" va='top', transform=ax.transAxes, fontsize=14)\n", " va='top', transform=ax.transAxes, fontsize=14)\n",
"mng = plt.get_current_fig_manager() \n", "mng = plt.get_current_fig_manager() \n",
"mng.window.showMaximized() \n", "mng.window.showMaximized() \n",
@ -380,7 +371,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.7" "version": "3.7.3"
}, },
"varInspector": { "varInspector": {
"cols": { "cols": {

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requirements_linux64.txt
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# This file may be used to create an environment using:
# $ conda create --name <env> --file <this 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://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://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-7.3.0-hdf63c60_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-7.2.0-hdf63c60_3.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.8.0-1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-7.3.0-hdf63c60_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pandoc-2.6-1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.9-1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-hf484d3e_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/freexl-1.0.5-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/geos-3.7.1-hf484d3e_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/giflib-5.1.4-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-hf484d3e_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/icu-58.2-hf484d3e_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/json-c-0.13.1-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-hf484d3e_1005.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h14c3975_1004.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.16-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/mkl-2019.1-144.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.3-h9ac9557_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/openssl-1.0.2p-h14c3975_1002.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/pcre-8.42-h439df22_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pixman-0.34.0-h14c3975_1003.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/proj4-5.2.0-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/tzcode-2018g-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.0.9-h14c3975_1004.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.2-h14c3975_1007.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h14c3975_1007.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h14c3975_1004.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/boost-cpp-1.68.0-h11c811c_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-h9745a5d_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.13-h9a582f1_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.4-nompi_h11e915b_1105.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.36-h84994c4_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-h648cc4a_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.8-h143f9aa_1005.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/readline-7.0-hf8c457e_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-h84994c4_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.2.2-hac72e42_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.3-h4937e3b_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.2.5-hf484d3e_1006.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/freetype-2.9.1-h94bbf69_1005.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.4.3-h1105359_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/glib-2.56.2-had28632_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/kealib-1.4.10-he7154bc_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/krb5-1.16.3-hc83ff2d_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-h328b03d_1009.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.3.0-hf38bd82_1003.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.26.0-h67949de_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.6.7-h14c3975_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.2-h714fa37_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-h2176d3f_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.0-hb453b48_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libcurl-7.64.0-h01ee5af_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libpq-10.6-h13b8bad_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libspatialite-4.3.0a-hb5ec416_1026.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/python-3.6.7-hd21baee_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.3-h14c3975_1004.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.10-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.1.5-h14c3975_1002.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.12-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/asn1crypto-0.24.0-py36_1003.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/attrs-18.2.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/backcall-0.1.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cachetools-2.1.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/cairo-1.14.12-h80bd089_1005.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/certifi-2018.11.29-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/chardet-3.0.4-py36_1003.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cloudpickle-0.7.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/curl-7.64.0-h646f8bb_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/dask-core-1.1.1-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/decorator-4.3.2-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/docutils-0.14-py36_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/entrypoints-0.3-py36_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.0-hbbd80ab_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/httplib2-0.12.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/idna-2.8-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/imagesize-1.1.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jeepney-0.4-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.0.1-py36h6bb024c_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/lazy-object-proxy-1.3.1-py36h14c3975_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/markupsafe-1.1.0-py36h14c3975_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/mccabe-0.6.1-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/mistune-0.8.4-py36h14c3975_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.15.4-py36hde5b4d6_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.4.2-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/parso-0.3.3-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pickleshare-0.7.5-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/postgresql-10.6-h66cca7a_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.5.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/psutil-5.5.0-py36h14c3975_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/ptyprocess-0.6.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyasn1-0.4.4-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.5.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pycparser-2.19-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyflakes-2.1.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.3.1-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pysocks-1.6.8-py36_1002.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pytz-2018.9-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pyzmq-17.1.2-py36h6afc9c9_1001.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/qtpy-1.6.0-pyh8a2030e_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/rope-0.10.7-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/send2trash-1.5.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/simplejson-3.16.1-py36h470a237_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/simplekml-1.3.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/sip-4.18.1-py36hf484d3e_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/six-1.12.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-1.2.1-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-websupport-1.1.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/testpath-0.4.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/tornado-5.1.1-py36h14c3975_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/typed-ast-1.3.1-py36h14c3975_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.1.7-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.11.1-py36h14c3975_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/wurlitzer-1.0.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/babel-2.6.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/cffi-1.11.5-py36h9745a5d_1001.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.9.0.1-py36h14c3975_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/gobject-introspection-1.56.1-py36h9e29830_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-1.9.0-he243708_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/jedi-0.13.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libdap4-3.19.1-hd48c02d_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.6.2-hbdf4f91_1001.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/packaging-19.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pexpect-4.6.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pillow-5.4.1-py36h00a061d_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/poppler-0.67.0-h2fc8fa2_1002.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyasn1-modules-0.2.3-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pyrsistent-0.14.10-py36h14c3975_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.0-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/qt-5.6.3-h8bf5577_3.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/qtawesome-0.5.6-pyh8a2030e_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/rsa-3.4.2-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/setuptools-40.8.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/terminado-0.8.1-py36_1001.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/traitlets-4.3.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/uritemplate-3.0.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/astroid-2.1.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/atk-2.25.90-hf2eb9ee_1001.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/bleach-3.1.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/cryptography-2.5-py36hb7f436b_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.36.12-h4f1c04b_1001.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-auth-1.6.2-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/isort-4.3.4-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jinja2-2.10-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/jsonschema-3.0.0a3-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jupyter_core-4.4.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/libgdal-2.4.0-h982c1cc_1002.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/networkx-2.2-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/oauth2client-4.1.3-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pango-1.40.14-hf0c64fd_1003.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pygments-2.3.1-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.6.0-py36h13b7fb3_1008.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/wheel-0.32.3-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-auth-httplib2-0.0.3-py_2.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.31-h5baeb44_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jupyter_client-5.2.4-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/nbformat-4.4.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pip-19.0.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/prompt_toolkit-2.0.8-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pylint-2.2.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pyopenssl-19.0.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.1.1-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-api-python-client-1.7.8-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/ipython-7.2.0-py36h24bf2e0_1000.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/keyring-17.1.1-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/nbconvert-5.3.1-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/urllib3-1.24.1-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/earthengine-api-0.1.167-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/ipykernel-5.1.0-py36h24bf2e0_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/requests-2.21.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jupyter_console-6.0.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/notebook-5.7.4-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/qtconsole-4.4.3-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/sphinx-1.8.4-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/spyder-kernels-0.4.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/numpydoc-0.8.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/widgetsnbextension-3.4.2-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/ipywidgets-7.4.2-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/spyder-3.3.3-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/jupyter-1.0.0-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/gdal-2.4.0-py36h1c6dbfb_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/imageio-2.5.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.0.2-py36_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.0.2-py36h167e16e_1002.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/mkl_fft-1.0.10-py36h14c3975_1.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/mkl_random-1.0.2-py36h637b7d7_2.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.15.4-py36h7e9f1db_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.0.1-py36h3010b51_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.2.0-py36h7c811a0_0.tar.bz2
https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.14.2-py36hf484d3e_1.tar.bz2
https://repo.anaconda.com/pkgs/main/linux-64/scikit-learn-0.20.2-py36hd81dba3_0.tar.bz2

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requirements_osx64.txt
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# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: osx-64
@EXPLICIT
https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.6-1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2018.03.07-0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/expat-2.2.5-hfc679d8_2.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/freexl-1.0.5-h470a237_2.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/geos-3.7.0-hfc679d8_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/gettext-0.19.8.1-h1f1d5ed_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/giflib-5.1.4-h470a237_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/icu-58.2-hfc679d8_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2019.1-144.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/jpeg-9c-h470a237_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/json-c-0.13.1-h470a237_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libcxxabi-4.0.1-hcfea43d_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-3.0.1-h93005f0_2.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.15-h470a237_3.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libsodium-1.0.16-h3efe00b_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pandoc-2.2.3.2-0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/pcre-8.41-hfc679d8_3.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/pixman-0.34.0-h470a237_3.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/poppler-data-0.4.9-0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/proj4-5.2.0-h470a237_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/xz-5.2.4-h1de35cc_4.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.11-h1de35cc_3.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/boost-cpp-1.68.0-h3a22d5f_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/hdf4-4.2.13-h951d187_2.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/hdf5-1.10.4-nompi_h5598ddc_1003.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libcxx-4.0.1-hcfea43d_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.35-ha441bb4_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.0.10-he6b73bb_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.9.8-h422b904_5.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/mkl-2018.0.3-1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/openssl-1.0.2p-h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.8-ha441bb4_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/xerces-c-3.2.2-h5d6a6da_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.9.1-hb4e5f40_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/geotiff-1.4.2-h9c44c65_7.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/kealib-1.4.10-heffcb4b_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.2.1-h475c297_4.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libkml-1.3.0-he469717_9.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libssh2-1.8.0-h5b517e9_3.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.1-h0a44026_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.3.0-h316dc23_3.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/zeromq-4.2.5-h0a44026_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/fontconfig-2.13.1-hce039c3_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/glib-2.56.2-h464dc38_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/libedit-3.1.20170329-hb402a30_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/readline-7.0-h1de35cc_5.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/cairo-1.14.12-h276e583_5.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/dbus-1.13.2-h760590f_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/krb5-1.16.2-hbb41f41_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libpq-10.5-hf16a0db_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/sqlite-3.26.0-hb1c47c0_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libcurl-7.62.0-hbdb9355_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libspatialite-4.3.0a-h201a3a7_25.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/postgresql-10.5-ha408888_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/python-3.6.6-h5001a0f_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/qt-5.9.6-h45cd832_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/alabaster-0.7.12-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/appnope-0.1.0-py36hf537a9a_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/asn1crypto-0.24.0-py36_1003.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/backcall-0.1.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cachetools-2.1.0-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/certifi-2018.10.15-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/chardet-3.0.4-py36_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cloudpickle-0.6.1-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/cryptography-vectors-2.3.1-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/curl-7.62.0-h74213dd_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/dask-core-1.0.0-py_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/decorator-4.3.0-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/docutils-0.14-py36hbfde631_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/entrypoints-0.2.3-py36_2.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/httplib2-0.12.0-py36_1000.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/idna-2.8-py36_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/imagesize-1.1.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ipython_genutils-0.2.0-py36h241746c_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.0.1-py36h0a44026_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/lazy-object-proxy-1.3.1-py36h1de35cc_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/markupsafe-1.1.0-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/mccabe-0.6.1-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/mistune-0.8.4-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.15.4-py36h8a80b8c_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/olefile-0.46-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pandocfilters-1.4.2-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/parso-0.3.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pickleshare-0.7.5-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/prometheus_client-0.4.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/psutil-5.4.8-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ptyprocess-0.6.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyasn1-0.4.4-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pycodestyle-2.4.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pycparser-2.19-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pyflakes-2.0.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-2.3.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/python.app-2-py36_9.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pytz-2018.7-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pyzmq-17.1.2-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/qtpy-1.5.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/free/osx-64/requests-2.14.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/rope-0.11.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/send2trash-1.5.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/simplejson-3.16.1-py36h470a237_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/simplekml-1.3.0-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/sip-4.19.8-py36h0a44026_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/six-1.11.0-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/snowballstemmer-1.2.1-py36h6c7b616_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/sphinxcontrib-1.0-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/testpath-0.4.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/toolz-0.9.0-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/tornado-5.1.1-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/typed-ast-1.1.0-py36h1de35cc_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/wcwidth-0.1.7-py36h8c6ec74_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/webencodings-0.5.1-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/wrapt-1.10.11-py36h1de35cc_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/wurlitzer-1.0.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/astroid-2.1.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/babel-2.6.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/cffi-1.11.5-py36h5e8e0c9_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/cycler-0.10.0-py36hfc81398_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/cytoolz-0.9.0.1-py36h470a237_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jedi-0.13.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/keyring-16.1.1-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libdap4-3.19.1-h18059cb_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libnetcdf-4.6.2-h45f6246_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/packaging-18.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pexpect-4.6.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/pillow-5.3.0-py36hc736899_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/poppler-0.67.0-hdf8a1b3_2.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyasn1-modules-0.2.1-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pyqt-5.9.2-py36h655552a_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.7.5-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/qtawesome-0.5.3-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/rsa-3.4.2-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/setuptools-40.6.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/sphinxcontrib-websupport-1.1.0-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/terminado-0.8.1-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/traitlets-4.3.2-py36h65bd3ce_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/uritemplate-3.0.0-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/bleach-3.0.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/cryptography-2.3.1-py36hdffb7b8_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-auth-1.6.1-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/isort-4.3.4-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jinja2-2.10-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jsonschema-2.6.0-py36hb385e00_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jupyter_core-4.4.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/libgdal-2.3.2-h42efa9e_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/networkx-2.2-py_1.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/oauth2client-4.1.2-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pygments-2.2.0-py36h240cd3f_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.32.3-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-auth-httplib2-0.0.3-py_2.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jupyter_client-5.2.3-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/nbformat-4.4.0-py36h827af21_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pip-18.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/prompt_toolkit-2.0.7-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/pylint-2.2.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/pyopenssl-18.0.0-py36_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/sphinx-1.8.2-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/google-api-python-client-1.7.5-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ipython-7.2.0-py36h39e3cac_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/nbconvert-5.3.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/numpydoc-0.8.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/earthengine-api-0.1.152-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ipykernel-5.1.0-py36h39e3cac_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jupyter_console-6.0.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/notebook-5.7.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/qtconsole-4.4.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/spyder-kernels-0.3.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/widgetsnbextension-3.4.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/ipywidgets-7.4.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/jupyter-1.0.0-py36_7.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/gdal-2.3.2-py36hfc77a4a_1.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/imageio-2.4.1-py36_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.0.1-py36h54f8f79_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.0.6-py36hb8a8100_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.0.1-py36h5d10147_1.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.15.4-py36h6a91979_0.tar.bz2
https://conda.anaconda.org/conda-forge/osx-64/pywavelets-1.0.1-py36h7eb728f_0.tar.bz2
https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.1.0-py36h28f7352_1.tar.bz2
https://conda.anaconda.org/anaconda/osx-64/scikit-image-0.14.0-py36h0a44026_1.tar.bz2
https://conda.anaconda.org/anaconda/osx-64/scikit-learn-0.20.1-py36h4f467ca_0.tar.bz2

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requirements_win64.txt
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# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: win-64
@EXPLICIT
https://repo.anaconda.com/pkgs/main/win-64/blas-1.0-mkl.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/ca-certificates-2018.03.07-0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/icc_rt-2017.0.4-h97af966_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/intel-openmp-2019.1-144.tar.bz2
https://repo.anaconda.com/pkgs/msys2/win-64/msys2-conda-epoch-20160418-1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pandoc-2.2.3.2-0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/vs2015_runtime-14.15.26706-h3a45250_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/winpty-0.4.3-4.tar.bz2
https://repo.anaconda.com/pkgs/msys2/win-64/m2w64-gmp-6.1.0-2.tar.bz2
https://repo.anaconda.com/pkgs/msys2/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/mkl-2018.0.3-1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/vc-14.1-h0510ff6_4.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/expat-2.2.5-he025d50_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/geos-3.6.2-h9ef7328_2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/icu-58.2-ha66f8fd_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/jpeg-9b-hb83a4c4_2.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/libiconv-1.15-h1df5818_7.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libsodium-1.0.16-h9d3ae62_0.tar.bz2
https://repo.anaconda.com/pkgs/msys2/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/openssl-1.0.2p-hfa6e2cd_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/proj4-4.9.3-hcf24537_7.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/python-3.6.7-h33f27b4_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/sqlite-3.25.3-he774522_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/tk-8.6.8-hfa6e2cd_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/xerces-c-3.2.2-ha925a31_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/xz-5.2.4-h2fa13f4_4.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/zlib-1.2.11-h62dcd97_3.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/alabaster-0.7.12-py36_0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/asn1crypto-0.24.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/backcall-0.1.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/cachetools-2.1.0-py_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/certifi-2018.10.15-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/chardet-3.0.4-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/cloudpickle-0.6.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/colorama-0.4.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/dask-core-1.0.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/decorator-4.3.0-py36_0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/docutils-0.14-py36h6012d8f_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/entrypoints-0.2.3-py36_2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/freexl-1.0.5-hfa6e2cd_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/hdf4-4.2.13-h712560f_2.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/hdf5-1.10.1-h98b8871_1.tar.bz2
https://conda.anaconda.org/conda-forge/win-64/httplib2-0.12.0-py36_1000.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/idna-2.7-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/imagesize-1.1.0-py36_0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/ipython_genutils-0.2.0-py36h3c5d0ee_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/kiwisolver-1.0.1-py36h6538335_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/krb5-1.16.1-h038dc86_6.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/lazy-object-proxy-1.3.1-py36hfa6e2cd_2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libboost-1.67.0-hd9e427e_4.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libpng-1.6.35-h2a8f88b_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libssh2-1.8.0-hd619d38_4.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libtiff-4.0.9-h36446d0_2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/libxml2-2.9.8-hadb2253_1.tar.bz2
https://repo.anaconda.com/pkgs/msys2/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/markupsafe-1.1.0-py36he774522_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/mccabe-0.6.1-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/mistune-0.8.4-py36he774522_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/numpy-base-1.15.4-py36h8128ebf_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/olefile-0.46-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pandocfilters-1.4.2-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/parso-0.3.1-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pickleshare-0.7.5-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/prometheus_client-0.4.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/psutil-5.4.8-py36he774522_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/pyasn1-0.4.4-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pycodestyle-2.4.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pycparser-2.19-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pyflakes-2.0.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pyparsing-2.3.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pytz-2018.7-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/pywin32-223-py36hfa6e2cd_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/qtpy-1.5.2-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/rope-0.11.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/send2trash-1.5.0-py36_0.tar.bz2
https://conda.anaconda.org/conda-forge/win-64/simplejson-3.16.1-py36hfa6e2cd_0.tar.bz2
https://conda.anaconda.org/conda-forge/noarch/simplekml-1.3.0-py_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/sip-4.19.8-py36h6538335_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/six-1.11.0-py36_1.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/snowballstemmer-1.2.1-py36h763602f_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/sphinxcontrib-1.0-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/testpath-0.4.2-py36_0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/toolz-0.9.0-py36_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/tornado-5.1.1-py36hfa6e2cd_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/typed-ast-1.1.0-py36hfa6e2cd_0.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/wcwidth-0.1.7-py36h3d5aa90_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/webencodings-0.5.1-py36_1.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/win_inet_pton-1.0.1-py36_1.tar.bz2
https://repo.continuum.io/pkgs/main/win-64/wincertstore-0.2-py36h7fe50ca_0.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/wrapt-1.10.11-py36hfa6e2cd_2.tar.bz2
https://repo.anaconda.com/pkgs/main/win-64/zeromq-4.2.5-he025d50_1.tar.bz2
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