sandpyper 0.1.0

Tools for automatic UAV-SfM beach volumetric and behavioural analysis.

- Tools for automatic UAV-SfM beach volumetric and behavioural analysis -

Sandpyper performs an organised and automatic extraction of elevation profiles from as many DSM and orthophotos as you like. It is thought to be used when a considerable number of DSMs and orthohpotos from many different locations and coordinate reference systems need to be processed. Then, computes volumetric and behavioural analysis of beachfaces, speeding up an otherwise long and difficult to handle (big rasters) job. It has some specialised functions to deal with the common limitations found in beach environments:

1. Swash zone: the water motion as waves wash in and out of the swash zone prevent Structure from Motion algorithm to find matches, thus, model elevation.
2. Vegetation: both dune vegetation and beach wracks (macroalgae, woody debris) should be removed or filetered as can compromise sediment volumetric computation.
3. File size: a few km long beach surveyed with a Phantom 4-Advanced at 100m altitude create roughly 10 Gb (uncompressed) of data, which can be cumbersome for some GIS to handle.

From user-defined cross-shore transects, you can clean profiles from unwanted non-sand points, detect significant hotspots/coldspots (cluster) of beach change, compute volumetric dynamics at the site and transect levels, plot alongshore change and model beachface behaviour using the Beachface Cluster Dynamics indices.

Plus, some outils functions that can come at hand, like automatic transect creation from a vector line, grid creation along a line and subsequent tiles extraction and others.

Follow the Jupyter Notebook tutorials (IN PREPARATION) to understand how it works!

This code has supported the analysis and publication of the article "Citizen science for monitoring seasonal-scale beach erosion and behaviour with aerial drones", in the open access Nature Scientific Report journal.

Explore the docs »
View Demo · Report Bug · Request Feature

Table of Contents

1. About The Project
   • Background
   • Modules
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Publications
9. Acknowledgements

About The Project

Background

This code has been developed to ease the analysis of big data coming from more than 300 Unnmanned Aerial Vehicles (UAV) surveys, performed by Citizen Scientist in Victoria, Australia. This is the Eureka Award Winning World-first beach monitoring program powered by volunteers, who autonomously fly UAVs on 15 sensitive sites (erosional hotspots) across the Victorian coast, every 6 weeks for 3 years, since 2018. This project is part of a broader marine mapping program called The Victorian Coastal Monitoring Program (VCMP), funded by the Victorian Department of Environment, Land, Water and Planning, co-funded by Deakin University and The University of Melbourne.

Each survey creates Digital Surface Models and orthophotos of considerable size (5-10 Gb uncompressed), which can be troublesome for some GIS to render, let alone perform rster-based computations.

Modules

• .outils: main storage of useful functions.
• .profile: extract elevation and colour information across profiles from a folder of rasters.
• .labels: use KMean and Silhouette analysis to help clean the extracted elevation data from unwanted non-sand points.
• .hotspot: use Local Moran's I to disscard spatial outliers and obtain significant hot/cold spots of beach change at site and transect levels.
• .dynamics: compute Beachface CLuster Dynamics indices at the site and transect levels.
• .volumetrics: compute volumetric timeseries of sand-only points and create plots.
• .space (under development): simple satellite and UAV instantaneous waterlines tidal-correction and shoreline shifts/error metrics.

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Install Conda in your local machine. We need it to create the "sandpyper_env" virtual environment.
• then, if you do not have it already, install Visual Studio C++ build tools in your local machine, which is needed to install the richdem package. You can download it here.
• If you don't have it already, add conda-forge channel to your anaconda config file, by typing this in your Anaconda Prompt terminal (base environment):

  conda config --add channels conda-forge
  

• Now, always in the (base) environment, create a new environment called sandpyper_env and install the required packages by typing:

  conda create --name sandpyper_env geopandas=0.8.2 matplotlib=3.3.4 numpy=1.20.1 pandas=1.2.2 tqdm pysal=2.1 rasterio=1.2.0 richdem=0.3.4 scikit-image=0.18.1 scikit-learn=0.24.1 scipy=1.6 seaborn=0.11.1 tqdm=4.56.2
  

• If rasterio package cannot be installed due to GDAL binding issues, follow the instructions in rasterio installation webpage.

Installation

1. conda activate sandpyper_env
   

2. pip install sandpyper
   

3. Install Jupyter Notebooks:

   conda install jupyter notebook
   

4. Once you open a Jupyter Notebook with the sandpyper_env, import it to test it works.

   import sandpyper
   

Usage

TO DO.

For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Nicolas Pucino - @NicolasPucino - [e-mail]

Project Link: https://github.com/npucino/sandpyper

Publications

• Pucino, N., Kennedy, D. M., Carvalho, R. C., Allan, B. & Ierodiaconou, D. Citizen science for monitoring seasonal-scale beach erosion and behaviour with aerial drones. Sci. Rep. 11, 3935 (2021)

Acknowledgements

• Funding provided by Deakin University and the Victorian Department of Environment, Land, Water and Planning
• Deakin University Citizen Science program webpage
• A/Prof Daniel Ierodiaconou, my PhD supervisor and the Deakin Marine Mapping team