rivabar 0.1.1

A Python package to automatically extract channel centerlines and banklines from water index images of rivers

rivabar

Description

rivabar is a Python package that aims to automatically extract channel centerlines and banklines from water index images of rivers. The focus is on getting good representations of the banklines, as centerlines are not physical features and how they are exactly derived is subjective. This is achieved in part by viewing channels as boundaries between polygons that correspond to islands or bars, as this allows us to take advantage of algorithms developed for spatial analysis. In this view, even a single-thread channel becomes the boundary between two land domains.

rivabar relies on the following Python packages, among others:

• networkx to work with graphs
• sknw for converting the channel skeleton into a graph
• Python Spatial Analysis Library (libpysal) to get neighborhood relations between banks / islands
• Urban Morphology Measuring Toolkit (momepy) to create clean centerline graphs

rivabar can be used to map single-thread and multithread rivers, in an almost entirely automated fashion. It requires a water mask as input and a start (source) and end (sink) points for the channel / channel belt / delta.

The images below illustrate how rivabar extracts both centerlines and banklines from a Landsat image of the Brahmaputra River, and creates an island neighborhood graph in addition to the centerline graph.

Brahmaputra River - water index

Rook graph and centerline polygons

Centerline graph and bar polygons

Installation

You can install rivabar directly from PyPI:

pip install rivabar

Alternatively, you can install from source:

git clone https://github.com/zsylvester/rivabar.git
cd rivabar
pip install -e .

Getting started

Here are some basic examples of how to use rivabar:

Basic Usage

import rivabar as rb

# Extract channel centerlines and banklines
# Define start and end points of the channel you want to extract
start_x, start_y = 675796.2, 98338.8 # UTM coordinates of the channel start
end_x, end_y = 628190.3, -91886.6 # UTM coordinates of the channel end
fname="LC08_L2SP_232060_20140219_20200911_02_T1_SR", # assumes that the Landsat bands are located in a folder with this name
dirname="../data/Branco/", # parent folder of the 'LC08...' folder


# Extract the channel centerline and related graphs
D_primal, G_rook, G_primal, mndwi, dataset, left_utm_x, right_utm_x, lower_utm_y, upper_utm_y, xs, ys = rb.extract_centerline(
    fname=fname,
    dirname=dirname,
    start_x=start_x,
    start_y=start_y,
    end_x=end_x,
    end_y=end_y,
    file_type='multiple_tifs',
    flip_outlier_edges=True,
    mndwi_threshold=0.0,
    ch_belt_smooth_factor=1e8,
    ch_belt_half_width=2000,
    remove_smaller_components=True,\
    delete_pixels_polys=False,
    small_hole_threshold=64,
    solidity_filter=False,
    plot_D_primal=True
)

# Save the extracted centerlines and banklines as shapefiles
rb.save_shapefiles(
    dirname="output_directory",
    fname="output_prefix",
    G_rook=G_rook,
    dataset=dataset
)

Analyzing Channel Widths and Morphology

# Get the main path through the channel network
edge_path = rb.get_main_path(D_primal)

# Analyze channel width - wavelength scaling
df, curv, s, loc_zero_curv, xsmooth, ysmooth = rb.analyze_width_and_wavelength(
    D_primal=D_primal,
    main_path=edge_path,
    ax,
    delta_s=5,
    smoothing_factor=0.5*1e7,
    min_sinuosity=1.1,
    dx=30
)

# Extract and plot channel widths along main path
xl, yl, w1l, w2l, w, s = rb.get_channel_widths_along_path(D_primal, D_primal.graph['main_path'])
plt.figure(figsize=(12, 4))
plt.plot(s, np.array(w)*30.0)
plt.xlabel('along-channel distance (m)')
plt.ylabel('channel width (m)');

For more examples and detailed usage, check out the example notebooks in the notebooks directory.

License

This project is licensed under the MIT License - see the LICENSE file for details.