GeoNet

Geomorphic feature extraction from high resolution topography data.

When using GeoNet, please cite the following papers:

Passalacqua, P., T. Do Trung, E. Foufoula-Georgiou, G. Sapiro, W. E. Dietrich (2010), A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths, Journal of Geophysical Research Earth Surface, 115, F01002, doi:10.1029/2009JF001254.

Sangireddy, H., R. A. Carothers, C.P. Stark, P. Passalacqua (2016), Controls of climate, topography, vegetation, and lithology on drainage density extracted from high resolution topography data, Journal of Hydrology, 537, 271-282, doi:10.1016/j.jhydrol.2016.02.051.

Environment

conda env create -f GeoNetEnv.yml

Scikit-FMM Install

git clone https://github.com/scikit-fmm/scikit-fmm.git

cd scikit-fmm

python setup.py install

Note: This was done assuming scikit-fmm was cloned into your working directory.

Configuration and Setup

Create a configuration file for your project using:

python pygeonet_configure.py

Optional Arguments:

-dir </path/to/GeoNet/Home_Directory>

-p [projectName]

-n [DEM_Name]

--input_dir [Input_Directory_Name]

--output_dir [Output_Directory_Name]

Create a file structure based on the previous inputs:

python pygeonet_prepare.py

Default File Structure:

• GeoNet

  • GeoInputs
    • GIS
      • Project Name (-p from configure step)
        • dem.tif
  • GeoOutputs
    • GIS
      • Project Name (-p from configure step)
        • dem.tif
  • *** configuration file ***

  Scripts

  1. Perona-Malik non-linear, diffusion filter:

  python pygeonet_nonlinear_filter.py

  2. Slope and Curvature:

  python pygeonet_slope_curvature.py

  3. Flow Direction, Flow Accumulation, Outlets, and Basins

  python pygeonet_grass_py2.py or python pygeonet_grass_py2.py

  If you have GRASS GIS 7.6 installed, used the first command. If you have GRASS GIS 7.8 installed, used the second command

  4. Flow Accumulation and Curvature Skeleton

  python pygeonet_skeleton_definition.py

  5. Geodesic Minimum Cost Path and Fast Marching Algorithm

  python pygeonet_fast_marching.py

  6. Channel Head Detection

  python pygeonet_channel_head_definition.py

  Note: Further research still needs to be done on the optimal threshold for identifying channel heads. Preliminary studies found a threshold of 0.3 to be sufficient, but this estimate can definitely be improved using analytical methods.