Pytopocomplexity

Final update of the JOSS manuscript & minor corrections in the README.md and example notebooks.

**pyTopoComplexity** is an open-source Python package designed to measure the topographic complexity (i.e., surface roughness) of land surfaces using digital elevation model (DEM) data. This package includes modules for three methods commonly used in the fields of geomorphology and oceanography for measuring topographic complexity, which are not fully available in Geographic Information System (GIS) software like QGIS.

| Modules | Classes | Method Descriptions |
| ------------- | ------------- | ------------- |
| pycwtmexhat.py | CWTMexHat | Quanitfy the wavelet-based curvature of the terrain surface using two-dimensional continuous wavelet transform (2D-CWT) with a Mexican Hat wevalet |
| pyfracd.py | FracD | Conduct fractal dimension analysis on the terrain surface using variogram procedure |
| pyrugostiy.py | RugosityIndex | Calculate rugosity index of the terrain surface |

In this repository, each module has a corresponding example Jupyter Notebook file that includes detailed instructions on module usage and brief explanations of the applied theories with cited references. Example raster file data are included in the `~/example/` folder.

There is also an additional Jupyter Notebook, **nonlineardiff_Landlab.ipynb**, which leverages the power of [Landlab](https://landlab.readthedocs.io/en/latest/index.html) to perform forward simulation of landscape smoothing through non-linear hillslope diffusion process.

Installation


pip install pytopocomplexity


Citation

A manuscript is being prepared for submission to the [Journal of Open Source Software](https://joss.theoj.org). If you use **pyTopoComplexity** and the associated Jupyter Notebooks in your work, please consider citing the paper once it is accepted. In the meantime, users may cite the [Zenodo DOI](https://doi.org/10.5281/zenodo.11239338)).

Modules for Surface Complexity Measurement

1. Two-Dimensional Continuous Wavelet Transform Analysis

python
from pytopocomplexity import CWTMexHat


The module **pycwtmexhat.py** uses two-dimensional continuous wavelet transform (2D-CWT) with a Mexican Hat wevalet to measure the topographic complexity (i.e., surface roughness) of a land surface from a Digital Elevation Model (DEM). Such method quanitfy the wavelet-based curvature of the surface, which has been proposed to be a effective geomorphic metric for identifying and estimating the ages of historical deep-seated landslide deposits.

The method and early version of the code was developed by Dr. Adam M. Booth (Portland State Univeristy) in [2009](https://doi.org/10.1016/j.geomorph.2009.02.027), written in MATLAB (source code available from [Booth's personal website](https://web.pdx.edu/~boothad/tools.html)). This MATLAB code was later revised and adapted by Dr. Sean R. LaHusen (Univeristy of Washington) and Dr. Erich N. Herzig (Univeristy of Washington) in their research ([LaHusen et al., 2020](https://doi.org/10.1126/sciadv.aba6790); [Herzig et al. (2023)](https://doi.org/10.1785/0120230079)). Dr. Larry Syu-Heng Lai (Univeristy of Washington), under the supervision of Dr. Alison R. Duvall (Univeristy of Washington), translated the code into this optimized open-source Python version in 2024.

See **pycwtmexhat_example.ipynb** for detailed explanations and usage instructions.

2. Fractal Dimentsion Analysis

python
from pytopocomplexity import FracD


The **pyfracd.py** module calculates local fractal dimensions to assess topographic complexity. It also computes reliability parameters such as the standard error and the coefficient of determination (R²). The development of this module was greatly influenced by the Fortran code shared by Dr. Eulogio Pardo-Igúzquiza from his work in [Pardo-Igúzquiza and Dowd (2022)](https://doi.org/10.1016/j.icarus.2022.115109).

The local fractal dimension is determined by intersecting the surface within a moving window with four vertical planes in principal geographical directions, simplifying the problem to one-dimensional topographic profiles. The fractal dimension of these profiles is estimated using the variogram method, which models the relationship between dissimilarity and distance using a power-law function. While the fractal dimension value does not directly scale with the degree of surface roughness, smoother or more regular surfaces generally have lower fractal dimension values (closer to 2), whereas surfaces with higher fractal dimension values tend to be more complex or irregular. This method has been applied in terrain analysis for understanding spatial variability in surface roughness, classifying geomorphologic features, uncovering hidden spatial structures, and supporting geomorphological and geological mapping on Earth and other planetary bodies.

See **pyfracd_example.ipynb** for detailed explanations and usage instructions.

3. Rugosity Index Calculation

python
from pytopocomplexity import RugosityIndex


The module **pyrugosity.py** measure rugosity index of the land surface, which is widely used to assess landscape structural complexity.

By definition, the rugosity index has a minimum value of one, representing a completely flat surface. Typical values of the conventional rugosity index without slope correction ([Jenness, 2004](https://onlinelibrary.wiley.com/doi/abs/10.2193/0091-7648%282004%29032%5B0829%3ACLSAFD%5D2.0.CO%3B2)) range from one to three, although larger values are possible in very steep terrains. The slope-corrected rugosity index, also known as the Arc-Chord Ratio (ACR) rugosity index ([Du Preez, 2015](https://doi.org/10.1007/s10980-014-0118-8)), provides a better representation of local surface complexity. This method has been applied in classifying seafloor types by marine geologists and geomorphologists, studying small-scale hydrodynamics by oceanographers, and assessing available habitats in landscapes by ecologists and coral biologists.

See **pyrugosity_example.ipynb** for detailed explanations and usage instructions.

Forward Simulation of Landscape Smoothing through Nonlinear Hillslope Diffusion Process

In the `~/example/` folder, the Jupyter Notebook file **nonlineardiff_Landlab.ipynb** demonstrates the use of [Landlab](https://landlab.readthedocs.io/en/latest/index.html), an open-source Python framework for simulating landscape evolution, to model topographic smoothing driven by near-surface soil disturbance and downslope soil creep processes. Specifically, this notebook employs the [`TaylorNonLinearDiffuser`](https://landlab.readthedocs.io/en/latest/reference/components/taylor_nonlinear_hillslope_flux.html) component from [Landlab](https://landlab.readthedocs.io/en/latest/index.html), described as one element in the [`terrainBento`](https://github.com/TerrainBento/terrainbento) package, developed by [Barnhart et al. (2019)](https://gmd.copernicus.org/articles/12/1267/2019/), to simulate topographic smoothing over time through non-linear hillslope diffusion processes ([Roering et al., 1999](https://doi.org/10.1029/1998WR900090)).

Users need to define the diffusion coefficient (K) for the simulation. The code will automatically detect the units of the XYZ directions (must be in feet or meters) of the input DEM raster file and convert the unit for K accordingly.


Example DEM Raster Files

This repository include example LiDAR DEM files under `~/example/ExampleDEM/` that cover the area and nearby region of a deep-seated landslide occurred in 2014 at Oso area of the North Fork Stillaguamish River (NFSR) valley, Washington State, USA. The souce LiDAR DEM files were compiled from 'Stillaguamish 2014' and 'Snohoco Hazel 2006' projects that was originally contracted by Washington State Department of Transportation (WSDOT), downloaded from the [Washington Lidar Portal](http://lidarportal.dnr.wa.gov) on April 4, 2024.

A goal of this work allow users to reproduce the research by [Booth et al. (2017)](https://doi.org/10.1002/2016JF003934) and permit comparison of topographic complexity metrics derived from other regions using **pyTopoComplexity** package and the **nonlineardiff_Landlab.ipynb** simulation tools presented in this repository.

The example DEM raster files have various grid size, coordinate reference system (CRS), and unit of grid value (elevation, Z).

| LiDAR DEM Files | CRS | XY Grid Size | Z Unit | Descriptions |
| ------------- | ------------- | ------------- | ------------- | ------------- |
| Ososlid2014_f_3ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 3.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_m_3ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~0.9144 [meters] | meters | 2014 Oso Landslide |
| Ososlid2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_m_6ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~1.8288 [meters] | meters | 2014 Oso Landslide |
| Osoarea2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide & nearby NFSR valley |

> [!NOTE]
> When testing the code with the example DEM files, users should place the entire `~/ExampleDEM/` subfolder in the same directory as the Jupyter Notebook files. Both the **pyTopoComplexity** package and the **nonlineardiff_landlab.ipynb** land-smoothing modeling tool have the capability to automatically detect the grid spacing and the units of the XYZ directions (must be in feet or meters) of the input DEM raster and compute the results in SI units.

Requirements
For **pyTopoComplexity** package:
* Python >= 3.10
* `numpy` >= 1.24
* `scipy` >= 1.10
* `rasterio` >= 1.3
* `dask` >= 2024.3
* `matplotlib` >= 3.7
* `tqdm` >= 4.66
* `numba` >= 0.57
* `statsmodels` >= 0.14

Additional packages for Jupyter Notebook examples:
* `pandas` >= 2.1
* `jupyter` >= 1.0

For landscape smoothing simulation:
* [`landlab`](https://landlab.readthedocs.io/en/latest/index.html) >= 2.7
* Used components: `TaylorNonLinearDiffuser`, `esri_ascii`, `imshowhs`
* `gdal`>= 3.6
* `ipywidgets` >= 8.1 [optional for interactive visualization]

See also the `environment.yml` file which can be used to create a virtual environment.

License
**pyTopoComlexity** is licensed under the [Apache License 2.0](LICENSE).

[](https://doi.org/10.5281/zenodo.11239338)

`pytopocomplexity` is an open-source Python package designed to measure the topographic complexity (i.e., surface roughness) of land surfaces using digital elevation model (DEM) data. This package includes modules for three methods commonly used in the fields of geomorphology and oceanography for measuring topographic complexity, which are not fully available in Geographic Information System (GIS) software like QGIS.

| Modules | Method Descriptions |
| ------------- | ------------- |
| pycwtmexhat.py | Quanitfy the wavelet-based curvature of the land surface using two-dimensional continuous wavelet transform (2D-CWT) with a Mexican Hat wevalet |
| pyfracd.py | Conduct fractal dimension analysis on the land surface |
| pyrugostiy.py | Calculate rugosity indext of the land surface |

In this GitHub repository, each module has a corresponding example Jupyter Notebook file that includes detailed instructions on module usage and brief explanations of the applied theories with cited references. Example raster file data are included in the `~/example/` folder.

There is also an additional Jupyter Notebook, `nonlineardiff_landlab.ipynb`, which leverages the power of [`landlab`](https://landlab.readthedocs.io/en/latest/index.html) to perform forward simulation of landscape smoothing through non-linear hillslope diffusion process.

> [!CAUTION]
> This package is under developement and has not officially released. Use it with caution.

Installation


pip install pytopocomplexity


Citation

The current version is still a pre-release. If you use the current version of `pytopocomplexity` and associated Jupyter Notebooks (including nonlinear diffusion simulation) in your work, please cite the Zenodo DOI:
* Lai, L. S.-H. (2024). pyTopoComplexity. Zenodo. https://doi.org/10.5281/zenodo.11239338

Modules for Surface Complexity Measurement

1. `pycwtmexhat`: 2D Continuous Wavelet Transform Method

python
from pytopocomplexity import pycwtmexhat


The module `pycwtmexhat` uses two-dimensional continuous wavelet transform (2D-CWT) with a Mexican Hat wevalet to measure the topographic complexity (i.e., surface roughness) of a land surface from a Digital Elevation Model (DEM). Such method quanitfy the wavelet-based curvature of the surface, which has been proposed to be a effective geomorphic metric for identifying and estimating the ages of historical deep-seated landslide deposits.

The method and early version of the code was developed by Dr. Adam M. Booth (Portland State Univeristy) in [2009](https://doi.org/10.1016/j.geomorph.2009.02.027), written in MATLAB (Source code available from [Booth's personal website](https://web.pdx.edu/~boothad/tools.html)). This MATLAB code was later revised and adapted by Dr. Sean R. LaHusen (Univeristy of Washington) and Dr. Erich N. Herzig (Univeristy of Washington) in their research ([LaHusen et al., 2020](https://doi.org/10.1126/sciadv.aba6790); [Herzig et al. (2023)](https://doi.org/10.1785/0120230079)). Dr. Larry Syu-Heng Lai (Univeristy of Washington), under the supervision of Dr. Alison R. Duvall (Univeristy of Washington), translated the code into this optimized open-source Python version in 2024.

See `pycwtmexhat_example.ipynb` for detailed explanations and usage instructions.

2. `pyfracd`: Fractal Dimentsion Analysis

python
from pytopocomplexity import pyfracd


The `pyfracd` module calculates local fractal dimensions to assess topographic complexity. It also computes reliability parameters such as the standard error and the coefficient of determination (R²). The development of pyfracd is made possible through the gratitude of Dr. Eulogio Pardo-Iguzquiza, who kindly shared his Fortran code used in his recent publication [Pardo-Igúzquiza and Dowd (2022)](https://doi.org/10.1016/j.icarus.2022.115109).

The local fractal dimension is determined by intersecting the surface within a moving window with four vertical planes in principal geographical directions, simplifying the problem to one-dimensional topographic profiles. The fractal dimension of these profiles is estimated using the variogram method, which models the relationship between dissimilarity and distance using a power-law function. While the fractal dimension value does not directly scale with the degree of surface roughness, smoother or more regular surfaces generally have lower fractal dimension values (closer to 2), whereas surfaces with higher fractal dimension values tend to be more complex or irregular. This method has been applied in terrain analysis for understanding spatial variability in surface roughness, classifying geomorphologic features, uncovering hidden spatial structures, and supporting geomorphological and geological mapping on Earth and other planetary bodies.

See `pyfracd_example.ipynb` for detailed explanations and usage instructions.

3. `pyrugosity`: Rugosity Index

python
from pytopocomplexity import pyrugosity


The module `pyrugosity` measure rugosity index of the land surface, which is widely used to assess landscape structural complexity. The development of this module is influenced by another open-source tool [`Rugosity_Calculator`](https://github.com/drk944/Rugosity_Calculator) created by [drk944](https://github.com/drk944).

The rugosity index is determined as the ratio of the real surface area to the geometric surface area, highlighting smaller-scale variations in surface height. This module adapt triangulated irregular networks method ([Jenness, 2004](https://doi.org/10.2193/0091-7648(2004)032[0829:CLSAFD]2.0.CO;2)), which approximate the surface area of with within each 9 cell as the sum of 8 truncated-triangle area connecting each cell centerpoint with the centerpoints of the 8 surrounding cells. The geometric surface area is assumed to be the planimetric area of the center cell. By definition, the rugosity index is as a minimum value of one (completely flate surface). Typical valuesrange from one to three although larger values are possible in very steep terrains. Such method has been applied in classifying seafloor types by marine geologists and geomorphologist, small-scale hydrodynamics by oceanographers, and studying available habitats in the landscape by ecologists and coral biologists.

See `pyrugosity_example.ipynb` for detailed explanations and usage instructions.

Forward Simulation of Landscape Smoothing by Nonlinear Hillslope Diffusion Process

In the `~/example/` folder, the Jupyter Notebook file `nonlineardiff_landlab.ipynb` demonstrates the use of [`landlab`](https://landlab.readthedocs.io/en/latest/index.html), an open-source Python framework for simulating landscape evolution, to model topographic smoothing driven by near-surface soil disturbance and downslope soil creep processes. Specifically, this notebook employs the [`TaylorNonLinearDiffuser`](https://landlab.readthedocs.io/en/latest/reference/components/taylor_nonlinear_hillslope_flux.html) component from LandLab, described as one element in the [`terrainBento`](https://github.com/TerrainBento/terrainbento) package (developed by [Barnhart et al. (2019)](https://gmd.copernicus.org/articles/12/1267/2019/), to simulate topographic smoothing over time through non-linear hillslope diffusion processes ([Roering et al., 1999](https://doi.org/10.1029/1998WR900090)).

Users need to define the diffusion coefficient (K) for the simulation. The code will automatically detect the units of the XYZ directions (must be in feet or meters) of the input DEM raster file and convert the unit for K accordingly.

> [!WARNING]
> There is a known/unresolved stability issue when running `TaylorNonLinearDiffuser` component with a DEM with reprojected coordinate reference system (CRS) through GIS softwares. When using the example DEM, users may only use the pre-reprojected DEM with CRS: NAD83/Washington South (ftUS) (EPSG: 2286) and Z unit in US survey feet (e.g., the DEM files named with *"_f_3ftgrid"* or *"_f_6ftgrid"*).

Example DEM Raster Files

Along with he example Jupyter Notebook files, this repository include example LiDAR DEM files under `~/example/ExampleDEM/` that cover the area and nearby region of a deep-seated landslide occurred in 2014 at Oso area of the North Fork Stillaguamish River (NFSR) valley, Washington State, USA. The souce LiDAR DEM files were compiled from 'Stillaguamish 2014' and 'Snohoco Hazel 2006' projects that was originally contracted by Washington State Department of Transportation (WSDOT), downloaded from the [Washington Lidar Portal](http://lidarportal.dnr.wa.gov) on April 4, 2024.

A goal of this work allow users to reproduce the research by [Booth et al. (2017)](https://doi.org/10.1002/2016JF003934) and permit comparison of topographic complexity metrics derived from other regions using `pytopocomplexity` package and the `nonlineardiff_landlab.ipynb` simulation tools presented in this repository.

The example DEM raster files have various grid size, coordinate reference system (CRS), and unit of grid value (elevation, Z).

| LiDAR DEM Files | CRS | XY Grid Size | Z Unit | Descriptions |
| ------------- | ------------- | ------------- | ------------- | ------------- |
| Ososlid2014_f_3ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 3.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_m_3ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~0.9144 [meters] | meters | 2014 Oso Landslide |
| Ososlid2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_m_6ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~1.8288 [meters] | meters | 2014 Oso Landslide |
| Osoarea2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide & nearby NFSR valley |

> [!NOTE]
> When testing the code with the example DEM files, users should place the entire `~/ExampleDEM/` subfolder in the same directory as the Jupyter Notebook files. Both the `pytopocomplexity` package and the `nonlineardiff_landlab.ipynb` land-smoothing modeling tool have the capability to automatically detect the grid spacing and the units of the XYZ directions (must be in feet or meters) of the input DEM raster and compute the results in SI units.

Requirements
For `pytopocomplexity` package
* Python >= 3.10
* `numpy`
* `scipy`
* `rasterio`
* `dask`
* `matplotlib`
* `tqdm`
* `numba`
* `gdal`
* `statsmodels`

Additional packages for Jupyter Notebook examples:
* `glob`
* `pandas`
* `jupyter`

for landscape smoothing simulation
* `landlab` for landscape smoothing simulation ([User Guide](https://landlab.readthedocs.io/en/latest/index.html))
* Used components: `TaylorNonLinearDiffuser`, `esri_ascii`, `imshowhs`
* `osgeo` [if imported raster is in the geotiff format]
* `ipywidgets` [optional for interactive visualization]

See also the `environment.yml` file which can be used to create a virtual environment.

License
pyTopoComlexity is licensed under the [Apache License 2.0](LICENSE).

[](https://doi.org/10.5281/zenodo.11239338)

This repository contains a set of codes for
1. measuring the topographic complexity (i.e., surface roughness) of a land surface, and
2. simulating topographic smoothing by non-linear hillslope diffusion processes.

Citation

This is still a pre-release. To use this code, please cite the following Zenodo publication and DOI:
* Lai, L. S.-H. (2024). pyTopoComplexity. Zenodo. https://doi.org/10.5281/zenodo.11239338

2D-CWT Measurement of Topopraphic Complexity

There are three Jupyter Notebook files (see table below) using two-dimensional continuous wavelet transform (2D-CWT) with a Mexican Hat wevalet to measure the topographic complexity (i.e., surface roughness) of a land surface from a Digital Elevation Model (DEM). Such method quanitfy the wavelet-based curvature of the surface, which has been proposed to be a effective geomorphic metric for relative age dating of deep-seated landslide deposits, allowing a quick assessment of landslide freqency and spatiotemporal pattern over a large area.

| Code Files | Descriptions |
| ------------- | ------------- |
| pyMexicanHat.ipynb | The base version. |
| pyMexicanHat_chunk.ipynb | This version is developed to mitigate the RAM issues when handling large GeoTIFF files. |
| pyMexicanHat_batch.ipynb | This version is developed for batch-processing a large amount of raster files in the same directory. Chunk-processing optimization is included to mitigate the RAM issues when handling large GeoTIFF files. |

The original MATLAB code was developed by Dr. Adam M. Booth (Portland State Univeristy) and used in Booth et al. (2009) and Booth et al. (2017) (See source code from [Booth's personal website](https://web.pdx.edu/~boothad/tools.html)). This MATLAB code was later revised and adapted by Dr. Sean R. LaHusen (Univeristy of Washington) and Dr. Erich N. Herzig (Univeristy of Washington) in their research (e.g., LaHusen et al., 2020; Herzig et al., 2023). Since November 2023, Dr. Larry Syu-Heng Lai (Univeristy of Washington), under the supervision of Dr. Alison R. Duvall (Univeristy of Washington), translated the code into a optimized open-source Python version.

The current codes have the capability to automoatically detect the grid spacing and the unit of XYZ directions (must be in feet or meters) of the input DEM raster, which can compute the 2D-CWT result with an proper wavelet scale factor at a designated Mexican Hat wavelet.

<hr>

Landform Smoothing via Nonlinear Hillslope Diffusion Modeling

The following Jupyter Notebook demonstrates the use of [Landlab](https://landlab.readthedocs.io/en/latest/index.html), a open-source Python framework for simulating landscape evolution, to model topographic smoothing driven by near-surface soil disturbance and downslope soil creep processes. Specifically, this notebook employs the `TaylorNonLinearDiffuser` component from LandLab, described as one element in the **terrainBento** package (Barnhart et al., 2019), to simulate topographic smoothing over time through non-linear hillslope diffusion processes (Roering et al., 1999).

| Code Files | Descriptions |
| ------------- | ------------- |
| NonlinearDiff_LandLab.ipynb | Using Lanblab component [TaylorNonLinearDiffuser](https://landlab.readthedocs.io/en/latest/reference/components/taylor_nonlinear_hillslope_flux.html) |

The current codes have the capability to automoatically detect the grid spacing and the unit of XYZ directions (must be in feet or meters) of the input DEM raster, which can convert the unit for diffusion coefficient (K) accordingly. A goal of this work is to reproduce the simulation methods and results in Booth et al. (2017).

**WARNING**: There is a known/unresolved stability issue when running `TaylorNonLinearDiffuser` component with a DEM with reprojected coordinate reference system (CRS) through GIS softwares. When using the example DEM, users may only use the original DEM with CRS: NAD83/Washington South (ftUS) (EPSG: 2286) and Z unit in US survey feet (e.g., the DEM files named with *"_f_3ftgrid"* or *"_f_6ftgrid"*).

<hr>

Example DEM Raster Files

The example rasters include the LiDAR DEM files that cover the area and nearby region of a deep-seated landslide occurred in 2014 at Oso area of the North Fork Stillaguamish River (NFSR) valley, Washington State, USA. The example DEMs have various grid size, coordinate reference system (CRS), and unit of grid value (elevation, Z).

The example DEM files include:

| LiDAR DEM Files | CRS | XY Grid Size | Z Unit | Descriptions |
| ------------- | ------------- | ------------- | ------------- | ------------- |
| Ososlid2014_f_3ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 3.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide |
| Ososlid2014_m_3ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~0.9144 [meters] | meters | 2014 Oso Landslide |
| Ososlid2014_m_6ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~1.8288 [meters] | meters | 2014 Oso Landslide |
| Osoarea2014_f_6ftgrid.tif | NAD83/Washington South (ftUS) (EPSG: 2286) | 6.0 [US survey feet] | US survey feet | 2014 Oso Landslide & nearby NFSR valley |
| Osoarea2014_m_6ftgrid.tif | NAD83/Washington South (EPSG: 32149) | ~1.8288 [meters] | meters | 2014 Oso Landslide & nearby NFSR valley |

When testing the codes with the example DEM files, users should place the whole ***ExampleDEM*** subfolder in the same directory as the Jupyter Notebook files.

References

*Journal Articles:*

* Barnhart, K., Glade, R., Shobe, C., Tucker, G. (2019). Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution. Geoscientific Model Development 12(4), 1267-1297. https://doi.org/10.5194/gmd-12-1267-2019
* Booth, A.M., LaHusen, S.R., Duvall, A.R., Montgomery, D.R., 2017. Holocene history of deep-seated landsliding in the North Fork Stillaguamish River valley from surface roughness analysis, radiocarbon dating, and numerical landscape evolution modeling. Journal of Geophysical Research: Earth Surface 122, 456-472. https://doi.org/10.1002/2016JF003934 
* Booth, A.M., Roering, J.J., Perron, J.T., 2009. Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 109, 132-147. https://doi.org/10.1016/j.geomorph.2009.02.027   
* Ganti, V., Passalacqua, P., Foufoula-Georgiou, E. (2012). A sub-grid scale closure for nonlinear hillslope sediment transport models. Journal of Geophysical Research: Earth Surface, 117(F2). https://doi.org/10.1029/2011jf002181
* Herzig, E.N., Duvall, A.R., Booth, A.R., Stone, I., Wirth, E., LaHusen, S.R., Wartman, J., Grant, A., 2023. Evidence of Seattle Fault Earthquakes from Patterns in Deep‐Seated Landslides. Bulletin of the Seismological Society of America. https://doi.org/10.1785/0120230079
* Hobley, D.E.J., Adams, J.M., Nudurupati, S.S., Hutton, E.W.H., Gasparini, N.M., Istanbulluoglu, E., Tucker, G.E., 2017. Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics. Earth Surf. Dynam. 5, 21-46. https://doi.org/10.5194/esurf-5-21-2017
* LaHusen, S.R., Duvall, A.R., Booth, A.M., Grant, A., Mishkin, B.A., Montgomery, D.R., Struble, W., Roering, J.J., Wartman, J., 2020. Rainfall triggers more deep-seated landslides than Cascadia earthquakes in the Oregon Coast Range, USA. Science Advances 6, eaba6790. https://doi.org/10.1126/sciadv.aba6790
* Roering, J. J., Kirchner, J. W., & Dietrich, W. E. (1999). Evidence for nonlinear, diffusive sediment transport on hillslopes and implications for landscape morphology. Water Resources Research, 35(3), 853-870. https://doi.org/10.1029/1998wr900090

*Digital Elevation Model (DEM) Examples:*

* Washington Geological Survey, 2023. 'Stillaguamish 2014' and 'Snohoco Hazel 2006' projects [lidar data]: originally contracted by Washington State Department of Transportation (WSDOT). [accessed April 4, 2024, at http://lidarportal.dnr.wa.gov]

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Requirements
Python 3.10+
* os
* glob
* numpy
* scipy
* rasterio
* dask
* matplotlib
* ipywidgets (the 'widgetsnbextension' package in the Jupyter Notebook needs to be enabled. See instruction [here](https://ipywidgets.readthedocs.io/en/7.x/user_install.html).)
* landlab ([User Guide](https://landlab.readthedocs.io/en/latest/index.html))
* Used components: TaylorNonLinearDiffuser, esri_ascii, imshowhs
* osgeo

License
segmenteverygrain is licensed under the [Apache License 2.0](LICENSE).

Minor update for readme file
**Full Changelog**: https://github.com/LarrySHLai/pyTopoComlexity/compare/v0.5.1...v0.5.2