Set of tools to manipulate Digital Elevation Models (DEMs)

More documentation to come!

To cite this package:

conda install -c conda-forge --strict-channel-priority xdem

The --strict-channel-priority flag seems essential for Windows installs to function correctly, and is recommended for UNIX-based systems as well.

Solving dependencies can take a long time with conda. To speed up this, consider installing mamba:

conda install mamba -n base -c conda-forge

Once installed, the same commands can be run by simply replacing conda by mamba. More details available through the mamba project.

If running into the sklearn error ImportError: dlopen: cannot load any more object with static TLS, your system needs to update its glibc (see details here). If you have no administrator right on the system, you might be able to circumvent this issue by installing a working environment with specific downgraded versions of scikit-learn and numpy:

conda create -n xdem-env -c conda-forge xdem scikit-learn==0.20.3 numpy==1.19.*

On very old systems, if the above install results in segmentation faults, try setting more specifically numpy==1.19.2=py37h54aff64_0 (worked with Debian 8.11, GLIBC 2.19).

NOTE: Setting up GDAL and PROJ may need some extra steps, depending on your operating system and configuration.

pip install xdem

Recommended: Use conda for depencency solving.

$ git clone https://github.com/GlacioHack/xdem.git
$ cd ./xdem
$ conda env create -f dev-environment.yml
$ conda activate xdem
$ pip install -e .

After installing, we recommend to check that everything is working by running the tests:

$ pytest -rA

xdem are for now composed of three libraries:

• coreg.py with tools covering differet aspects of DEM coregistration
• spatial_tools.py for spatial operations on DEMs
• dem.py for DEM-specific operations, such as vertical datum correction.

You can find ways to improve the libraries in the issues section. All contributions are welcome. To avoid conflicts, it is suggested to use separate branches for each implementation. All changes must then be submitted to the dev branch using pull requests. Each PR must be reviewed by at least one other person.

Please see our contribution page for more detailed instructions.

See the documentation at https://xdem.readthedocs.io

These tools are only valuable if we can rely on them to perform exactly as we expect. So, we need testing. Please create tests for every function that you make, as much as you are able. Guidance/examples here for the moment: https://github.com/GeoUtils/georaster/blob/master/test/test_georaster.py https://github.com/corteva/rioxarray/blob/master/test/integration/test_integration__io.py

Coregister a DEM to another DEM

import xdem

reference_dem = xdem.DEM("path/to/reference.tif")
dem_to_be_aligned = xdem.DEM("path/to/dem.tif")

nuth_kaab = xdem.coreg.NuthKaab()

nuth_kaab.fit(reference_dem.data, dem_to_be_aligned.data, transform=reference_dem.transform)


aligned_dem = xdem.DEM.from_array(
	nuth_kaab.apply(dem_to_be_aligned.data, transform=dem_to_be_aligned.transform),
	transform=dem_to_be_aligned.transform,
	crs=dem_to_be_aligned.crs
)

aligned_dem.save("path/to/coreg.tif")

This is an implementation of the Nuth and Kääb (2011) approach. Please see the documentation for more approaches.

Subtract one DEM with another

import xdem

first_dem = xdem.DEM("path/to/first.tif")
second_dem = xdem.DEM("path/to/second.tif")

difference = first_dem - second_dem

difference.save("path/to/difference.tif")

By default, second_dem is reprojected to fit first_dem. This can be switched with the keyword argument reference="second". The resampling method can also be changed (e.g. resampling_method="nearest") from the default "cubic_spline".