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Tools for extracting crevasse location and volume from high-resolution digital elevation models, following Chudley et al. (2025).

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# CrevDEM

**_Tools for extracting crevasse location and volume from high-resolution digital elevation models._**

![An aerial image of crevasses on the Greenland Ice Sheet](https://raw.githubusercontent.com/trchudley/crevdem/main/images/crevasses_uav_header.jpeg "An aerial image of crevasses on the Greenland Ice Sheet")

CrevDEM is a Python package for extracting crevasse location and volume from high-resolution Digital Elevation Models (DEMs) of glaciers and ice sheets provided by the ArcticDEM and REMA projects. It is the software accompaniment to Chudley _et al._ (2025, _Nat. Geosci._)

> **Warning** 
> These tools are designed for scientific purposes only. ArcticDEM and REMA strips are not suitable for detecting metre-scale and snow-covered crevasses, and should not be used for field hazard assessment.

The functions and associated notebooks provide a complete workflow for:

1. Automatically downloading and clipping 2 m strips of the version 4.1 ArcticDEM (Porter _et al._ 2022) and Reference Elevation Model of Antarctica (REMA; Howat _et al._ 2022).
2. DEM geoid correction and filtering of bare rock and proglacial sea-ice/mélange.
3. Extracting crevasse presence following the method outlined by Chudley _et al._ (_in prep_).

The principle of crevasse extraction is based around Black Top Hat filtering of a detrended surface (Kodde _et al._ 2007). The optimal kernel size can be determined quantiatively through the use of variogram analysis: a notebook is provided to aid with this.

![An example output from CrevDEM](https://raw.githubusercontent.com/trchudley/crevdem/main/images/crevdem_output.jpg "An example output from CrevDEM")

## Cite

Please cite the source paper when using CrevDEM:

> Chudley, T. R., Howat, I. M., King, M. D., and MacKie, E. (2025) Increased crevassing across accelerating Greenland Ice Sheet margins. _Nature Geoscience_ doi:[10.1038/s41561-024-01636-6](https://doi.org/10.1038/s41561-024-01636-6)

As always when using ArcticDEM and REMA products, please [cite](#references) the datasets appropriately and [acknowledge](#acknowledgements) the PGC.

## Installation

### Install CrevDEM

CrevDEM is not currently available on package managers such as `pip` or `conda`/`mamba`, and so must be installed locally.

First, clone the github repository to your local environment. If you are exclusively managing your python packages using `pip` (i.e. not via `conda` or `mamba`), `crevdem` can be installed from the top-level directory via `pip install .`:

```bash
git clone https://github.com/trchudley/crevdem
cd crevdem
pip install .
```

If you are using `conda` or `mamba`, you may wish to install the necessary dependencies via these methods before installing `crevdem`. An `environment.yml` file is included to make this installation simple and easy:

```bash
git clone https://github.com/trchudley/crevdem
cd crevdem/
mamba env create --file environment.yml -n crevdem_env
mamba activate crevdem_env
pip install .
```

Dependencies can also be installed independently by the user. They are:

- `rioxarray`
  - `rasterio`
  - `shapely`
  - `numpy`
  - `opencv-python`

### Optional Packages and Datasets

#### Packages

`crevdem` does not provide a solution for searching and downloading ArcticDEM or REMA strips. [The authors have released an independent package, `pdemtools`](https://pdemtools.rtfd.io), for this purpose. This package is used to download ArcticDEM strips in the included Notebooks, and more information on how to use it to download strips can be found [here](https://pdemtools.readthedocs.io/en/latest/examples/strip_search.html).

The [variogram analysis notebook](https://github.com/trchudley/crevdem/blob/main/notebooks/variogram_notebook.ipynb) requires additional explicit packages: `scikit-gstat`, `geopandas`, and `matplotlib`. If you would rather not install these yourself using `conda` or similar, you can use `pip install .[variogram]` during the initial install.

#### Supplementary datasets

Supplementary datasets are required to be available locally to complete geoid correction and filtering of non-glacial regions: specifically, the BedMachine Greenland v5 or BedMachine Antarctica v3 respectively (Morlighem _et al._ 2022a, 2022b) and, for Greenland, the GrIMP ice mask (Howat, 2017) for bedrock filtering. File or directory paths will be requested in the relevant functions.

These can be downloaded from the NSIDC manually ([Greenland BedMachine](https://doi.org/10.5067/GMEVBWFLWA7X), [Antarctic BedMachine](https://doi.org/10.5067/FPSU0V1MWUB6), [Greenland surface mask](https://doi.org/10.5067/B8X58MQBFUPA)) but for conveninence, download scripts are provided in the `supp_data` directory. The BedMachine download scripts are provided by the NSIDC, and require an Earthdata user account and password to be provided. The files will be downloaded into the directory the scripts are run.

```bash
cd supp_data
python download_bedmachine_greenland_v5.py
python download_grimp_2015_15m.py
python download_bedmachine_antarctica_v3.py
```

## Usage

The sections below briefly outline the purpose of user-exposed functions available through the package. In order to see them in action, Jupyter Notebooks are provided in the [`./notebooks` directory](https://github.com/trchudley/crevdem/tree/main/notebooks) in order to provide an introduction into the use of CrevDEM.

Information on the required and optional input variable for individual functions can be accessed through Python's `help()` function, e.g. `help(crevdem.load_aws)`.

### Preprocessing the DEM strips

`mask_bedrock()` - Returns a bedrock-masked DEM. Can either provide your own mask (as a DataArray) using the `mask` variable (where bedrock = 0/False and ice/ocean = 1/True), or provide the path to a directory containing the [GrIMP 15 m classification mask](https://doi.org/10.5067/B8X58MQBFUPA) using the `grimp_mask_dir` variable.

`geoid_correct()` - Returns a geoid-corrected DEM. Can provide either your own geoid (as a DataArray) using the `geoid` variable, or the filepath to an appropriate BedMachine dataset using the `bedmachine_fpath` variable.

`mask_melange()` - Returns a DEM with mélange/ocean region, as identified by `get_melange_mask()` function, filtered out. If no likely sea level is identified, returns the original DEM. DEM must be geoid-corrected.

`get_melange_mask()` - Returns a mask of mélange/ocean regions of a DEM, using sea level as returned by the `get_sea_level()` function. Input DEM must be geoid-corrected. In returned mask, land/ice is True and ocean is False.

`get_sea_level()` - Returns estimated sea level following method of Shiggins _et al._ (2023). If no candidate sea level is identified, `None` is returned. Input DEM must be geoid-corrected.

### Extracting crevasse presence

`find()` - Returns crevasse depths, batch processed from input DEM strip. Parameters default to Chudley _et al._ generic workflow for Greenland marine margins, but can be modified. This function is a wrapper for the `detrend`, `bth_filter`, `threshold_depth`, `interpolate_surface`, and `calc_depth` functions.

- `detrend()` - Returns a detrended DEM DataArray using a large gaussian filter. Standard deviation size should be >> the features of interest (in the default `crevdem` settings, the gauss_std to be 3* the range).

- `bth()` - Returns a black-top-hat-filtered DEM DataArray from the (detrended) DEM DataArray. Kernel diameter is set following the range distance.

- `threshold_depth()` - Returns crevasse mask (crevasse = 1; not crevasse = 0) DataArray from BTH-filtered DataArray. Mask is filtered to the threshold BTH value, which is set to 1 metre in the default workflow.

- `interpolate_surface()` - Returns a 'crevasse-filled' DEM from the original DEM and crevasse mask, using the GDAL FillNodata algorithm (inverse distance weighting) to fill crevasse-masked regions. Smoothing iterations are applied to smooth  out artefacts.

- `calc_depth()` - Returns final crevasse depth, calculated from the raw DEM and the filled DEM.

<!-- ### Loading DEM strips from the cloud

> **Note**
> These functions are depracated. [It is not recommended to use the `pdemtools` package to download ArcticDEM and REMA strips.](https://github.com/trchudley/pdemtools).

`load_aws()` - Returns the selected ArcticDEM/REMA strip, downloaded from the relevant AWS bucket, as an xarray DataArray suitable for further processing by `crevdem`. Option to filter to bounds and bitmask. 2 m DEM strips are large in size and loading remotely from AWS may take some time.

`load_local()` - Loads the desired ArcticDEM/REMA DEM strip, from local filepaths, as an xarray DataArray suitable for further processing by `crevdem`. Option to filter to bounds and bitmask. -->

## Improvements

The tool is presented _as-is_, but requests/contributions to functionality are welcome (tom.chudley@bristol.ac.uk). Avenues for future work include the following:

- Currently, the only automated input mask is the GrIMP mask. However, `pdemtools` contains function for an automated BedMachine mask option for use in both Antarctica and Greenland at a larger resolution (150 m). A built-in wrapper function could introduct BedMachine masking to `crevdem`..
 - The default PGC bitmask has a habit of leaving in some cloud blunders. Ian Howat has implemented an additional filter (in Matlab) to catch these remnant cloud blunders in the latest ArcticDEM and REMA mosaic tools. This could be rewritten in Python to filter out these blunders.
 - The currently chosen Guassian and BTH filters (as implemented in OpenCV) return `Nan` if `NaN`s are present within the kernel, leading to loss of data at glacier margins. It is worth searching for or developing alternative implementations to account for `NaN` values. `astropy` already has such a function for Gaussian filters but is very slow over large datasets. A test `numba.stencil` implementation was also slow (>6 m vs <1 s for OpenCV).

## References

Chudley, T. R., _et al._ (2025). An increase in crevasses across accelerating Greenland Ice Sheet margins. https://doi.org/10.1038/s41561-024-01636-6

Howat, I. (2017). MEaSUREs Greenland Ice Mapping Project (GIMP) Land Ice and Ocean Classification Mask, Version 1 [Data Set]. _NASA National Snow and Ice Data Center Distributed Active Archive Center_. https://doi.org/10.5067/B8X58MQBFUPA

Howat, I., _et al._ (2022). The Reference Elevation Model of Antarctica – Strips, Version 4.1. _Harvard Dataverse_ https://doi.org/10.7910/DVN/X7NDNY

Kodde, M. P., _et al._ (2007). Automatic glacier surface analysis from airborne laser scanning. _The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences_, 36(3), 221–226.

Morlighem, M. _et al._ (2022). MEaSUREs BedMachine Antarctica, Version 3 [Data Set]. _NASA National Snow and Ice Data Center Distributed Active Archive Center_. https://doi.org/10.5067/FPSU0V1MWUB6

Morlighem, M. _et al._ (2022). IceBridge BedMachine Greenland, Version 5 [Data Set]. _NASA National Snow and Ice Data Center Distributed Active Archive Center_. https://doi.org/10.5067/GMEVBWFLWA7X

Porter, C., _et al._ (2022). ArcticDEM - Strips, Version 4.1. _Harvard Dataverse_. https://doi.org/10.7910/DVN/OHHUKH

Shiggins, _et al._ (2023). Automated ArcticDEM iceberg detection tool: insights into area and volume distributions, and their potential application to satellite imagery and modelling of glacier–iceberg–ocean systems, The Cryosphere, 17, 15–32, https://doi.org/10.5194/tc-17-15-2023

## Acknowledgements

**ArcticDEM:** DEMs are provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736.

**REMA:** DEMs are provided by the Byrd Polar and Climate Research Center and the Polar Geospatial Center under NSF-OPP awards 1543501, 1810976, 1542736, 1559691, 1043681, 1541332, 0753663, 1548562, 1238993 and NASA award NNX10AN61G. Computer time provided through a Blue Waters Innovation Initiative. DEMs produced using data from Maxar.

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