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Notebook","category":"Cryosphere","sub_category":"Glacier and Ice Sheets","monthly_downloads":109,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# pDEMtools\n\n__Conveniently search, download, and process ArcticDEM and REMA products__\n\n\n[![conda-forge version](https://anaconda.org/conda-forge/pdemtools/badges/version.svg)](https://anaconda.org/conda-forge/pdemtools) [![PyPI version](https://badge.fury.io/py/pdemtools.svg)](https://pypi.org/project/pdemtools/) [![Documentation Status](https://readthedocs.org/projects/pdemtools/badge/?version=latest)](https://pdemtools.readthedocs.io/en/latest/?badge=latest) [![Unit Tests](https://github.com/trchudley/pdemtools/actions/workflows/unit_test.yml/badge.svg)](https://github.com/trchudley/pdemtools/actions/workflows/unit_test.yml) [![JOSS paper](https://joss.theoj.org/papers/10.21105/joss.07149/status.svg)](https://doi.org/10.21105/joss.07149)\n\npDEMtools provides a convenient set of functions to explore, download, and preprocess high-resolution DEMs of the polar regions from the ArcticDEM (Porter _et al._  2022; 2023) and Reference Elevation Model of Antarctica (REMA; Howat _et al._ 2022a, b) products, courtesy of the Polar Geospatial Center (PGC).\n\nThe first aim of pDEMtools is to enable access to ArcticDEM and REMA mosaics and multitemporal strips using the `search()` function and `load` module:\n\n - **`search()`**: This function aims to allow users to easily query the PGC STAC API to find relevant ArcticDEM and REMA strips for their areas of interest. \n - **`load`**: This module provides simple one-line functions to preview and download strips and mosaics from the relevant AWS bucket to an `xarray` Dataset.\n\nThe second aim is to provide (pre)processing functions _specific_ to the sort of uses that ArcticDEM and REMA users might want (e.g. a focus on ice sheet and cryosphere work), as well as the particular _strengths_ of ArcticDEM and REMA datasets (high-resolution and multitemporal). Tools include:\n\n - Terrain attribute derivation (hillshade, slope, aspect, various curvatures) using a 5x5 polynomial fit suited for high-resolution data.\n - Quick geoid correction using BedMachine source data.\n - Simple coregistration for quick elevation change analysis.\n - Identifying/masking sea level and icebergs.\n\nRather than introducing custom classes, pDEMtools will always try and return DEM data as an [`xarray`](https://docs.xarray.dev/en/stable/) DataArray with geospatial metadata via the [`rioxarray`](https://corteva.github.io/rioxarray/stable/) extension. The aim is to allow the user to quickly move beyond pDEMtools into their own analysis in whatever format they desire, be that `xarray`, `numpy` or `dask` datasets, DEM-specific Python packages such as [`xdem`](https://github.com/GlacioHack/xdem) for advanced coregistration or [`richdem`](https://github.com/r-barnes/richdem) for flow analysis, or exporting to geospatial file formats for analysis beyond Python.\n\nContact: thomas.r.chudley@durham.ac.uk\n\n## Quick Install\n\nThe latest release of pdemtools can installed using `conda`:\n\n```\n$ conda install pdemtools -c conda-forge\n```\n\nPlease visit the [pDEMtools readthedocs](https://pdemtools.readthedocs.io/) for more information on installing, using, and contributing to pDEMtools.\n\n## Cite\n\nA software paper for `pdemtools` is published in the [Journal of Open Source Software](https://joss.theoj.org/), and can be cited as follows:\n\n\u003e Chudley, T. R., and Howat, I. M. (2024). pDEMtools: conveniently search, download, and process ArcticDEM and REMA products. _Journal of Open Source Software_, _9_(102), 7149, doi.org/10.21105/joss.07149\n\nor by using `bibtex`:\n\n```\n@article{Chudley2024,\n  title = {pDEMtools: conveniently search,  download,  and process ArcticDEM and REMA products},\n  volume = {9},\n  ISSN = {2475-9066},\n  url = {http://dx.doi.org/10.21105/joss.07149},\n  DOI = {10.21105/joss.07149},\n  number = {102},\n  journal = {Journal of Open Source Software},\n  publisher = {The Open Journal},\n  author = {Chudley,  Thomas R. and Howat,  Ian M.},\n  year = {2024},\n  pages = {7149}\n}\n```\n\nWhen using ArcticDEM and REMA products, please [cite](#refererences) the datasets appropriately and [acknowledge](#acknowledgements) the PGC.\n\nSeveral algorithms implemented in the library were developed by others. These will be highlighted in the documentation, and the original authors should be properly cited when used. For example:\n\n\u003e We masked sea ice and melange following the method of Shiggins _et al._ (2023) as implemented in pDEMtools (Chudley and Howat, 2024).\n\n\u003c!-- # To do\n\nThe tool is presented _as-is_, but requests/contributions to functionality are welcome (thomas.r.chudley@durham.ac.uk). Avenues for future work include the following:\n\n - Quicker preview downloads of hillshades and DEMs through use of the GeoTIFF overviews and the `rxr.open_rasterio()` `overview_level` function. This can result in uneven x/y resolutions though, so perhaps another option for upsampling may be useful as an accessor utility.\n - Implement Ian Howat's blunder filter algorithm. --\u003e\n\n\n\n# Refererences\n\nHowat, I., _et al._ (2022a). The Reference Elevation Model of Antarctica – Strips, Version 4.1. _Harvard Dataverse_ https://doi.org/10.7910/DVN/X7NDNY\n\nHowat, I., _et al._ (2022b). The Reference Elevation Model of Antarctica – Mosaics, Version 2, _Harvard Dataverse_ https://doi.org/10.7910/DVN/EBW8UC\n\nPorter, C., _et al._ (2022). ArcticDEM - Strips, Version 4.1. _Harvard Dataverse_. https://doi.org/10.7910/DVN/OHHUKH\n\nPorter, C., _et al._ (2023), ArcticDEM, Version 4.1, _Harvard Dataverse_. https://doi.org/10.7910/DVN/3VDC4W\n\n\n# Acknowledgements\n\n**ArcticDEM:** DEMs are provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736.\n\n**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.\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.21105/joss.07149","http://dx.doi.org/10.21105/joss.07149","https://doi.org/10.7910/DVN/X7NDNY","https://doi.org/10.7910/DVN/EBW8UC","https://doi.org/10.7910/DVN/OHHUKH","https://doi.org/10.7910/DVN/3VDC4W"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["cryosphere"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/328089","html_url":"https://ost.ecosyste.ms/projects/328089"}