Matthew Love12, Christopher Amante12, Kelly Carignan12, Elliot Lim12, Michael MacFerrin12
The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI), through its collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder, develops digital elevation models (DEMs) that range from the local to global scale. Collectively, these elevation models are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously-Updated DEM (CUDEM) Program, to systematically generate DEMs from the local coastal community to the global scale.
We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository (https://github.com/ciresdem) for consistency, transparency, and to promote accessibility. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM Program is also enabling the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI's suite of regional and global DEMs. The CUDEMs are a shift from project-based DEM specifications, to a comprehensive program that systematically and continuously develops and updates DEMs across all spatial scales.
The CUDEM framework provides a set of powerful open-source command-line tools as well as a Python-3-based application programming interface (API) for (1) identifying and downloading topographic and bathymetric source data from more than forty different publicly available datasets, (2) filtering data, (3) ranking and merging multiple datasets using a variety of algorithms, (4) generating DEMs from those source datasets in a variety of data formats and projections/datums, (5) gernerating auxiliary data products including uncertainty and data mask rasters, and (6) generating metadata documentation of those DEMs. Individual components are modular and may be run standalone; for example, the CUDEM “fetches” module can download airborne lidar data, multi-beam sonar data, or nautical charts covering specific geographic regions; or the “stacks” module can efficiently combine and gap-fill raster datasets of varying qualities using customizable data lists. The CUDEM tools may also be conjoined in end-to-end workflows ensuring permanent traceability and reproducibility in DEM generation. The Coastal DEM Team uses the CUDEM framework to accurately inform and map risk assessments of coastal regions (e.g. Amante et al.)
GDAL and GDAL-Python are required for use.
Other useful external programs needed for full functionality include: GMT, MB-System, HTDP and VDatum.
Installation of the above programs are system dependent. Only GDAL and GDAL-Python are required.
Download and install git (If you have not already): git installation
pip install git+https://github.com/ciresdem/cudem.git#egg=cudem
- Setup a conda environment and install the dependencies:
conda create -n cudem -c conda-forge gdal gmt pygmt numpy scipy pandas pyproj utm requests lxml matplotlib laspy h5py boto3 tqdm mercantile git
conda activate cudem
(cudem) pip install laspy[laszip]
(cudem) pip install --no-deps git+https://github.com/ciresdem/cudem.git
- If on Windows, install windows-curses with pip
git config --global http.sslVerify false
pip install windows-curses
Installation of HTDP and MB-System are system dependent, see their respective installation instructions for your system.
The open-access code includes command-line tools and a Python application programming interface (API) for automated data download, processing, DEM gridding, and interpolation uncertainty grid generation with three main software tools: "fetches", "waffles", and "dlim". "Fetches" is the data download tool for obtaining publicly available elevation data froma variety of sources and can optionally list, download or process thefetched data for use in DEM generation. We download a variety of data types, e.g., topographic-bathymetry lidar, multibeam swath sonar bathymetry, hydrographic soundings, compiled grids, etc., from a variety of sources, e.g., NOAA Office for Coastal Management (OCM) Digital Coast, NOAA NCEI NOS Hydro Surveys, NOAA NCEI Multibeam, USGS The National Map, and U.S. Army Corps of Engineers (USACE) Navigation Condition Surveys. Other data sources include digitized bathymetric charts or topographic maps, shorelines, satellite-derived elevations, and precisely surveyed geodetic monuments (Table 1). We typically download data in an area slightly larger (~5%) than the DEM extents. This data "buffer" ensures that interpolative gridding occurs across rather than along the DEM boundaries to prevent edge effects, which is especially important with sparse bathymetric data with large interpolation distances. Data buffers also minimize artificial offsets between adjacent DEM tiles.
Main Console Programs and Python APIs provided with CUDEM:
| Module | Description |
|---|---|
| dlim | process data from a variety of data types |
| waffles | generate Digital Elevation Models from scattered data using a variety of methods |
| fetches | fetch elevation data from a variety of public online sources |
| regions | process REGIONS |
| vdatums | generate vertical transformation grids |
| perspecto | generate images of DEMs |
| grits | filter DEMs |
| cudem | run CUDEM cli programs and scripts |
Additional scripts provided with CUDEM:
| Script | Description |
|---|---|
| bag2tif2chunks2xyz.sh | convert a BAG to chunked XYZ |
| clip_xyz.sh | clip an xyz file based on a vector |
| coastline2xyz.sh | convert a coastline vector to XYZ |
| colortable.py | generate a colortable |
| create_datalist.sh | create a datalist from data in the current directory |
| create_outliers.sh | identify outliers in a DEM |
| create_povray_template.sh | generate a POVray template from a DEM |
| create_coastline.py | generate a coastline |
| ddms.py | convert between dd and dms |
| error_distance_plots.py | generate an error/distance plots |
| gdal_null.py | generate a null grid |
| gdal_outliers.py | filter vertical outliers from a grid |
| gdal_nan2null.py | convert NaN values from a grid |
| gdal_findreplace.py | find/replace values in a grid |
| gdal_query.py | query values from a grid |
| gdal_chunk.py | parse a grid into chunks |
| gdal_crop.py | crop a grid by its nodata value |
| gdal_cut.py | cut a grid to a given region |
| gdal_clip.py | clip a grid to a vector |
| gdal_split.py | split a grid by z value |
| gdal_percentile.py | get a percentile from a grid |
| gdal_histogram.py | generate an historgram from a grid |
| gdal_hillshade.py | generate a hillshade image from a grid |
| gdal_minmax.py | get min/max values from a grid |
| grd2mesh.py | generate an unstructured grid |
| has_nulls.py | check if a grid has nodata values |
| nsidc_download.py | downlaod nsidc data |
| ogr_edit_field.py | edit OGR field values |
| outliers_shp.sh | identify outliers in a DEM |
| percentiles_minmax.py | get percentiles from a grid |
| rename_shp.py | rename a shapefile |
| smooth_dem_bathy.py | smooth a DEM < 0 with a Gaussian filter |
| spatial-meta.sh | generate spatial metadata using BOUNDS |
| tif2chunks2xyz.sh | chunk a DEM and output as chunked XYZ |
| usace_interp.sh | interpolate usace cross surveys |
| vdatum_cmd.py | run NOAAs vdatum from command-line |
| x360.py | flip a DEM |
| xyz_clip.py | clip an xyz file based on a raster mask |
| xyztindex.py | generate a tile index of xyz files. |
| fetch_osm_coastline.py | Fetch and process a water/landmask from Open Street Map |
- Generate a CRM of Point Mugu in California
- Generate a set of Tile CRMs of Northern California
- Filter Multibeam data
- Estimate DEM uncertainty
The CUDEM code repository is frequently updated, and code syntax is subject to change. Please see the code help function for the latest code syntax and examples. See Eakins and Grothe (2014) for more information on the challenges of building integrated DEMs and Eakins et al. (2015) for the initial specifications of the comprehensive DEM development framework. See Hare et al. (2011), Amante and Eakins (2016), and Amante (2018) for additional information on the DEM uncertainty.
For additional questions, please contact:
MIT License
Copyright (c) 2010 - 2024 Regents of the University of Colorado
Permission is hereby granted, free of charge, to any person
obtaining a copy
of this software and associated documentation files (the
\"Software\"), to deal
in the Software without restriction, including without
limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell
copies of the Software, and to permit persons to whom the
Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be
included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY
KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO
EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE
SOFTWARE.
Love, M., Amante, C., Carignan, K., MacFerrin, M., & Lim, E. (2023). CUDEM (Version 1.10.5) [Computer software]. [https://github.com/ciresdem/cudem]{.ul}
Amante CJ, Love M, Carignan K, Sutherland MG, MacFerrin M, Lim E. Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling. Remote Sensing. 2023; 15(6):1702. https://doi.org/10.3390/rs15061702
Amante, C. J. (2018). Estimating coastal digital elevation model uncertainty. Journal of Coastal Research, 34(6), 1382-1397. https://doi.org/10.2112/JCOASTRES-D-17-00211.1
Amante, C. J., & Eakins, B. W. (2016). Accuracy of interpolated bathymetry in digital elevation models. Journal of Coastal Research, (76 (10076)), 123-133. https://doi.org/10.2112/SI76-011
Eakins, B. W., & Grothe, P. R. (2014). Challenges in building coastal digital elevation models. Journal of Coastal Research, 30(5), 942-953.
Eakins, B. W., Danielson, J. J., Sutherland, M. G., & Mclean, S. J. (2015). A framework for a seamless depiction of merged bathymetry and topography along US coasts. In Proc. US Hydro. Conf (pp. 16-19).
Hare, R., Eakins, B., & Amante, C. J. (2011). Modelling bathymetric uncertainty. The International Hydrographic Review.
