Matthew Love12, Christopher Amante12
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.
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 numpy scipy pandas pyproj utm requests lxml matplotlib laspy h5py boto3 tqdm mercantile git
conda activate cudem
- If on Windows, install windows-curses with pip inside conda environment
pip install windows-curses
- install HTDP and MB-System
Installation of HTDP and MB-System are system dependent, see their respective installation instructions for your system.
- Install cudem with pip ignoring the dependencies
pip install --no-deps git+https://github.com/ciresdem/cudem.git
Main Console Programs provided with CUDEM:
| Command | Description |
|---|---|
| dlim | process data from a variety of data types |
| waffles | generate Digital Elevation Models from scattered data using a variety of methods |
| regions | process REGIONS |
| fetches | fetch elevation data from a variety of public online sources |
| vdatums | generate vertical transformation grids |
| perspecto | generate images of DEMs |
| grits | filter DEMs |
| cudem | run CUDEM cli programs and scripts |
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.
Table 1. Data source modules available in the CUDEM software tool "fetches*.*"
| Name | Description | URL |
|---|---|---|
| arcticdem | Arctic DEM | https://www.pgc.umn.edu/data/arcticdem/ |
| bluetopo | A curated collection of high resolution seafloor models from NOAA. | https://www.nauticalcharts.noaa.gov/data/bluetopo.html |
| buoys | Buoy information from NOAA | https://www.ndbc.noaa.gov |
| charts | NOS Nautical Charts, including electronic Nautical Charts and Raster Nautical Charts | https://www.charts.noaa.gov/ |
| chs | Canadian Hydrographic Surveys | https://open.canada.ca |
| copernicus | Copernicus elevation data | https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/elevation/copernicus-dem/elevation |
| digital_coast | Lidar and Raster data from NOAA’s Digital Coast | https://coast.noaa.gov |
| earthdata | NASA Earthdata | https://cmr.earthdata.nasa.gov |
| ehydro | USACE hydrographic surveys | https://navigation.usace.army.mil/Survey/Hydro |
| emodnet | EmodNET European Bathymetric/Topographic DEM | https://portal.emodnet-bathymetry.eu/ |
| fabdem | FABDEM (Forest And Buildings removed Copernicus DEM) | https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn |
| gebco | A global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. | https://www.gebco.net/data_and_products/gridded_bathymetry_data/ |
| gmrt | The Global MultiResolution Topography synthesis | https://www.gmrt.org |
| hrdem | High-Resolution DEMs from Canada | https://open.canada.ca |
| hydrolakes | HydroLakes vector and derived elevations | https://www.hydrosheds.org/products/hydrolakes |
| mar_grav | Marine Gravity Satellite Altimetry Topography from Scripps. | https://topex.ucsd.edu/WWW_html/mar_grav.html |
| mgds | Marine Geoscience Data System | https://www.marine-geo.org |
| multibeam | NOAA Multibeam bathymetric data | https://data.ngdc.noaa.gov/platforms/ |
| nasadem | NASA Digital Elevation Model | https://www.earthdata.nasa.gov/esds/competitive-programs/measures/nasadem |
| ncei_thredds | NCEI DEM THREDDS Catalog | https://www.ngdc.noaa.gov/thredds/demCatalog.xml |
| ngs | NGS monuments | http://geodesy.noaa.gov/ |
| nos | NOS Hydrographic DataBase (NOSHDB) | https://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html |
| osm | Open Street Map | https://wiki.openstreetmap.org/ |
| srtm_plus | SRTM15+: Global bathymetry and topography at 15 arc-seconds. | https://topex.ucsd.edu/WWW_html/srtm15_plus.html |
| tides | Tide station information from NOAA | https://tidesandcurrents.noaa.gov/ |
| tnm | USGS National Map | http://tnmaccess.nationalmap.gov/ |
| trackline | NOAA trackline bathymetry data | http://www.ngdc.noaa.gov/trackline/ |
| usiei | US Interagency Elevation Inventory | https://coast.noaa.gov/inventory/ |
| wsf | World Settlement Footprint from DLR (German Aerospace Center) | https://download.geoservice.dlr.de/WSF2019/ |
| vdatum | Vertical Datum transformation grids | https://vdatum.noaa.gov https://cdn.proj.org/ |
| icesat | IceSat2 granules from NASA (requires NASA credentials) | https://cmr.earthdata.nasa.gov |
"Waffles" is the data gridding tool for DEM generation using various gridding modules (Table 2). We leverage existing open-source software packages from a variety of sources including GMT, GDAL, and MB-System. From these sources, there are also multiple gridding algorithms, e.g., spline, inverse distance weighting (IDW), triangulate, average, near-neighbor, etc. Previous research at NOAA NCEI indicates spline interpolation is the most accurate gridding algorithm for generating DEMs (Amante and Eakins, 2016). However, the measurement density and terrain characteristics (e.g. terrain slope and curvature) may influence the accuracy of the various gridding algorithms and multiple algorithms should be evaluated. We then generate DEMs by a combination of data masking, weighting, and interpolation.
Table 2. Gridding modules available in the CUDEM software tool "waffles."
| Name | Description |
|---|---|
| gdal-average | Generate an average DEM using GDAL's gdal_grid command. |
| coastline | Generate a coastline (land/sea mask) using a variety of data sources. |
| cudem | CUDEM integrated DEM generation. Generate an topographic-bathymetric integrated DEM using a variety of data sources. |
| IDW | Generate a DEM using an Inverse Distance Weighted algorithm. If weights are used, will generate a UIDW DEM, using weight values as inverse uncertainty, as described here, and here |
| gdal-invdst | Generate an inverse distance DEM using GDAL's gdal_grid command. |
| gdal-linear | Generate a linear DEM using GDAL's gdal_grid command. |
| mbgrid | Generate a DEM using MB-System's mbgrid command. |
| gdal-nearest | Generate a nearest DEM using GDAL's gdal_grid command. |
| gmt-nearneighbor | Generate a DEM using GMT's nearneighbor command |
| num | Generate an uninterpolated DEM using various gridding modes, including options from GMT's xyz2grd command. |
| linear | Generate a DEM using Scipy's linear gridding algorithm) |
| cubic | Generate a DEM using Scipy's cubic gridding algorithm |
| nearest | Generate a DEM using Scipy's nearest gridding algorithm |
| stacks | Generate a DEM using a raster stacking method. By default, calculate the [weighted]-mean where overlapping cells occur. Set supercede to True to overwrite overlapping cells with higher weighted data. |
| gmt-surface | Generate a DEM using GMT's surface command |
| gmt-triangulate | Generate a DEM using GMT's triangulate command |
| vdatum | Generate a vertical datum conversion grid. |
| flatten | Generate a DEM by flattening nodata areas. |
| lakes | Estimate/interpolate Lake bathymetry. |
| uncertainty | Estimate interpolation uncertainty. |
We generate uncertainty grids that represent an estimate of potential interpolation errors based on a split-sample approach and distance to the nearest measurement. Using a split-sample approach, a percentage of the data is omitted, an interpolation method is applied, and the differences between the interpolated elevations and the original omitted elevations are calculated. We omit a percentage of the measurements, apply an interpolation method, and calculate the differences between the interpolated values and the omitted elevations. We repeat this process and aggregate the differences between the original measurements and the interpolated elevations and then we derive a best-fit equation of interpolation uncertainty as a function of distance to the nearest measurement.
We generate spatial metadata for our DEMs that document the data sources that went into the DEM generation process.
Generate a CRM of the Point Mugu area of Malibu using remote datalists provided from fetches. Datasets included in this example are Marine Gravity (mar_grav), Nautical Charts (charts), NOS Soundings (hydronos:datatype=xyz), Copernicus (copernicus), USACE surveys (ehydro) and NOS BAG surveys (hydronos:datatype=bag)
waffles -R -119.25/-119/34/34.25 -E 1s -M cudem:pre_count=1:filter_outliers=85:min_weight=.6 -O mugu_crm_test -P epsg:4326+5703 -w -X0:5 mar_grav,-106:bathy_only=True,.001 charts,-200,.01 hydronos:datatype=xyz,-202,.1 copernicus,-103,.6 ehydro,-203,.6 hydronos:datatype=bag,-202,.75
Output:
mugu_crm_test.tif
Generate a hillshade of the generated DEM:
perspecto -M hillshade mugu_crm_test.tif
Output:
mugu_crm_test_hs.tif
Figure 1. DEM hillshade generated from CUDEM code example.
Generate a .25 degree tileset for an area in northern California
regions -R-123.5/-122.75/37.75/38.75 -T .25
Output:
regions_tile_set.shp
Generate DEMs of all the tiles
waffles -R regions_tile_set.shp -E 1s -M cudem:pre_count=1:filter_outliers=85:min_weight=.6 -O nocal -P epsg:4326+5703 -p -w -X0:5 mar_grav,-106:bathy_only=True,.001 charts,-200,.01 hydronos:datatype=xyz,-202,.1 copernicus,-103,.6 ehydro,-203,.6
Output:
nocal1_n38x00_w123x00_2024v1.tif
nocal1_n38x00_w123x50_2024v1.tif
nocal1_n38x25_w123x25_2024v1.tif
nocal1_n38x50_w123x00_2024v1.tif
nocal1_n38x50_w123x50_2024v1.tif
nocal1_n38x75_w123x25_2024v1.tif
nocal1_n38x00_w123x25_2024v1.tif
nocal1_n38x25_w123x00_2024v1.tif
nocal1_n38x25_w123x50_2024v1.tif
nocal1_n38x50_w123x25_2024v1.tif
nocal1_n38x75_w123x00_2024v1.tif
nocal1_n38x75_w123x50_2024v1.tif
Generate hillshades of all the DEMs, setting min/max z of the CPT so the hillshade edges match:
for i in nocal1*v1.tif; do perspecto -M hillshade $i --min_z -360 --max_z 800; done
Figure 2. DEM hillshade generated from CUDEM code example.
We provide an example of code to download and process depth and elevation data from the "gebco", "copernicus", and "mar_grav" data sources in Table 1, and then generate a DEM and accompanying interpolation uncertainty grid. First, we download all the data, and then process the data by masking out GEBCO data where TID is equal to 0 (Land) or 40 (Predicted based on satellite-derived gravity data - depth value is an interpolated value guided by satellite-derived gravity data). See the GEBCO Cookbook chapter on GEBCO TID generation for more information.
We then stack the raster data sources with higher weighted datasets masking out lower weighted datasets and apply spline interpolation to the bathymetry by limiting the interpolation to an upper limit value of zero and clipping the resulting grid to an automatically generated coastline from the Copernicus data.
Lastly, we combine this bathymetric surface with the other data sources that have a weighting above the min_weight specification (in this example, min_weight=.15), and apply spline interpolation with a weighted averages of these data sources to generate the final integrated bathymetric-topographic DEM.
To generate the configuration file that is then used to generate the 15 arc-second resolution DEM and the interpolation uncertainty grid, execute the following command:
waffles -R -79.5/-76.5/25/28 -E 15s -O gebcop -p -w -m -P epsg:4326 -M cudem:min_weight=.15:landmask=True:pre_count=1 gebco,-102:exclude_tid=0/40,.015 copernicus,-103,10 mar_grav,-106:bathy_only=True,.001 gmrt,-101:swath_only=True,1.5 --config
The contents of the generated config file configuration file (gebcop15_n28x00_w079x50_2023v1.json) are as follows:
{
"mod": "cudem:min_weight=.15:landmask=True:pre_count=1",
"mod_name": "cudem",
"mod_args": {
"min_weight": ".15",
"landmask": true,
"pre_count": "1"
},
"kwargs": {
"verbose": true,
"sample": "bilinear",
"xsample": null,
"ysample": null,
"dst_srs": "epsg:4326",
"srs_transform": false,
"fltr": [],
"name": "gebcop15_n28x00_w079x50_2023v1",
"cache_dir": "/home/ncei/Projects/crm/software/.cudem_cache",
"ndv": -9999,
"xinc": 0.004166666666666667,
"yinc": 0.004166666666666667,
"want_weight": true,
"want_mask": true,
"data": [
"gebco,-102:exclude_tid=0/40,.015,0",
"copernicus,-103,10,0",
"mar_grav,-106:bathy_only=True,.001,0",
"gmrt,-101:swath_only=True,1.5,0"
],
"src_region": [
-79.5,
-76.5,
25.0,
28.0
]
}
}
To generate the DEM, execute the following command:
waffles -G gebcop15_n28x00_w079x50_2023v1.json
Output:
gebcop3_n28x00_w079x50_2023v1.json - json config file
gebcop3_n28x00_w079x50_2023v1_msk.tif - data mask
gebcop3_n28x00_w079x50_2023v1_stack.tif - stacks data 'stacked' output
gebcop3_n28x00_w079x50_2023v1_u.tif - uncertainty grid
gebcop3_n28x00_w079x50_2023v1.tif - final DEM
Figure 3. Final DEM generated from CUDEM code example.
Figure 4. Interpolation uncertainty grid generated from the best-fit interpolation uncertainty equation applied to the distance to the nearest measurement raster.
First, we use the 'multibeam' fetches module as a datalist entry in a waffles command to fetch and grid the multibeam data in our region, the using that result as a datalist entry in a waffles command to grid the data using the 'IDW' waffles gridding module.
waffles -R-123.25/-123/48.25/48.5 -E .11111111s -O mb -p -P epsg:4326 -m -u -M stacks multibeam
waffles -R-123.25/-123/48.25/48.5 -E .11111111s -O mb_idw -p -P epsg:4326 -m -u -M IDW:radius=10 mb19_n48x50_w123x25_2024v1.tif
Figure 5. Auto-gridded raw multibeam data
The above results show some major artifacts from the raw multibeam data, so we then filter that output using the grits 'outlier' filter and re-generate the IDW grid with waffles:
grits mb19_n48x50_w123x25_2024v1_filtered.tif -M outliers
waffles $(dlim -r mb19_n48x50_w123x25_2024v1.tif) -M IDW:radius=5 -O mb_idw -E .11111111s -p -P epsg:4326 mb_data19_n_w_filtered.tif
Figure 6. Auto-grided filtered multibeam data
We can combine all the above steps using a single waffles command to fetch the multibeam, filter it using the grits 'outlier' module with the waffles -T switch and generate an IDW DEM with the waffles 'IDW' module.
waffles -R-123.25/-123/48.25/48.5 -E .11111111s -O mb -p -P epsg:4326 -m -u -M IDW:radius=10 -T outliers:stacks=True multibeam
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:
fetches (2.0.4): Fetches; Fetch and process remote elevation data
usage: fetches [ -hlqzHR [ args ] ] MODULE ...
Options:
-R, --region Restrict processing to the desired REGION
Where a REGION is xmin/xmax/ymin/ymax[/zmin/zmax[/wmin/wmax]]
Use '-' to indicate no bounding range; e.g. -R -/-/-/-/-10/10/1/-
OR an OGR-compatible vector file with regional polygons.
Where the REGION is /path/to/vector[:zmin/zmax[/wmin/wmax]].
If a vector file is supplied, will use each region found therein.
-H, --threads Set the number of threads (1)
-l, --list Return a list of fetch URLs in the given region.
-z, --no_check_size Don't check the size of remote data if local data exists.
-q, --quiet Lower the verbosity to a quiet
--modules Display the module descriptions and usage
--help Print the usage text
--version Print the version information
Supported FETCHES modules (see fetches --modules <module-name> for more info):
gmrt, mar_grav, srtm_plus, charts, digital_coast, multibeam, gebco, mgds, trackline,
ehydro, ngs, hydronos, ncei_thredds, tnm, emodnet, chs, hrdem, arcticdem, bluetopo,
osm, copernicus, fabdem, nasadem, tides, vdatum, buoys, earthdata, usiei, wsf, hydrolakes,
https, bing_bfp
dlim (2.0.4): DataLists IMproved; Process and generate datalists
usage: dlim [ -acdghijquwEJPR [ args ] ] DATALIST,FORMAT,WEIGHT,UNCERTAINTY ...
Options:
-R, --region Restrict processing to the desired REGION
Where a REGION is xmin/xmax/ymin/ymax[/zmin/zmax[/wmin/wmax/umin/umax]]
Use '-' to indicate no bounding range; e.g. -R -/-/-/-/-10/10/1/-/-/-
OR an OGR-compatible vector file with regional polygons.
Where the REGION is /path/to/vector[:zmin/zmax[/wmin/wmax/umin/umax]].
If a vector file is supplied, will use each region found therein.
-E, --increment Block data to INCREMENT in native units.
Where INCREMENT is x-inc[/y-inc]
-X, --extend Number of cells with which to EXTEND the output DEM REGION and a
percentage to extend the processing REGION.
Where EXTEND is dem-extend(cell-count)[:processing-extend(percentage)]
e.g. -X6:10 to extend the DEM REGION by 6 cells and the processing region by 10
percent of the input REGION.
-J, --s_srs Set the SOURCE projection.
-P, --t_srs Set the TARGET projection. (REGION should be in target projection)
-D, --cache-dir CACHE Directory for storing temp and output data.
-Z, --z-precision Set the target precision of dumped z values. (default is 4)
--mask MASK the datalist to the given REGION/INCREMENTs
--spatial-metadata Generate SPATIAL METADATA of the datalist to the given REGION/INCREMENTs
--archive ARCHIVE the datalist to the given REGION[/INCREMENTs]
--glob GLOB the datasets in the current directory to stdout
--info Generate and return an INFO dictionary of the dataset
--weights Output WEIGHT values along with xyz
--uncertainties Output UNCERTAINTY values along with xyz
--quiet Lower the verbosity to a quiet
--modules Display the datatype descriptions and usage
--help Print the usage text
--version Print the version information
Supported datalist formats (see dlim --modules <dataset-key> for more info):
datalist (-1), zip (-2), scratch (-3), xyz (168), gdal (200), bag (201), las (300),
mbs (301), ogr (302), https (-100), gmrt (-101), gebco (-102), copernicus (-103),
fabdem (-104), nasadem (-105), mar_grav (-106), srtm_plus (-107), charts (-200),
multibeam (-201), hydronos (-202), ehydro (-203), bluetopo (-204), ngs (-205), tides (-206),
digital_coast (-207), ncei_thredds (-208), tnm (-209), emodnet (-300), chs (-301),
hrdem (-302), arcticdem (-303), vdatum (-500), hydrolakes (-600)
waffles (2.0.4): Generate DEMs and derivatives.
usage: waffles [OPTIONS] DATALIST
Options:
-R, --region Specifies the desired REGION;
Where a REGION is xmin/xmax/ymin/ymax[/zmin/zmax[/wmin/wmax]]
Use '-' to indicate no bounding range; e.g. -R -/-/-/-/-10/10/1/-
OR an OGR-compatible vector file with regional polygons.
Where the REGION is /path/to/vector[:zmin/zmax[/wmin/wmax]].
If a vector file is supplied, will use each region found therein.
-E, --increment Gridding INCREMENT and RESAMPLE-INCREMENT in native units.
Where INCREMENT is x-inc[/y-inc][:sample-x-inc/sample-y-inc]
-M, --module Desired Waffles MODULE and options. (see available Modules below)
Where MODULE is module[:mod_opt=mod_val[:mod_opt1=mod_val1[:...]]]
-S, --sample_alg ReSAMPLE algorithm to use (from gdalwarp)
Set as 'auto' to use 'average' when down-sampling and 'bilinear' when up-sampling
This switch controls resampling of input raster datasets as well as resampling
the final DEM if RESAMPLE-INCREMENT is set in -E
-X, --extend Number of cells with which to EXTEND the output DEM REGION and a
percentage to extend the processing REGION.
Where EXTEND is dem-extend(cell-count)[:processing-extend(percentage)]
e.g. -X6:10 to extend the DEM REGION by 6 cells and the processing region by 10
percent of the input REGION.
-T, --filter FILTER the output DEM using one or multiple filters.
Where FILTER is fltr_id[:fltr_val[:split_value=z]]
Available FILTERS:
1: perform a Gaussian Filter at -T1:<factor>.
2: use a Cosine Arch Filter at -T2:<dist(km)> search distance.
3: perform an Outlier Filter at -T3:<aggression<1-9>>.
The -T switch may be set multiple times to perform multiple filters.
Append :split_value=<num> to only filter values below z-value <num>.
e.g. -T1:10:split_value=0 to smooth bathymetry (z<0) using Gaussian filter
-L, --limits LIMIT the output elevation or interpolation values, append
'u<value>' to set the upper elevation limit,
'l<value>' to set the lower elevation limit,
'p<value>' to set an interpolation limit by proximity, or
's<value>' to set an interpolation limit by size.
e.g. -Lu0 to set all values above 0 to zero, or
-Ls100 to limit interpolation to nodata zones smaller than 100 pixels.
-C, --clip CLIP the output to the clip polygon -C<clip_ply.shp:invert=False>
-K, --chunk Generate the DEM in CHUNKs.
-F, --format Output grid FORMAT. [GTiff]
-O, --output-name BASENAME for all outputs.
-P, --t_srs Projection of REGION and output DEM.
-N, --nodata The NODATA value of output DEM.
-G, --wg-config A waffles config JSON file. If supplied, will overwrite all other options.
Generate a waffles_config JSON file using the --config flag.
-H, --threads Set the number of THREADS (1). Each input region will be run in up to THREADS threads.
-D, --cache-dir CACHE Directory for storing temp data.
Default Cache Directory is ~/.cudem_cache; cache will be cleared after a waffles session.
to retain the data, use the --keep-cache flag.
-f, --transform Transform all data to PROJECTION value set with --t_srs/-P where applicable.
-p, --prefix Set BASENAME (-O) to PREFIX (append <RES>_nYYxYY_wXXxXX_<YEAR>v<VERSION> info to output BASENAME).
note: Set Resolution, Year and Version by setting this to 'res=X:year=XXXX:version=X',
leave blank for default of <INCREMENT>, <CURRENT_YEAR> and <1>, respectively.
-r, --grid-node Use grid-node registration, default is pixel-node.
-w, --want-weight Use weights provided in the datalist to weight overlapping data.
-u, --want-uncertainty Generate/Use uncertainty either calculated or provided in the datalist.
-m, --want-mask Mask the processed datalist.
-a, --archive ARCHIVE the datalist to the given region.
-k, --keep-cache KEEP the cache data intact after run
-x, --keep-auxiliary KEEP the auxiliary rastesr intact after run (mask, uncertainty, weights, count).
-d, --supercede higher weighted data supercedes lower weighted data.
-s, --spatial-metadata Generate SPATIAL-METADATA.
-c, --continue Don't clobber existing files.
-q, --quiet Lower verbosity to a quiet.
--help Print the usage text
--config Save the waffles config JSON and major datalist
--modules Display the module descriptions and usage
--version Print the version information
Datalists and data formats:
A datalist is a file that contains a number of datalist entries,
while an entry is a space-delineated line:
`path [format weight uncertainty [name source date type resolution hdatum vdatum url]]`
Supported datalist formats:
datalist (-1), zip (-2), scratch (-3), xyz (168), gdal (200), bag (201), las (300),
mbs (301), ogr (302), https (-100), gmrt (-101), gebco (-102), copernicus (-103),
fabdem (-104), nasadem (-105), mar_grav (-106), srtm_plus (-107), charts (-200),
multibeam (-201), hydronos (-202), ehydro (-203), bluetopo (-204), ngs (-205), tides (-206),
digital_coast (-207), ncei_thredds (-208), tnm (-209), emodnet (-300), chs (-301),
hrdem (-302), arcticdem (-303), vdatum (-500), hydrolakes (-600)
Modules (see waffles --modules <module-name> for more info):
stacks, IDW, linear, cubic, nearest, gmt-surface, gmt-triangulate, gmt-nearneighbor, mbgrid,
gdal-linear, gdal-nearest, gdal-average, gdal-invdst, vdatum, coastline, lakes, cudem,
uncertainty, scratch, flatten
License:
MIT License
Copyright (c) 2010 - 2023 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.