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Short title: streams_error

A Sextante implementation of these algorithms called FlowTools can be obtained here (contributed by Daniel Nüst). The project report can be downloaded as a PDF. You can access the Eclipse project “flowTools” in the following public Subversion repository:

http://svn.xp-dev.com/svn/FlowTools/

You can also download an initial version (might be outdated!) of the project folder as a zip file (12).

Purpose and use:

Extraction of stream networks from a DEM using error propagation technique.

Programming environment: R / S language
Status of work: Public Domain
Reference: On the uncertainty of stream networks derived from elevation data: the error propagation approach
Data set name: Baranja hill

Attachment:

streams_error_0.zip

stream_sims_0.zip

Wolfgang Schwanghart from the Geographisches Institut, Universität Basel has recently released a toolbox that allows analysis of relief and flow pathways in digital elevation models in Matlab. The toolbox can be used to visualize DEMs, extract simple derivatives, run (and modify) flow models, delineate drainage basins, produce hydrographs and implement similar DEM-based hyrdological analysis. For more info see the User Guide to TopoToolbox.

When you use TopoToolbox in your work, please refer to this publication:

Schwanghart, W., Kuhn, N. J. (2010): TopoToolbox: a set of Matlab functions for topographic analysis. Environmental Modelling & Software, in press.

Short title:geomorph

For a complete description of the processing steps, see the original publication.

Purpose and use:

Automated extraction of geomorphological features using digital elevation data: case study Drente; outputs: extracted classes and summary statistics; various plots and images.

Programming environment:R / S language
Status of work:Public Domain
Reference:{Semi-automated identification and extraction of geomorphological features using digital elevation data}
Data set name:Boschoord case study

Attachment:

maps_KML.zip

FYI: a short report from Geomorphometry 2009 by Bob MacMillan (ISRIC) published in the Pedometron newsletter #28:

“The main purpose of my participation was to keep informed about other efforts, similar to GlobalSoilMap.net, that have a interest in processing digital elevation data and other digital data sets globally or at least for extremely large areas. This conference actually contained a large number of presentations of direct relevance for the GlobalSoilMap.net project. Perhaps first and foremost were the descriptions of efforts being undertaken in Australia (Gallant and Read) and Europe (Köthe and Bock) to process SRTM DEM data at 30 m (Australia) and 90 m (Europe) grid resolution to reduce artefacts and produce a filtered and cleaned DEM that is more suitable for use to produce inputs for the GlobalSoilMap.net project. Both of these presentations highlighted the significant advantages that can be realised by applying a series of filtering and conditioning routines to the original raw SRTM DEM data. It is obvious that similar procedures would prove equally useful if applied to SRTM DEM data sets for other parts of the world under the jurisdiction of other GlobalSoilMap.net nodes. Gallant has offered to help with efforts in other Nodes if asked.

Also of great interest were several projects that demonstrated that it is indeed possible to process and produce digital output for global scale digital data sets, including global scale SRTM DEM data sets. Reuter and Nelson presented a description of WorldTerrain, a contribution of the Global Geomorphometric Atlas. Peter Guth described processing of global scale SRTM data to identify and classify organized linear landforms (dunes). Peter also provided examples of multiple scale analysis and illustrated what you get to “see” from DEMs of 1 m, 100 m and 2 km grid resolution. Guth intends to publish the many different grids of DEM derivatives he produced for his project and make these processed data available for free and widespread use by others. Marcello Gorini described a physiographic classification of the ocean flood using a multi-resolution geomorphometric approach.

Several authors presented methods that may prove of interest to the GlobalSoilMap.net project. Gallant and Hutchinson described a differential equation for computing specific catchment area that could be applied to produce an improved terrain covariate for use in the GlobalSoilMap.net project. Similarly, Peckham, gave a new algorithm for creating DEMs with smooth elevation profiles that could be used to condition rough SRTM or GDEM data sets to smooth out noise and produce more hydrologically plausible surfaces. This algorithm was of particular interest to the GlobalSoilMap.net project because it appeared to be able to introduce hydrologically and geomorphologically relevant detail into 90 m SRTM DEMs of relatively low spatial detail.

Romstad and Etzelmuller described a new approach for segmenting hillslopes into landform elements by applying a watershed algorithm to a surface defined by the total curvature at a point instead of the raw elevation value. The resulting watersheds were bounded by lines of maximum curvature, effectively structuring each hillslope into components partitioned by lines of maximum local curvature. This is harder to explain than to understand when illustrated but it is remarkably simple to implement and may provide a new way of automatically segmenting hillslopes in a simple and efficient fashion.

Metz and others presented an algorithm for fast and efficient processing of massive DEMs to extract drainage networks and flow paths. This is of considerable interest and relevance to the GlobalSoilMap.net project because of the project’s need to process SRTM data globally to compute hydrological flow networks and various indices that are computed based on flow networks (e.g. elevation above channel, distance from divide). This algorithm can process data sets of hundreds of millions of cells (11,424 rows by 13,691 cols) in a few minutes instead of a few days (or not at all for some algorithms that fail on data sets this large).

Overall, this was an excellent conference, dominated by leading edge research in the area of geomorphic processing of digital elevation data that is of direct relevance and interest to the GlobalSoilMap.net project. We have much to learn from these researchers and much to benefit from maintaining contacts and working relationships with them.”

The case study “Boschoord” (3024 ha) is a small area located in the province of Drenthe, in the northern part of the Netherlands. The Boschoord area is part of the Drenthe Plateau which is underlain by boulder clay deposited by the second last (Saalien) ice sheet. This rather complex genesis created a fragmented landscape in which hydrological differences are strongly linked to this polycyclic landscape development. What makes this dataset especially interesting is that it is an area of low relief, but with distinct geomorphological classes that have been mapped with relatively high accuracy (Koomen and Maas, 2004). The elevations range from 3 to 10 m above the sea level, with a standard deviation of 1.54 m; changes in topography are difficult to notice even in the field. This data set is available only for collaborators. To receive a password to use the data, please contact the authors.

Fig: Location of the study area (a) and the two main DEM data sources used for analysis: DEM25TOPO – generated using ordinary kriging (b) and DEM25LIDAR (c).

Available layers:

- Elevation datasets – This includes the 5 m LiDAR DEM (surveyed in 2004), and a point dataset with 5010 measurements of heights (surveyed in 1960-69). Both datasets show elevations measured with a high precision (±10-20 cm).
- Geomorphological map (GKN50) – The map contains of 12 classes: Ground moraine (3L1), Low plains with ridges (3N3), Peat bog depressions (2R4), Cover sand undulated (3L5), Low plains/depressions without ridges (3N4), Low dunes + plains (3L8), Low plains/depressions without ridges (3N4), Cover sand undulated (3K14), Undulating ground moraines (3L1), Ground moraine (high) (3L2a), Low dunes + plains (3L9), Areas partially covered with cover sand (2M14), Low dunes (4K19), and Cover sand areas (2M13).
- Topographic data – Includes all roads and infrastructure, land use classes and similar features from the TOP10VECTOR basic topographic map of the Netherlands (1:5000 scale). This data is used only for orientation purposes.Grid definition:

ncols: 1110
nrows: 1081
xllcorner: 208297
yllcorner: 543057
cellsize: 5proj4:+proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +units=m +no_defsLineage:

The 5020 field measurements of elevation (land survey) collected in the 1960s by the ‘Meetkundige Dienst Rijkswaterstaat’. This was used to generate the 25 m DEM25TOPO. The 5 m LiDAR-based DEM distributed by the Ministry of transportation and water management (measurements in cm). This dataset is also known as “Actueel Hoogtebestand Nederland” (AHN ) (van Heerd et al. 2008).

Data owner:Universiteit van Amsterdam
Reference:Semi-automated identification and extraction of geomorphological features using digital elevation data
Location: Boschoord Netherlands52° 53’ 55.7772” N,6° 13’ 41.2896” E
See map:Google Maps

Whitebox Workflows Next Gen, a complete rewrite of the Whitebox geospatial analysis platform, is now publicly available for Python, R, and QGIS.

Built from the ground up in pure Rust, Whitebox Next Gen provides more than 700 geospatial analysis tools for geomorphometry, terrain analysis, spatial hydrology, LiDAR processing, remote sensing, vector GIS, and spatial statistics. All three interfaces run on the same high-performance backend, allowing users to work in scripts, notebooks, statistical workflows, or a familiar desktop GIS environment.

Whitebox Next Gen is not an incremental update to the previous Whitebox architecture. It replaces the earlier monolithic system with a modular, full-stack geospatial platform. Core capabilities for raster and vector I/O, coordinate systems and reprojection, vector topology, spatial indexing, LiDAR processing, and other foundational operations are implemented directly within the Whitebox codebase rather than delegated to external C or C++ GIS libraries. This approach provides consistent cross-platform behaviour, fewer system-level dependencies, tighter control over performance and correctness, and greater flexibility for continued research and development

Highlights

More than 700 tools for geomorphometry, hydrology, LiDAR, remote sensing, vector analysis, spatial statistics, and general geospatial processing
Publicly available interfaces for Python, R, and QGIS
A modular, high-performance backend written entirely in Rust, including wbprojection, wbraster, wbvector, wblidar, wbspatialstats, and wbtopology open-source (MIT/Apache licensed) backend libraries
Most of the 80+ tools that existed within the previous extension product have been migrated to the new Whitebox open core, including the tools for advanced surface curvature analysis and DEM processing
Native coordinate-reference-system handling and reprojection workflows via wbprojection
Expanded raster support (19 formats), including GeoTIFF, Cloud-Optimized GeoTIFF (COGs), GeoPackage Raster, and JPEG2000
Expanded vector support (12 formats), including Shapefile, GeoPackage, FlatGeobuf, GeoParquet, GeoJSON, TopoJSON, and GML
Modern point-cloud support, including LAS, LAZ, COPC, E57, and PLY
A dedicated topology engine supporting robust vector analysis, network analysis, route-event workflows, and linear referencing
Local-first processing on Windows, macOS, and Linux
A consistent analysis platform across Python scripts and R workflows
A newly updated and more advanced QGIS Processing toolbox

Whitebox has particular strengths in terrain analysis, geomorphometry, spatial hydrology, and LiDAR processing. Whitebox Next Gen carries these areas forward while substantially expanding the project’s capabilities for vector analysis, remote sensing, modern spatial data formats, and reproducible geospatial workflows.

Get started

The geomorphometry community is invited to explore Whitebox Next Gen, test its tools and workflows, report issues, and contribute to the continued development of the project.

First round of the DEMIX exercise published final report

Global digital elevation models (DEMs) have become routine operational inputs across mapping, environmental monitoring, modelling and Earth-observation workflows. Their broad coverage and global availability have made them data that many practitioners simply take for granted. But knowledge of the terrain and topography of Earth’s surface is fundamental for monitoring and understanding terrestrial ecosystems and the planet’s habitability. For that reason, the CEOS Working Group on Calibration and Validation (WGCV) maintains a subgroup dedicated to digital topography and the quality of DEMs, the ‘Terrain Mapping Sub-Group (TMSG).

DEMs are representations of elevation in the form of a georectified grid, at global scale commonly derived from space-based interferometric synthetic aperture radar (InSAR) or stereoscopic optical observations, while regionally and locally airborne Laser induced detection and ranging (LIDAR) has become the primary source. DEMs can be classified as digital surface models (DSMs) when depicting the lower surface of the atmosphere or digital terrain models (DTMs) when depicting the top of the lithosphere (the Earth’s crust).

A wide variety of DEM products exist – each having different characteristics which suit different applications. It can be challenging to understand which of the available DEM products are fit for purpose for certain applications or regions. That is the practical gap addressed by the Digital Elevation Model Intercomparison Exercise (DEMIX) undertaken by TMSG. By exposing where widely used global DEMs agree, where they differ and how those differences affect rankings, the exercise delivered evidence to users for selecting a DEM rather than defaulting to one by habit.

DEM products were compared through a ‘wine contest,’ which identified requirements of openness, reproducibility, adaptability, and statistical rigour. The characteristics, evaluations, and ranking for each DEM were collected in a GIS database with over 50,000 entries. A quantitative assessment was carried out for each global DEM based on pixel-by-pixel differences of geomorphometric parameters against finer spatial resolution (1-5 m resolution) reference DEMs.

The study incorporates an unprecedented amount of high quality reference data covering a broad range of landforms and surface types, comparing their characteristics without resampling or interpolation. Users are invited to consult the results of DEMIX in order to make informed choices when needing to use a DEM, or in some cases a combination of the most relevant DEMs for their applications.

The final rankings of the wine contest, presented below, compared the six DEMs based on land cover (forest, urban, or barren), slope (cliff, steep, gentle, or flat), differences in elevation, slope and roughness, and statistical metrics.

Led by the European Commission’s Joint Research Centre (JRC), the CEOS Working Group on Calibration and Validation (WGCV) Terrain Mapping Subgroup (TMSG) established DEMIX in partnership with the International Society for Geomorphometry. Several members of the International Society of the Geomorphometry contributed significantly and we hosted various secssions at our latest conference. The exercise was performed over a three-year period starting in 2020, with community-wide calls for participation and an assembly of experts producing a number of peer-reviewed publications, culminating with a JRC reference report published in July 2026.

The objective of DEMIX was to propose a procedure to rank the available free and open global DEMs, taking into consideration user needs while promoting the implementation of FAIR (Findable, Accessible, Interoperable and Reusable) principles. The exercise compared DEMs based on criteria for land cover and terrain slope categories, representative testing, and a statistically sound ranking approach. The report outputs tailored recommendations regarding available DEM products that are not limited to one domain, geographic area, or landscape type, with flexibility for different user needs and applications.

A major challenge in the comparison of the global DEMs were the varying formats, data, and metadata contents. The adoption of a common set of standards would enhance the quality and interoperability of global DEMs and streamline the exchange and utilisation of DEM data for both providers and users. DEMIX makes the following recommendations for data providers:

● Adopt a standardised grid layout, with complete grid definitions and encodings following ISO/OGC rules.

● Include vertical datum information as part of raster DEM files.

● Clearly indicate the product version in the file names.

● Participate in future DEMIX rounds, which serve as a neutral platform for independently evaluating products before their release.

● Provide high quality, finer resolution, accurate, and multi-temporal elevation data to be used as a reference alongside DEM products.

Global DEMs are indispensable operational geospatial datasets, but they should not be treated as error-free or interchangeable. DEMIX delivered to the global community a systematic and transparent method to help choose elevation products more intelligently and help define what better elevation measurements should look like in the future.

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