We are pleased to invite submissions for a cover design contest for the second edition of the Geomorphometry book, to be published in 2026.

The submissions will be gathered in a poll, and the entire community will be able to vote for their favorite design.

If your design is selected, you will receive the appropriate credits, but would need to provide the necessary permissions to use the image.

You can submit your design by email before October 17th. Please ensure that the image is of at least 300 dpi resolution.

Get designing!

The editors,
Hannes Reuter
Carlos Grohmann
Vincent Lecours

Dr. Li-Yang Xiong
Nanjing Normal University, China

October 1st , 2025
8:00 MDT (UTC -6), 10:00 EDT (UTC -4), 11:00 BRT (UTC - 3), 15:00 BST (UTC +1), 16:00 CEST (UTC +2), 17:00 EEST (UTC +3), 22:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Dr. Li-Yang Xiong is a professor at the School of Geographical Science, Nanjing Normal University (NNU), China. He is currently responsible for managing NNU’s research in Digital Terrain Model and Digital Terrain Analysis. His main research interests include AI based terrain modelling, loess terrain feature characterization, landform evolution modeling, paleotopography reconstruction and geomorphological process mining. His recent work involves deep learning-based DEM reconstruction, geomorphology-oriented digital terrain analysis, and value-added digital terrain applications for geoscience. He also serves as Associate Editor for the journal Earth Surface Processes and Landforms and as an Editorial Board Member for International Journal of Geographical Information Science.

Abstract: In this talk, I will present some recent research achievements related to terrain modelling theory, terrain analysis method and terrain application in China. This terrain modeling theory focused on how we understand terrain knowledge and integrate it into AI methods for terrain reconstruction. In term of the terrain analysis method, the mathematical vector operation we believe should be highlighted in the research of Geomorphometry, which is suitable for multi-source data structure by considering the directional property of terrain parameters. Actually, this directional property should be made a full consideration for process- oriented geographical modeling and simulation. Lastly, I will show some terrain applications towards different typical geographical areas in China as well as global scale application.

Laipelt L., Andrade B.C., Collischonn W., Teixeira A.A., Paiva R.C.D., Ruhoff A., 2024. ANADEM: A Digital Terrain Model for South America. Remote Sensing 16(13):2321. https://doi.org/10.3390/rs16132321

Paper abstract:
Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

Data Access
ANADEM can be downloaded at https://hge-iph.github.io/anadem/ or accessed directly in GoogleEarth Engine (see instructions on the project link).

I’m grateful if you can circulate information on this PhD opportunity in Italy. The potential candidates can contact me (strevisani@iuav.it) for further information. Here the main elements of the position:

Research topics: Predicting and supporting benthic and pelagic biodiversity through geomorphometry and machine learning

Link to the call (Italian and English): https://www.unipa.it/didattica/dottorati/dottorato-xli/bando-di-accesso-ciclo-41/

Position code [BIODIV.OGS]

Research headquarters OGS Trieste and University of Palermo

Funded by OGS - Istituto Nazionale di Oceanografia e di Geofisica Sperimentale

Key dates: Deadline: 7th August 2025 - 14:59 (Italian time)

Yu-Feng Ho
OpenGeoHub, NL

May 7th , 2025
7:00 MDT (UTC -6), 9:00 EDT (UTC -4), 10:00 BRT (UTC -3), 13:00 GMT (UTC +0), 15:00 CEST (UTC+2), 15:00 EET (UTC +2), 21:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Yu-Feng Ho is a Research Assistant / Geoinformatician in OpenGeoHub. He is the one of the main producers of GEDTM dataset and has experiences in global space-borne lidar (ICESat-2 and GEDI) and operating global topography datasets. He specializes in geocomputing with Remote Sensing data, optimize and automate modeling frameworks.

Abstract: Terrain models and derivatives are used in multidisciplinary subjects. What is the current software and methodologies that we can use to derive and maintain an open global terrain dataset? How can open data easily interact with the International Society for Geomorphometry (ISG)? OpenGeoHub creates a fully opened global digital terrain model in 30m (GEDTM30) and its multiscale derivatives in 30, 60, 120, 240, 480 and 960m. In this presentation, we will dive in the methodology of “global-to-local” modeling of deriving GEDTM30 by fusing ALOS AW3D30, CoperincusDEM and global satellite lidar (ICESat-2 and GEDI), and also the implementation of Whitebox Workflow to derive multiscale DTM derivatives. At the end, we provide several solutions to access and interact with this open global terrain dataset through Jupyter notebook, QGIS, and GitHub.

Wolfgang Schwanghart
University of Potsdam Germany

April 2nd , 2025
7:00 MDT (UTC -7), 9:00 EDT (UTC -5), 11:00 BRT (UTC -3), 14:00 GMT (UTC +0), 15:00 CEST (UTC+1), 16:00 EET (UTC +2), 22:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Dr. Wolfgang Schwanghart is a physical geographer and geomorphologist at the University of Potsdam, Germany. He has more than 15 years experience in working with DEMs and has written the software TopoToolbox, a MATLAB software for terrain analysis. His research focuses on landscape evolution on geological timescales and natural hazards, particularly in high mountain areas. In this context, he applies digital terrain analysis to extract as much quantitative and qualitative information as possible from DEMs, enhancing the understanding of tectonic and climatic influences on landscape evolution and improving predictions of natural hazards and risks.

Abstract: In this talk, I will present current developments within the project TopoToolbox 3 – improving the quality and reuse of a research software for terrain analysis. Adopting FAIR principles of research software development, the project will make the MATLAB-based TopoToolbox available in other high-level programming languages. I will also talk about the software design which aims at providing a platform that fosters creativity, development, and proto-typing. Lastly, I will show some applications.

Ernest Fahrland
Airbus, Germany

December 4th, 2024
7:00 MDT (UTC -7), 9:00 EST (UTC -5), 11:00 BRT (UTC -3), 14:00 GMT (UTC +0), 15:00 CET (UTC +1), 16:00 EET (UTC +2), 22:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Ernest Fahrland is a 3D Data Development Manager working within the Radar Programs unit of Airbus. He is a studied Cartographer and has more than 15 years of experience in the field of global Digital Elevation Models. He was involved in the process design and development of production tools for the WorldDEMTM, a consistent, highly accurate and pole-to-pole DEM, that was released in 2015. On-going acquisitions of global raw DEM data in combination with the user demand for quick delivery of error-free DEMs invoked the development of a fully automated, high-performant and consistent DEM production process, hence leading to the WorldDEM Neo product. Ernest was responsible for its development and contributed his concept ideas, processing strategies and algorithmic tools. His passion for global Digital Elevation Models and his continuous strive for improvements have paved the ground for many current DEMs such as the Copernicus DEM and its derivatives.

Abstract: In his talk, Ernest Fahrland will provide an insight into a fully-automated editing & production process of global DEM data primarily from interferometric radar DEM acquisitions. The presentation comprises a short look into the history with its manual & semi-automated DEM editing. He will also address on-going challenges with interferometry-based elevation data and provide an outlook on error compensation strategies (e.g. height reconstruction from radar amplitude data based on machine-learning techniques).

Check the full announcement at https://github.com/OSGeo/grass/releases/tag/8.4.0RC1.

Please support in testing this release candidate.

Highlights

• location becomes project: The Python API, command line, and graphical user interface are now using project instead of location for the main component of the data hiearchy while maintaining backward compatibility.
• v.fill.holes: New tool to remove inner isles and to keep outer boundary only
• i.svm: New support vector machine image classification (SVM)
• r.horizon: Output for multiple points, distances, and many other improvements
• r.univar: Parallelization computation extended statistics
• JSON output format support (format=”json”) in multiple tools (e.g., r.report, r.info, v.db.select, t.rast.list, etc.)
• New grass.jupyter.SeriesMap class for animating series of vectors or rasters
• ipyleaflet integration for grass.jupyter to create a map in ipyleaflet and add GRASS data in a single line
• Greatly simplified the creation of new projects in Python (no more chicken and egg problems) - also in Jupyter notebooks
• GUI: New easy command history navigation through the History browser panel browser panel
• GUI: Further improvements of new single-window GUI with undockable map display window, for a smoother user experience
• New GRASS GIS Programming Style Guide

https://github.com/OSGeo/grass/blob/main/doc/development/style_guide.md

New Addon Tools

• i.eodag https://grass.osgeo.org/grass-stable/manuals/addons/i.eodag.html: Downloads imagery datasets from various providers through the EODAG API.
• r.flowaccumulation https://grass.osgeo.org/grass-stable/manuals/addons/r.flowaccumulation.html: Calculates flow accumulation from a flow direction raster map using the Memory-Efficient Flow Accumulation (MEFA) parallel algorithm by Cho (2023).
• r.fusion https://grass.osgeo.org/grass-stable/manuals/addons/r.fusion.html: image fusion, generalized pan-sharpening.
• r.windfetch: addon for computing wind fetch.
• r.maxent.train https://grass.osgeo.org/grass-stable/manuals/addons/r.maxent.train.html
• r.maxent.predict https://grass.osgeo.org/grass-stable/manuals/addons/r.maxent.predict.html: train, and predict a Maxent model to create a suitability distribution layer.

Riazanoff, S.; Corseaux, A.; Albinet, C.; Strobl, P.A.; López-Vázquez, C.; Guth, P.L.; Tadono, T. Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models. ISPRS Int. J. Geo-Inf. 13, 96. https://doi.org/10.3390/ijgi13030096

Paper abstract:
In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

The posts themselves are quite simple to make. Just write it using markdown and it should work. If you’re new to markdown, this guide can help you with the first steps.

The website is hosted at Github and deployed as a github-page, so not all markdown formatting works. Check this guide to see what works and what doesn’t.

When including figures, make sure they look good on the web without being too heavy and don’t worry about having the image files in a different folder, just keep all the files in the same place, and we’ll organize the files here (images are being stored into /img/posts/some_fig.jpg but you don’t need to use that same path, of course).

Once you’re happy with your post, zip everything and send it to us using the contact form. The content will be moderated by ISG board members before being published.

Resources

• https://www.markdownguide.org/getting-started/
• https://docs.github.com/pt/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax
• https://www.markdownguide.org/tools/github-pages/