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Featured Photo: Ohsome dashboard interface for Heidelberg, Germany.

In the words of Confucius, “The man who moves a mountain begins by carrying away small stones.” As we release OSHDB (OpenStreetMap History Database) Version 1.0, we look back at versions 0.5, 0.6, 0.7, and all the other small improvements to our historical OpenStreetMap database as the small stones allowing us to move the mountain.

The mountain itself was identified by our Scientific and Managing Director Prof. Dr. Alexander Zipf back in 2010. According to HeiGIT’s current product owner for the ohsome and big data team Benjamin Herfort, researchers at the time faced significant hurdles if they intended to study OSM data over time. While it was fairly straightforward to analyze contemporary developments in OSM, creating a framework for looking at the evolution of data proved complicated. “Every researcher had to find their own setup and their own way of crunching OSM data. That’s what we wanted to change.”

Prof. Zipf led the team in setting up a server so that researchers could probe historic OSM data without worrying about any setup and processes besides their study focus. With the release of OSHDB, users of historic OSM data were no longer required to be computer scientists, database engineers, or familiar with running a cluster. OSHDB would do that job for them through an intuitive API.

Since then, the changes to OSHDB have been small and meaningful, focusing on creating proper software through improved internal documentation and testing. Instead of adding any crazy features, releases have concentrated on well-functioning software to enable accessible analysis. Now, after five years of development and testing, the team is releasing OSHDB Version 1.0.

This version continues to allow users to visualize and explore the amount of data and contributions to OSM over time starting from the beginning of OSM itself. Not only are the features in the OSM History Data interesting in and of themselves, ranging from country borders to buildings and turn restrictions, but they also facilitate the investigation of data quality, regional quality comparisons, and allow for the computation of aggregated data statistics.

One of the most important aspects of the database is its usability. Two clicks on the ohsome dashboard allow any researcher, journalist, or citizen scientist to view the evolution of OSM data over time for any region. This temporal change can inform us about data quality as we evaluate saturation and check for currentness. In the case of Heidelberg, for example, the number of buildings has not significantly increased since 2012, at which point we may say that Heidelberg became saturated and data in the area is likely of a high quality. To draw such conclusions, we would also like to check for currentness, looking at when entities were changed or added. For more information on this application, make sure to read our blog post on the topic.

Graphs: Saturation Indicators for Heidelberg, Germany over time using OSHDB.

With our simple API constructed for intuitive use with a wide range of analysis queries in mind, users can work with a lossless dataset including deletions of past objects, erroneous, and partially incomplete data. Data can be viewed as snapshots at specific points in time or as a full history of the region, tag, or entity type to allow for endless use cases and to meet all user needs.

The full list of changes is available on github, with most improvements occurring in the “boring bits” that users will only notice through a smoother analysis process. We would like to highlight one change, however, that will prove useful to many researchers. New OSHDB filters allow practitioners to filter entities by the shape of the geometry. This feature targets a common discussion in the OSM community: how to deal with imperfectly-mapped objects.

When some beginners add objects to OSM, they may make minor mistakes in adding new buildings by drawing edges and not properly aligning rectangular buildings. These small errors can contribute to data quality considerations, a major application of OSHDB. With Version 1.0, users can now filter by rectangular or not-perfectly-rectangular objects and can thus identify distorted shaped in OSM. Filter methods like this addition allow researchers to comment on the quality of their OSM data for a region over time.

This filter method along with the many behind-the-scenes changes in this version and over the evolution of OSHDB contribute to the accessible database that allows researchers to easily interpret data in a way that was not possible in 2010, when OSHDB was only a vision in the minds of our team members. With the release of Version 1.0, we’re proud to offer a simple tool for the important task of data quality analysis. We look forward to carrying away many more small stones in the months and years to come.

Video: Evolution of OSM road network mapping in Heidelberg, Germany since 2007.

In the next few days, we will offer a range of content to celebrate and expand upon this release including demos, features, use cases, and insights into the development process. Keep up with us through our blogs and social media channels!


If you’re on the job market or know someone who is, check out this exciting new opening at GIScience. The offer is included as text below!

You are interested in enhancing methods for analyzing & improving OpenStreetMap data? You are an experienced Spatial Data Scientist innovating geoinformatics methods & workflows? You have profound hands-on experience on contributing to & working with OpenStreetMap (OSM) data?

We are looking as soon as possible for a senior researcher / Postdoc (m/f/d) in the context of a project aiming at developing, evaluating & advancing methods & technologies for OSM data quality analysis. The application domain will be routing & navigation. The goal is to innovate robust & meaningful approaches for evaluating OSM data quality for transportation (fitness for purpose), which actually can be applied in practice. Ensuring transferability of the methods to usage around the globe is a key focus.

You will be working closely together with the teams at both the GIScience Research Group at Heidelberg University, as well as the associated Heidelberg Institute for Geoinformation Technology. At HeiGIT a team is already working at developing a comprehensive open source framework for OSM data analysis. This can be a base for further developments in the analysis of OSM data quality targeted to routing & navigation. In addition GIScience & HeiGIT jointly develop the open source OSM routing platform openrouteservice.org which allows detailed insights & discussions about realizing OSM based mobility services.

We offer an attractive position (up to 100%, part time possible if preferred) in an interdisciplinary dynamic team and in a cutting-edge research field. The department is, among others, a member of the University’s Interdisciplinary Center for Scientific Computing (IWR) and a founding member of the Heidelberg Center for the Environment (HCE). The affiliated institute HeiGIT gGmbH translates the research into practical applications. The Heidelberg University of Excellence (top 3 national university ranking) offers a particularly stimulating interdisciplinary research environment in one of Germany’s most attractive cities, with many attractive personal development & education opportunities.

We expect an PhD and above-average university degree preferably in one of the subjects such as geoinformatics, computer science, data science, geography or similar disciplines. In addition to a strong team spirit & high motivation, excellent & demonstrated broad methodological skills & research experience in GIScience are required, e.g. in topics such as Spatial Data Science & Machine Learning. In addition we expect a deep understanding of OSM, programming skills (e.g. Python, Java or R), the ability to work effective & efficient in a team, as well as excellent communication & presentation skills. The position is to be filled as soon as possible and is initially limited to 3 years, with potential extension to 6 years. The remuneration is according to TV-L E13. Please send your meaningful application documents (cv, certificates, references, etc.) as soon as possible digitally to zipf@uni-heidelberg.de.

We are looking forward to your application!

Der Klimawandel, Grenz- und Wasserkonflikte stellen enorme Herausforderungen für die aktuelle und kommenden Generationen dar. In all diesen Bereichen und darüber hinaus bietet die Geographie einmalige Chancen, zielgerichtet Lösungen zu erarbeiten.

Das Geographische Institut der Universität Heidelberg möchte gemeinsam mit der Fachschaft Geographie, dem HeiGIT(Heidelberg Institute for Geoinformation Technology), GeoDACH e.V. und anderen internationalen Partnerorganisationen Aufmerksamkeit auf das Fach Geographie und dessen Potenzial lenken.

In der Woche vom 14.-19.11.2022 findet die von National Geographic ausgerufene Geography Awareness Week statt. Durch Veranstaltungen wie Mapathons, Keynote-Vorträgen und Events zum sozialen Austausch oder Berufseinblick werden die interdisziplinäre Bedeutung des Fachs sowie die Chancen, die es bietet, herausgestellt. Angesprochen sind explizit auch Menschen außerhalb des Universitätsalltags.

Der breite Anwendungsbereich von geographischem Wissen im Alltag spielt bei den Veranstaltungen eine entscheidende Rolle. Verglichen mit anderen Fächern bietet die Geographie mit ihrem breiten Themenspektrum von Geomorphologie über wirtschaftliche und politische Fragestellungen bis hin zur Informatik die Möglichkeit, Brücken zwischen Disziplinen zu schlagen. Sie spielt damit eine zentrale Rolle in der gemeinsamen Bewältigung drängender Zukunftsfragen.


  • Di.-Do., ganztägig, INF 348: Pflanzentauschbörse. Im Foyer des geographischen Instituts im Neuenheimer Feld 348 werden Pflanzen und Pflanzensamen gesammelt. Jede Person, die eine Pflanze mitbringt, darf sich eine bereits vorhandene nehmen.

  • Di., 11 Uhr, STAR der Berliner Straße 48: Q&A „Ehrenamt und gemeinnützige Tätigkeiten im geographischen Kontext“. GeoDACH e.V., die Vertretung deutschsprachiger Geographiestudierender stellt sich vor und bietet ein Forum zum lockeren Austausch für engagiere Menschen und solche, die sich engagieren wollen. Gemeinsam mit dem HeiGIT soll über gemeinnützige Tätigkeiten in der Geographie gesprochen werden.
  • Di., 19 Uhr, INF 227: Gegen eine kleine Spende an die Heidelberger Geographische Gesellschaft (HGG) können Interessierte einen Vortrag von Frank Keppler, Professor für Biogeochemie am Institut für Geowissenschaften der Universität Heidelberg, besuchen. Der Vortrag mit dem Titel „Methan: Energieträger, Klimagas und bioaktive Substanz“ ist Teil der HGG-Vortragsreihe „Hothouse Earth“.
  • Mi., 14 Uhr, Hörsaal der Berliner Straße 48: Interaktiver Vortrag „Experimente in der Wirtschaftsgeographie“ von Johannes Glückler, Professor für Wirtschafts- und Sozialgeographie am Geographischen Institut der Universität Heidelberg. Diese interaktive Veranstaltung richtet sich neben Studierenden vor allem an Studieninteressierte, die im Rahmen des Studieninformationstages die Universität Heidelberg besuchen.
  • Do., 11 Uhr, Seminarraum 015 INF 348: Interaktiver Vortrag „Relevanz des Raumes: Geographische Analysen in Umwelt, Entwicklung und Gesundheit“ von apl. Prof. Dr. Sven Lautenbach. Der Vortrag soll Transdisziplinarität als Stärke der Geographie hervorheben und konkrete Anwendungsbereiche von geographischen Methoden aufzeigen.
  • Do., 17 Uhr, Hörsaal der Berliner Straße 48: Videovortrag „Klimawissen Student LAB“ mit anschließendem Q&A von Studierenden und Dozierenden. Die Geography Awareness Week soll mit einem kurzen Videovortrag und anschließendem, lockeren Beisammensein bei Speis und Trank ausklingen. Hierzu sind alle interessierten Menschen eingeladen.

Mehr Informationen.

Beteiligte Organisationen bei der Geography Awareness Week in Heidelberg:

Poster #EUGAW2022 in voller Auflösung.

The commune Sandhausen (Baden-Württemberg) got its name from the inland dune, which is located in the area of the village. In 2021 and 2022, the 3DGeo group of Heidelberg University conducted UAV-based and ground-based surveys of three areas of the inland dune of Sandhausen to acquire 3D point clouds and orthophotos. The dataset is freely and openly accessible on the PANGAEA data repository:

Weiser, Hannah; Winiwarter, Lukas; Zahs, Vivien; Weiser, Peter; Anders, Katharina; Höfle, Bernhard (2022): UAV-Photogrammetry, UAV laser scanning and terrestrial laser scanning point clouds of the inland dune in Sandhausen, Baden-Württemberg, Germany. PANGAEA, https://doi.org/10.1594/PANGAEA.949228


View of the point cloud of Zugmantel-Bandholz in the potree renderer

The inland dune formed during the last glacial period (Würm) by drifting sands from the Rhine Valley. The age of the dune is estimated at around 10,000 to 15,000 years. After glaciation, the dunes were forested and experienced little change for a while. Only in the High Medieval period, the dunes were partially deforested to do agriculture. Intensive use as pasture forest led to destruction in parts. In 1950, the areas were placed under nature protection. In the protected areas, the steppe vegetation could be preserved or re-established, which today is considered a botanical peculiarity and floristic rarity. The fauna of the inland dune is also remarkable and worthy of protection. Particularly among the insects, there are a number of specialists that otherwise occur only very rarely.

Different nature protection measures are undertaken, such as species registration and monitoring, mowing, grazing, or installing fences to limit disturbances. In particular, climate change with increasing heat and dry periods influences the characteristic of the dunes and leads to shift of Mediterranean species to Sandhausen but also loss of other species.

Our dataset captures the current state of the inland dune in 2021 and 2022, in particular the topography and vegetation cover in different seasons of the year. This supports the monitoring of the area and the improvement of protection measures. The products can also be helpful in public outreach and environmental education.

We surveyed three dune areas in Sandhausen:

Our dataset encompasses:

  • UAV-based photogrammetric point clouds
  • UAV-based photogrammetric orthophotos
  • UAV images which were used to create the point clouds and orthophotos
  • Metadata on the images and the photogrammetric processing, including locations of ground control points
  • Terrestrial laser scanning point clouds
  • UAV-borne laser scanning point clouds
Check out the point clouds of Zugmantel-Bandholz in our potree viewer!
German website of the inland dune: http://duene-sandhausen.de/

As of 02.11.2022 we have reached objects 10,107,826,483* in our oshdb. As such, we think it is most definitely time to celebrate!

*For information on the background this figure, read our Basic guide to OSM data filtering ;)

This blog post is all about reaching 10 billion objects in OpenStreetMap. For the database to aggregate such a wonderful horde, we want to thank our active and engaged mapping community.

As a case study, we decided to sit down and take a moment to plot the development of the amenity=bench tag in Bolivia, a tag and boundary never before used in any of the ohsome series so far. We’re excited to take these parameters for a spin to mark this special occasion!


As usual we got our input boundary here. As for the querry itself, following filter conditions led to our output results:

filter=type=node and amenity=bench
showMetadata=yes (optional)

As you can see in the figure above, there were zero benches tagged in Bolivia until December 2012. This could be due to these kinds of objects not being a top priority when it comes to mapping infrastructure. Still, benches help paint a fuller picture of the real world as long as the objects are marked with a high degree of accuracy to ensure data quality. The main increases in bench tagging numbers were during summer 2017 and after late 2020. The latter trend could be due to several factors, including the pandemic.

Although the overall values for bench tagging in Bolivia may not be incredibly high, they too helped our community to achieve the 10th billionth object. Even small contributions can help make OpenStreetMap a very rich source of free, (ideally) accurate and current geospatial information.

This example illustrates how ohsome API and the OpenStreetMap itself can help visualize the development of local knowledge throughout the years as the community grows and external events allow contributors to increase available data and data quality.

Thank you for joining us on this journey and taking the time to read about our “ohsome” news! Stay tuned as there’s always more news coming!

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

We have exciting ohsome news! As many of you know, past OSHDB updates happened weekly and led to a delay between the data in OpenStreetMap (OSM) and our database. Basically, you had to wait up to a week to see changes in OSM reflected in the OSHDB. From this point forward, that lag is a thing of the past! Well… almost.

With our latest release, the data of the OSHDB is updated in near real-time every hour. Now, it is possible to send requests to the ohsome API and explore the most recent developments within OSM. With this improvement, you can answer questions like the following:

This figure gives you the full temporal detail of OSM. Running an analysis such as this one can help you understand mapping activity during mapathons or when you need immediate feedback. This might also reveal when the obligatory mapathon-pizza-break happened.

In this figure, we see that there are about 50-70 daily active users in Denmark. Let’s check this regularly and find out if we will see 100 active mappers sometime in the future. You can use our scripts to monitor and run it on your own.

The hourly mapping activity for Heidelberg shows some interesting patterns. On 2022-09-02, Heidelberg’s mapper community edited OSM either in the morning between 7-11 am or in the evening between 8-9 pm. No Night-Time-Mappers found.

If you want to run the analysis on your own, you can check out this snippet and easily adjust the parameters. If you are curious as to how up-to-date the data of the OSHDB is, take a look at the metadata of the ohsome API. The “toTimestamp” attribute will tell you when the data was last updated.

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

Welcome back to our ohsome region of the month series! In this post, we’ll take a look at the temporal development of highways and added smoothness-information, as well as social facilities and updates on map development for Kyiv. Before we start, if you’re new to the series, make sure to check out previous installments of the series such as this one, which also deals with the highway tag, or this one about forests in Canada. For now, let’s take a look at Kyiv’s highways and social facilities.

Data & Requests

As usual, our input boundary was extracted here in GeoJSON format. For our requests, we’ve decided to do a long-term request as well as a short-term request for each endpoint used. The following filter conditions were set.

Social Facilities:

filter = type:way and social_facility=*

filter2=type:way and social_facility=* and social_facility:for=*

time = 2022-01-01/2022-09-01/P1D; 2012-01-01/2022-09-01/P2W

endpoint = elements/area/ratio


filter = type:way and highway=*

filter2=type:way and highway=* and smoothness=*

time = 2022-01-01/2022-09-01/P1D; 2012-01-01/2022-09-01/P2W

endpoint = elements/length/ratio

When sending a ratio-request, you’ll get three different columns of data - one for each filter and a third with the ratio values. After setting these values, we created the following figures.

Social Facilities

The figure above shows both the long-term (left) development of ratio values for objects tagged with social_facility=*and social_facility=* and social_facility:for=* as well as the short-term development (right) since January of this year. Overall, there were no values available until June of 2018, yet most of the added social_facility=* objects did have their purpose (social_facility:for=*) updated right away. An increase took place during the second half of 2019 and another during the second half of 2021. Except for a slight increase around February 2022, values were more or less stagnant until a small decrease during June. One trend to note is the increase of ratio during late August.

In the figures above you can see the temporal development of the tags themselves. Again, on the left side, the long-term development is depicted and on the right side you can see the development since January 2022. The output values are shown in area in km2. Generally, there is a quite similar temporal development for both filter-conditions, however, a few exceptions can be spotted. For example, there has been no increase in social_facility=* and social_facility:for=* between March and August 2022, although objects have been tagged with social_facility. A comparable occurrence can be found during the early months of 2019.


As in the first figure, the development of ratio values is plotted in the figure above for the ratio of highway=* and smoothness=* to highway=* in this graph. Long-term developments can be seen in the left graph and short-term developments within the right. Whilst social-facility development proceeds rather slowly, but with a high completeness, highway-smoothness ratio has been steadily increasing, but never reaching even 5% of all highway objects. There has been a notable increase mid-January 2022 after which development stagnated until the second half of May. Peaking in June, the ratio values slightly declined again in the second half of the year.

For a better visual on the development of both ratio components, temporal development is displayed in the figure above. There is a mostly steady increase in both cases, but highway=* shows a steeper slope and much higher values, as expected. In contrast to long-term development, values remain stagnant throughout 2022.

Ultimately, we see that there have been no major increases in the values for the highway tag over the course of this year. In the case of the social_facility-tag, this observation is less straightforward. Over the course of time, several data jumps are observed since January 2022, but always to a lesser extent than during the preceding time period, the exception being August. The first data jump in 2022 took place in the period from February 23 to February 24 during the same time as Russian troops invaded Ukraine. On May 26 and June 07, the values for social_facility-tags increased again. Among other events, the May 26 increase coincides with an attack on Kharkiv while the June 07 jump occurred during Pramila Patten’s speech at the 9056th meeting of the UN Security Council on Sexual Violence during the War. The most prominent increase takes place from August 25 to 26, at which time the nuclear power plant in Zaporizhzhia was also under reoccurring shelling. All of the above-mentioned reasons may be (though must not necessarily be) related to the observable jumps in the social_facility-tag. Future observing of the tag’s development and a closer examination of its tagging character may prove insightful.

Thank you for reading this post in our ohsome region of the month-series. Stay tuned for more news from the world of ohsome API.

If you wish to run the analysis on your own, you can check out this snippet and easily adjust the parameters. If you are curious as to how up-to-date the data of the OSHDB is, take a look at the metadata of the ohsome API. The “toTimestamp” attribute will tell you when the data was last updated.

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

The software HELIOS++ simulates the laser scanning of a given virtual scene that can be composed of different spatial primitives and 3D meshes with distinct granularity. The high computational cost of this type of simulation software demands efficient computational solutions. Classical solutions based on GPU are not well suited when irregular geometries compose the scene combining different primitives and physics models because they lead to different computation branches. In this paper, we explore the usage of parallelization strategies based on static and dynamic workload balancing and heuristic optimization strategies to speed up the ray tracing process based on a k-dimensional tree (KDT). Using HELIOS++ as our case study, we analyze the performance of our algorithms on different parallel computers, including the CESGA FinisTerrae-II supercomputer. There is a significant performance boost in all cases, with the decrease in computation time ranging from 89.5% to 99.4%. Our results show that the proposed algorithms can boost the performance of any software that relies heavily on a KDT or a similar data structure, as well as those that spend most of the time computing with only a few synchronization barriers. Hence, the algorithms presented in this paper improve performance, whether computed on personal computers or supercomputers.

Global speedups between best strategy and simple sequential for different HELIOS++ input scenes.

Find all details in the full paper:

Esmorís, A. M., Yermo, M., Weiser, H., Winiwarter, L., Höfle, B. & Rivera, F.F. (2022): Virtual LiDAR simulation as a high performance computing challenge: Towards HPC HELIOS++. IEEE Access 10, pp. 105052-105073.


About HELIOS++

HELIOS++ is an open source software project with a modern implementation in C++, including Python bindings to allow easy use in existing workflows. The code and ready-for-use precompiled versions are hosted on GitHub. We invite interested researchers and developers to contribute to further development of this project by submitting pull requests. We also host an extensive wiki, where the complete functionality of HELIOS++ is documented.

HELIOS++ is actively developed in great collaboration with CITIUS (Centro Singular de Investigacìon en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela (USC))

If you use HELIOS++ in your work, please cite:

HELIOS++ is funded by the German Research Foundation (DGF) in frame of the SYSSIFOSS project (funding code: 411263134) and the VirtuaLearn3D project (funding code: 496418931), and by the Federal Ministry for Education and Research (BMBF) in frame of the LOKI project (funding code: 03G0890A).

The fourth edition of the Innsbruck Summer School of Alpine Research took place in September 2022, finally back in the lovely mountain landscape of the Ötztal valley in Tyrol, Austria. Once again, 40 participants - young researchers from all over the world - gathered in Obergurgl to learn and exchange about new concepts and solutions for mapping and monitoring mountain environments undergoing continuous change.

This time, a news team from the Austrian ORF accompanied the field work and featured the summer school and techniques of 3D environmental monitoring in a story (in German): https://tirol.orf.at/stories/3176928/

The story was also streamed in this short TV video:
Link to video

Find all the information on keynotes, lectures, and the participants’ research topics in the proceedings:

Rutzinger, M., Anders, K., Bremer, M., Eltner, A., Höfle, B., Lindenbergh, R., Mayr, A., Oude Elberink, S., Pirotti, F., Tolksdorf, H. & Zieher, T. (2022): Sensing Mountains: Innsbruck Summer School of Alpine Research 2022 – Close Range Sensing Techniques in Alpine Terrain. pp. 1-130. DOI: 10.15203-99106-081-9.

If you are interested in participating in the next summer school in 2024, stay tuned for updates and find some more impressions on the summer schools in 2015, 2017 and 2019. On Twitter, you can follow us via #SensingMountains

The summer school organizing committee is thankful to all sponsors and the keynote speakers who made this exciting and successful event possible!

Morphologies of highly complex star dunes are the result of aeolian dynamics in past and present times. These dynamics reflect climatic conditions and associated forces like sediment availability and vegetation cover, as well as feedbacks with adjacent environments. However, an understanding of aeolian dynamics on star dune morphometries is still lacking sufficient detail, and their influence on formation and evolution remains unclear.

In a new paper, the research groups on Geomorphology and 3DGeo at Heidelberg University present an investigation of the dynamics of a complex star dune in Erg Chebbi, Morocco. The analysis compares wind measurements to morphometric changes derived from multitemporal high-accuracy 3D observations during two surveys (October 2018 and February 2020) to derive the reaction of a star dune surface to an observed constant unimodal sand-moving wind. TLS point clouds are used for morphometric analysis as well as direct surface change analysis, which relates to sand transport.

3D surface changes between 2018 and 2020 indicating accumulation (positive changes) and erosion (negative changes) and corresponding sand rose (Herzog et al., 2022).

The results point to a self-sustained dune growth, which has not yet been described in such spatial detail. Steep slopes, often found on star dunes around the globe, seem to partly hinder upslope sand transport. The analysis approach of combining meteorological data and high-resolution multitemporal 3D elevation models can be used for monitoring all dune forms and contributes to a general understanding of dune dynamics and evolution.

Find all details in the full paper:

Herzog, M., Anders, K., Höfle, B. & Bubenzer, O. (2022): Capturing complex star dune dynamics—repeated highly accurate surveys combining multitemporal 3D topographic measurements and local wind data. Earth Surface Processes and Landforms, 47 (11), pp. 2726-2739. DOI: 10.1002/esp.5420.

Open Access funding enabled and organized by Projekt DEAL.

The data used for analyses in this paper is available on the data repository of Heidelberg University: https://doi.org/10.11588/data/ZAMGCL.

Terrestrial laser scanning (TLS) during field campaign in Erg Chebbi, Morocco.

Find more information about the field campaigns in previous blog posts.

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