Wind erosion constitutes a prominent land degradation process in regions of Hungary characterized by low annual precipitation. In these areas, it poses significant challenges to agricultural productivity and adversely impacts soil and environmental quality. Presently, human activities exert a more pronounced influence on the endangered areas of Hungary in comparison to climate-related factors. It is noteworthy that the wind erodibility of Hungarian soils not only poses a soil conservation challenge but also gives rise to economic ramifications, such as nutrient loss, as well as environmental and human health concerns. Within agricultural landscapes, wind erosion contributes to the removal and transportation of the finest and biologically active soil fractions, rich in organic matter and nutrients.

High-resolution topographic surveys have become integral for assessing volumetric changes in sand dune mobility and mapping wind erosion. While Unmanned Aerial Systems (UAS) surveys have been extensively employed for erosion rates exceeding the decimeter scale, Terrestrial Laser Scanning (TLS) surveys have demonstrated efficiency in capturing more extensive negative erosional forms, even in a vertical orientation. To enhance the field of view, a mounting framework can be implemented to elevate the TLS. However, determining centimeter-scale material displacement in flat terrain conditions remains challenging and requires an increased number of scanning positions.

To identify optimal settings for surveying centimeter-scale wind erosion magnitudes, we conducted combined multi-temporal TLS and UAS surveys at the Westsik experimental site near Nyíregyháza during the spring of 2023. This site features dune topography with a height of 6 meters. Our investigations encompassed various UAS image acquisition modes, involving different flight altitudes and camera settings, utilizing a DJI Matrice M210 RTK v2 drone and a Zenmuse X7 24 mm lens. Additionally, we generated diverse point clouds through various scanning scenarios using a Trimble X7 TLS device. In the data processing phase, we explored multiple co-registration algorithms to address the challenge of larger Root Mean Square Error (RMSE) in Digital Terrain Models (DTMs) from UAS Structure from Motion (SfM) compared to the actual wind erosion rates.

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The research is supported by the NKFI K138079 project.

How to cite: Bertalan, L., Négyesi, G., Szabó, G., Túri, Z., and Szabó, S.: Evaluating the efficacy of multitemporal TLS and UAS surveys for quantifying wind erosion magnitudes of sand dune topography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4399, https://doi.org/10.5194/egusphere-egu24-4399, 2024.

Landslides are a pervasive natural hazard with significant societal and environmental impacts. In addressing the critical need for accurate landslide detection and monitoring, our previous research introduced a feature-based monitoring method enhanced by histogram analyses, straddling a middle ground between point-based and point cloud-based methods. This paper expands upon that foundation, introducing an innovative contour line extraction technique from various epochs to precisely identify areas prone to deformation. This refined focus diverges from conventional methodologies that analyze entire point clouds. By applying on regions where contour lines do not match, indicating potential ground movement, we significantly elevate the efficiency and precision of our feature-based monitoring system.

One of the principal challenges of feature-based monitoring is managing a substantial number of outliers. Our prior research tackled this issue effectively by integrating feature tracking with histogram analysis, thereby filtering these outliers from the final results. However, the process of extracting features from each patch and matching them with corresponding patches from different epochs was time-intensive.

The incorporation of contour line extraction into our workflow, using high-resolution laser scanner data, allows for a more focused and efficient analysis. We can now identify and analyze areas of landscape alteration with greater accuracy. This approach limits the application of feature tracking and histogram analysis to these critical areas, thus streamlining the process and significantly reducing computational demands. This focused methodology not only accelerates data processing but also enhances the accuracy of landslide predictions.

Our findings indicate a substantial improvement in the efficiency of landslide monitoring methods. This methodology represents a promising advancement in geospatial analysis, particularly for environmental monitoring and risk management in regions susceptible to landslides. This research contributes to the ongoing efforts to develop more effective, efficient, and accurate approaches to landslide monitoring, ultimately aiding in better informed and timely decision-making processes for hazard mitigation and risk management.

How to cite: Hosseini, K., Hummelsberger, J., Czerwonka-Schröder, D., and Holst, C.: Enhancing 3D Feature-based Landslide Monitoring Efficiency by Integrating Contour Lines in Laser Scanner Point Clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5670, https://doi.org/10.5194/egusphere-egu24-5670, 2024.

The water level and discharge of river are crucial parameters to understand the variance in riverbed scour. The detail behavior of scouring can be studied by the hydraulic simulation. The grain-size distribution of riverbed is also one of crucial parameter for modeling. Thus, how to investigate the grain-size of riverbed efficiently and swiftly is the urgent issue. However, the conventional measurement methods including Wolman counts (particles sampled at a fixed interval) which are a long and laborious task cannot survey the grain-size efficiently in the large area. In recent years, with an advantage of image segmentation and recognition has been applied to the investigation of grain-size, for example, capturing images through UAV and generating orthoimage is one of commonly used image technique. Although above the method can investigate the grain-size in the large area, it does not provide the information in the field immediately. Hence, a recent study developed the low-cost portable scanner to obtain the information of grain-size distribution in the field. However, the calibrating parameters of camera (e.g., height camera capture) are necessary before survey, and the uncertainties in calculation of image resolution will significantly affect the accuracy of grain-size analysis. Therefore, this study provides the additional algorithm to analyze the grain-size by using RGB-D image as inputs. The application of RGB-D can be categorized into two-dimensional (2D) and three-dimensional (3D) spaces. In a case of 2D, it integrates depth information with traditional RGB image processing to separate the grain-size of riverbed from the background (e.g., bottomland). Furthermore, depth information is also applied for grain-size edge detection. In a case of 3D, the collected RGB-D image information is transformed into point cloud data, then extract 3D features of grain particle by Deep learning, specifically PointNet. Our study demonstrates that clustering of 3D features can achieve the automatic identification of particle. The grain-size of particle can also be estimated by fitting 3D ellipsoid geometry. In the end, results show the grain-size distribution curves with the RGB、RGB-D、PointNet recognition, and compare with the true observations. 3D image information provides the cloud points of grain object, leading the possibility of estimating the 3D geometric morphology of the object. Our study successfully overcomes the limitations of conventional RGB-based process, which could only capture size and shape information in 2D planar. RGB-D-based image recognition, is an innovative technique for the hydraulic problem, not only advances survey efficiency but also addresses the intricate steps required for field investigations.

Key words: Riverbed grain size, RGB-D image, Point cloud, Deep Learning

How to cite: Chen, B. R. and Chao, W. A.: A point-cloud deep learning model based on RGB-D images: Application of riverbed grain size survey, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14087, https://doi.org/10.5194/egusphere-egu24-14087, 2024.

We advance the characterization of landscape dynamics through analysis of point cloud time series by integrating virtual laser scanning, machine learning and innovative open source methods for 4D change analysis. We present a novel approach for automatic identification of different surface activity types in real-world 4D geospatial data using a machine learning model trained exclusively on simulated data.

Our method focuses on classifying surface activity types based on spatiotemporal features. We generate training data using virtual laser scanning of a dynamic coastal scene with artificially induced surface changes. Scenes with surface change are generated using geographic knowledge and the concept of 4D objects-by-change (4D-OBCs) [1, 2], which represent spatiotemporal subsets of the scene that exhibit change with similar properties. A realistic 3D scene modelling is essential for accurately replicating the dynamic nature of coastal landscapes, where morphological changes are driven by both natural processes and anthropogenic activities.

The Earth's landscapes exhibit complex dynamics, spanning large spatiotemporal scales, from high-mountain glaciers to sandy coastlines. The challenge lies in effectively detecting and classifying diverse surface activities with varying magnitudes, spatial extents, velocities, and return frequencies. Effective characterization of these dynamics is crucial for understanding the underlying environmental processes and their interplay with human activities. Supervised machine learning classification of surface activities from point cloud time series is challenging due to the limited availability of comprehensive and diverse real-world datasets for training and validation. Our approach combines virtual laser scanning with machine learning-based classification, enabling the generation of comprehensive training datasets covering the full spectrum of expected change patterns [3].

In our approach, the simulation of LiDAR point clouds is performed in the open-source framework HELIOS++ [4, 5]. HELIOS++ allows the flexible simulation of custom LiDAR campaigns with diverse acquisition modes and settings together with automatic annotations of artificially induced surface changes. We train a supervised machine learning model to classify synthetic 4D-OBCs into typical surface activity types of a sandy beach (e.g. dune erosion/accretion, sediment transport, etc.). Moreover, we investigate descriptors for 4D-OBCs, assessing their suitability for representing general types of surface activity (transferable between use cases) and types specific to particular surface processes.

We evaluate our model for 4D-OBC classification in terms of its capacity to discriminate surface activity types in a real-world dataset of a sandy beach in the Netherlands [6]. 4D-OBCs are extracted, classified into our target classes and validated with manually labelled reference data based on expert evaluation.

Our study showcases the efficacy of coupling virtual laser scanning, innovative open-source 4D change analysis methods, and machine learning for classifying natural surface changes [7]. Our findings not only contribute to advancing the understanding of landscape dynamics but also provide a promising approach to mitigating environmental challenges.

REFERENCES

[1] Anders et al. (2022): DOI: https://doi.org/10.5194/egusphere-egu22-4225

[2] py4dgeo: https://github.com/3dgeo-heidelberg/py4dgeo 

[3] Zahs et al. (2022): DOI: https://doi.org/10.1016/j.jag.2023.103406

[4] HELIOS++: https://github.com/3dgeo-heidelberg/helios

[5] Winiwarter et al. (2022): DOI: https://doi.org/10.1016/j.rse.2021.112772 

[6] Vos et al. (2022): DOI: https://doi.org/10.1038/s41597-022-01291-9

[7] CharAct4D: www.uni-heidelberg.de/charact4d

How to cite: Zahs, V., Höfle, B., Federer, M., Weiser, H., Tabernig, R., and Anders, K.: Automatic Classification of Surface Activity Types from Geographic 4D Monitoring Combining Virtual Laser Scanning, Change Analysis and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1640, https://doi.org/10.5194/egusphere-egu24-1640, 2024.

Our study focuses on the geologically, topographically, and oceanographically complex region of Disko Bay in West Greenland. Disko Bay is also considered a marine biodiversity hotspot in Greenland. Given the impact of commercial fishing on seafloor integrity in the area, seafloor habitats studies are crucial for sustainable use of marine resources. One of the key fishery resources in Greenland, as well as in the North Atlantic Ocean, is northern shrimp.

In this study we analyzed multiple (1) monitoring datasets from 2010 to 2019, including data from shrimp and fish surveys, commercial shrimp fishery catches, satellite chlorophyll data, and (2) seafloor models, encompassing high-resolution (25 x 25 m) multibeam data with a low-resolution (200 x 200 m) IBCAO grid. Using multivariate regression analysis and spatial linear mixed-effect model we assessed the impact of physical (water depth, bottom water temperature, sediment type), biological (chlorophyll a, Greenland halibut predation), and anthropogenic factors (shrimp fishery catch and effort) on shrimp density in the area. The resulting high-resolution predictive model of northern shrimp distribution in Disko Bay is the first model of this kind developed for an Arctic area.

Our findings reveal that shrimp density is significantly associated with static habitat factors, namely sediment type and water depth, explaining 34% of the variation. The optimal shrimp habitat is characterized by medium-deep water (approximately 150-350 m) and mixed sediments, primarily in the north-eastern, south-eastern, and north-western Disko Bay. This pioneering study highlights the importance of seafloor habitat mapping and modeling, providing fundamental geophysical knowledge necessary for long-term sustainable use of marine resources in Greenland.

The developed high-resolution model contributes to a better understanding of detailed patterns in northern shrimp distribution in the Arctic, offering valuable insights for stock assessments and sustainable fishery management. This novel approach to seafloor habitat mapping supports the broader goal of ensuring the responsible utilization of marine resources, aligning with principles of environmental conservation and fisheries management. Our work serves as a foundation for ongoing efforts to balance economic interests with the preservation of marine ecosystems, fostering a harmonious coexistence between human activities and the fragile Arctic environment.

How to cite: Krawczyk, D., Vonnahme, T., Burmeister, A.-D., Maier, S., Blicher, M., Meire, L., and Nygaard, R.: Arctic puzzle: pioneering a shrimp habitat model in topographically complex Disko Bay (West Greenland), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5757, https://doi.org/10.5194/egusphere-egu24-5757, 2024.

The Maltese coasts extend for approximately 273km, representing a notable resource for the country and of one of its pillar economies, the tourism sector. Natural processes and anthropic interventions continue to threaten Malta's coastal morphology, shaping its landscape and triggering soil erosion phenomena. Therefore, many research projects (Colica et al., 2021, 2022 and 2023) have concentrated their work on the investigation and monitoring of the instability of cliffs and the erosion of pocket beaches. The results of such activities can be widely disseminated and shared with expert and non-expert users through web mapping, which has only been used in a very limited way in collaborative coastal management and monitoring by different entities in Malta. This study describes the performance of a WebGIS designed to disseminate the results of innovative geomatic investigations for monitoring and analyzing erosion risk, performed by the Research and Planning Unit within the Public Works Department of Malta. While aiming to include the entire national coastline, three study areas along the NE and NW regional coasts of the island of Malta have already been implemented as pilot cases. This WebGIS was generated using ArcGIS pro software by ESRI and a user-friendly interactive interface has been programmed to help users view in 2D and 3D, satisfying both multi-temporal and multi-scale perspectives. It is envisaged that through further development and wider dissemination there will be a stronger uptake across different agencies involved in coastal risk assessment, monitoring and management.

References

Colica, E., D’Amico, S., Iannucci, R., Martino, S., Gauci, A., Galone, L., ... & Paciello, A. (2021). Using unmanned aerial vehicle photogrammetry for digital geological surveys: Case study of Selmun promontory, northern of Malta. Environmental Earth Sciences, 80, 1-14.

Colica, E. (2022). Geophysics and geomatics methods for coastal monitoring and hazard evaluation.

Colica, E., Galone, L., D’Amico, S., Gauci, A., Iannucci, R., Martino, S., ... & Valentino, G. (2023). Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images. Remote Sensing, 15(5), 1465.

How to cite: Colica, E., Fenech, D., Gauci, C., and Buhagiar, G.: Integrating structure-from-motion photogrammetry with 3D webGIS for risk assessment, mapping and monitoring of coastal area changes in the Maltese archipelago, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16939, https://doi.org/10.5194/egusphere-egu24-16939, 2024.

The acquisition of aerial photographs for cartographic applications started in the 1930s, and more intensively after World War II. Such old, often panchromatic, imagery offers metre to sub-metre scale spatial resolution over landscapes that have significantly evolved over the decades. Before the appearance of the first digital aerial camera systems at the end of the 20^th Century, surveys were performed with analogue metric cameras, with images acquired on films or glass plates and, next, developed on photo papers. In Europe and North America, several institutions hold unique collections of historical aerial photographs having local, national and, in some cases, colonial coverages. They represent invaluable opportunities for environmental studies, allowing the comparison with today’s land use land cover, and the analysis of long-term surface displacements.

Initially, the photogrammetric processing of analogue aerial photographs would require expensive equipment, specialised operators, and significant processing time. Thanks to the digital revolution of the past two decades and the development of modern digital photogrammetric approaches, the processing of this type of image datasets has become less cumbersome, time consuming and expensive, at least in theory. In practice, this is more complex, with digitising and processing issues related to the ageing and quality of conservation of the aerial photographs, the potential distortions created during the digitising process, and the lack of ancillary data, such as, flight plans, and camera calibration reports. The limited overlap between photographs, typically 60 % and 10-20 %, along-track and across-track, respectively, make their processing with Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry poorly reliable to accurately reconstruct the topography and orthorectify the images. Given the fact that some collections reach up to millions of historical aerial photographs, the digitising, pre-processing, and photogrammetric processing of these images remain a challenge that must be properly tackle if we would like to ensure their preservation and large-scale valorisation.

In the present work, we describe the mass-digitising, digital image pre-processing and photogrammetric processing approaches implemented at the Royal Museum for Central Africa (RMCA, Belgium) to preserve and valorise the collection of >320,000 historical aerial photographs conserved in this federal institution. This imagery was acquired between the 1940’s and the 1980’s, over Central Africa, and mostly D.R. Congo, Rwanda and Burundi. For the digitising, a system of parallelized flatbed scanners controlled by a Linux computer and a self-developed software allows speeding-up the scanning of the entire collection in only few years. A series of Python scripts were developed and combined to allow a swift pre-processing that prepare and optimise the digitised images for photogrammetric processing. Finally, a SfM-MVS photogrammetric approach adapted to historical aerial photos is used. Examples of application for geo-hydrological hazards studies in the western branch of the East African Rift are shown.

How to cite: Smets, B., Dille, A., Dewitte, O., and Kervyn, F.: Digitising, pre-processing and photogrammetric processing of historical aerial photographs for the production of high resolution orthomosaics and the study of geohazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2356, https://doi.org/10.5194/egusphere-egu24-2356, 2024.

Historical aerial images, captured by film cameras in the previous century, have emerged as valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, historical aerial images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Using photogrammetric techniques on stereo-images enables extracting 3D information to reconstruct Digital Surface Models (DSMs), and orthoimages.

This study presents a highly automated photogrammetric approach for generating nationwide DSMs for Switzerland at 1 m resolution using aerial stereo-images acquired between 1979 and 2006. The 8-bit scanned images, with known exterior and interior orientation, were processed using BAE Systems' SocetSet (v5.6.0) with the "Next-Generation Automatic Terrain Extraction" (NGATE) package for DSM generation. The primary objective of the study is to derive four nationwide DSMs for the epochs 1979-1985, 1985-1991, 1991-1998, and 1998-2006. The study assesses DSM quality in terms of vertical accuracy and completeness of image matching across different land cover types, with a focus on forest dynamics and management research.

The elevation accuracy of the generated DSMs was assessed using two reference datasets. Firstly, the elevation differences between a nationwide reference Digital Terrain Model (DTM - swissAlti3d 2017 by Swisstopo) and the generated DSMs were calculated on points classified as "sealed surface". Secondly, elevation values of the DSMs were compared to approximately 500 independent geodetic points distributed across the country. Six study areas were chosen to assess completeness, and it was calculated as the percentage of successfully matched points to the potential total number of matched points within a predefined area. This assessment was conducted for six land cover classes based on the land cover/land-use statistics dataset from the Federal Office of Statistics.

Across the entire country, the median elevation accuracy of the DSMs on sealed points ranges between 0.28 to 0.53 m, with a Normalized Median Absolute Deviation (NMAD) of around 1 m (maximum 1.41 m) and an RMSE of a maximum of 3.90 m. The elevation differences between geodetic points and DSMs show higher accuracy, with a median value of a maximum of 0.05 m and an NMAD smaller than 1 m. Completeness results reveal mean completeness between 64 % to 98 % for the classes "glacial and perpetual snow" and "sealed surfaces," respectively and 93 % specifically for the “closed forest” class.

This work demonstrates the feasibility of generating accurate DSM time series (spanning four epochs) from historical scanned images for the entire Switzerland in a highly automated manner. The resulting DSMs will be available upon publication, providing an excellent opportunity to detect major surface changes, such as forest dynamics.

How to cite: Ginzler, C., Piermattei, L., Marty, M., and Waser, L. T.: Four nationwide Digital Surface Models from airborne historical stereo-images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5142, https://doi.org/10.5194/egusphere-egu24-5142, 2024.

Rural landscapes inherited from the past are marked by a strong interaction between man and nature, a relationship rooted in a long history that testifies to the importance of the landscape as one of the most historically representative expressions of a country's cultural identity.

In this broad context, olive groves markedly characterize the agricultural landscape of many European rural areas, particularly in the Mediterranean region. Along with other rural landscapes, they form a semi-natural environment that can contribute to biodiversity conservation, soil protection and ecosystem resilience.

In addition to the global increase in temperatures, the main threats affecting these agrarian landscapes include the abandonment of traditional practices and the intensification of cultivation through the installation of irregular, intensive and overly dense planting beds.

The Land Cover classification and change-detection can provide useful indications for the restoration, conservation, and enhancement of olive groves

The objective of this work was to identify , rural landscapes in the Lazio region with characteristics of historical interest and determine their level of conservation. In particular, it was investigated the olive landscape of Cures (historic province of Sabina) trough a multi-temporal analysis of literature and cartographic information (e.g. orthophotos from the Italian Aeronautical Group flight of 1954)

The technique concerns the VASA (Historical Environmental Assessment) methodology, which allows the temporal evaluation of a given landscape and can inform on how agricultural practices and land use have changed over time.

Softwares  Collect Earth and Google Earth were employed to manipulate the historical series of high-resolution satellite images and implement photointerpretation. The coverage of identitied land units  was then estimated to address the configuration of the target landscape.

Landscape evolution over time was achieved by overlaying the 1954 and 2022 land use polygons, resulting in a merging database, in which an evolutionary dynamic was associated with each land use change.

The approach generated in-depth insights on the significant elements of the CURES olive landscape and informed on the dynamics of the area in relation to the risk of their disappearance, making it possible to identify what are the "landscape emergencies," i.e., the land uses that have seen the most̀ reduction in their area.

The methodologies employed have proven reliability in improving the knowledge ng target landscapes.  It might be useful to promote  sustainable agricultural practices for better preservation and management of rural environments so that cultural traditions can be preserved as well, and the environmental balance of the agrarian land can be maintained.

How to cite: Cuccaro, V. C., Di Giovannantonio, C., Pica, G., Malatesta, L., and Attorre, F.: Employng satellite immagery interpretation tools to detect land-use land-change dynamics in Italian historical rural landscapes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11949, https://doi.org/10.5194/egusphere-egu24-11949, 2024.

Jeremie' Tranchant¹, Daniel Fenech¹, Christopher Gauci¹, Daniela Ghirxi¹, Ines Felix Martins¹, Emanuele Colica¹, George Buhagiar¹

¹  Research and Planning Unit, Ministry for Transport, Infrastructure and Public Works, Project House, Triq Francesco    Buonamici, Floriana, FRN1700, Malta

The assessment of coastal erosion through shoreline change analysis, is an exercise of national utility undertaken in many countries. The Maltese Islands are particularly vulnerable to coastal erosion given the economic value of coastal activities and their high ratio of coast-to-land surface. The integration of historical cartographic material is often used to hindcast shoreline change across long periods of time, as well as to model future erosion rates. The Public Works Department have produced detailed 1:2500 maps of Malta in collaboration with the British Ordnance Survey from the end of the 19th century to 1957, however these maps have never been scientifically assessed. The initial research carried out evaluated the usefulness of the two oldest 25-inches Maltese maps series (early 20th century and 1957) for shoreline change analysis.  The two series were digitised, georeferenced, and compared in a GIS environment to assess their differences. The inaccuracies of the original drawings, absent shoreline indicators, and the absence of a geographic coordinate system (datum and projection) were identified as limitations for their use in evaluating small gradual changes, but were ideal for the identification of stochastic, large-scale historic erosion events using difference maps. This assessment showed that the two series are highly congruous and any changes between the two series are largely attributed to changes in infrastructure. There were, however, minor exceptions and these need to be explored on a case-by-case basis. These methods and the insights garnered from their production will function as scientific steppingstones towards developing a holistic coastal erosion national monitoring program.  

How to cite: Fenech, D., Tranchant, J., Gauci, C., Ghirxi, D., Felix-Martins, I., Colica, E., and Buhagiar, G.: Evaluating Ordnance Survey sheets (1890s – 1957) for shoreline change analysis in the Maltese Islands , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17822, https://doi.org/10.5194/egusphere-egu24-17822, 2024.