Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Remote Sensing and Geovisualization of Rock Slopes and Landslides

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists

Application of mixed and virtual reality in geoscience and engineering geology

Visual learning and efficient communication in mining and geotechnical practices is crucial, yet often challenging. With the advancement of Virtual Reality (VR) and Mixed Reality (MR) a new era of geovisualization has emerged. This thesis demonstrates the capabilities of a virtual continuum approach using varying scales of geoscience applications. An application that aids analyses of small-scale geological investigation was constructed using a 3D holographic drill core model. A virtual core logger was also developed to assist logging in the field and subsequent communication by visualizing the core in a complementary holographic environment. Enriched logging practices enhance interpretation with potential economic and safety benefits to mining and geotechnical infrastructure projects. A mine-scale model of the LKAB mine in Sweden was developed to improve communication on mining induced subsidence between geologists, engineers and the public. GPS, InSAR and micro-seismicity data were hosted in a single database, which was geovisualized through Virtual and Mixed Reality. The wide array of applications presented in this thesis illustrate the potential of Mixed and Virtual Reality and improvements gained on current conventional geological and geotechnical data collection, interpretation and communication at all scales from the micro- (e.g. thin section) to the macro- scale (e.g. mine)

Vombat: An Open Source Tool for Creating Stratigraphic Logs from Virtual Outcrops

An open source tool, Vombat , is presented that is designed to operate on Virtual Outcrop Models of sedimentary rocks, with the specific aim of assisting the stratigraphic analysis and interpretation. Vombat makes it possible to estimate the average attitude of the bedding and to create one or more attitude-aligned stratigraphic reference frames. This allows Vombat to extract continuous stratigraphic logs of any property associated with the point clouds (e.g. the lidar intensity or RGB color). Stratigraphic logs produced by Vombat can be compared and correlated to typical outcrop logs and petrophysical logs obtained from boreholes (e.g. gamma ray logs) and can provide information about the lithological variations in a stratigraphic succession. Furthermore, Vombat stratigraphic reference frames can be used to associate a stratigraphic position (a depth in the stratigraphic column) to any observation made on the outcrop, allowing visualization in 3D (on the virtual outcrop model) and 1D (on a stratigraphic column) for any collected data. All the geological objects created in the virtual environment can then be saved. The tool has been developed to be user-friendly and is constituted by a dynamically loaded plugin for the open source software CloudCompare

Utilization of Lidar Intensity Data and Passive Visible Imagery for Geological Mapping of Planetary Surfaces

While lidar has been historically used for generating digital terrain maps and as a navigation tool, recent research demonstrates that lidar has many potential scientific applications, including high resolution analysis of geological outcrops. Case studies were completed at the Tunnunik impact structure, Victoria Island, Arctic Canada, and the Nickel Rim South mine, Sudbury, Canada, to assess the fidelity of characterizing and differentiating mineralogical and lithological units remotely by integrating passive visible imagery with lidar intensity data. Unsupervised classification via k-means clustering was performed on the fused datasets, with results indicating that lithologies can indeed be successfully differentiated with minor a priori knowledge of the setting. Semi-quantitative analysis through XRD of Tunnunik samples demonstrates that distance-corrected intensity is linked in a linear relationship with both dolomite and clay content. The simultaneous acquisition of both geospatial and scientific data greatly increases the applications and value of using lidar, especially for mining, geological mapping in remote environments, and for future planetary missions

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

Lidar And Hyperspectral Data Integration For Landslide Monitoring: The Test Case Of Valoria Landslide

In the framework of the WISELAND project, funded by MIUR, we tested the integration between Lidar and hyperspectral methodologies in the Valoria landslide (Modena province, Italy), a high risk area with vulnerable elements, subjected to periodic and abrupt reactivations. Multitemporal Lidar Digital Terrain Models (DTMs) allowed the calculation of a differential surface, highlighting absolute height variations, recognizing the main landslide components and identifying depletion and accumulation zones. Hyperspectral data helped in the landslide terrain roughness characterization, performing the Principal Component Analysis (PCA) and correlating the results with Flatness and Organization geomorphometric parameters derived from Lidar DTM