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This study investigates rock glacier destabilization based on the results of a unique in situ and remote-sensing-based monitoring network focused on the kinematics of the rock glacier in Äußeres Hochebenkar (Austrian Alps). We consolidate, homogenize, and extend existing time series to generate a comprehensive dataset consisting of 14 digital surface models covering a 68-year time period, as well as in situ measurements of block displacement since the early 1950s. The digital surface models are derived from historical aerial imagery and, more recently, airborne and uncrewed-aerial-vehicle-based laser scanning (ALS and ULS, respectively). High-resolution 3D ALS and ULS point clouds are available at annual temporal resolution from 2017 to 2021. Additional terrestrial laser scanning data collected in bi-weekly intervals during the summer of 2019 are available from the rock glacier front. Using image correlation techniques, we derive velocity vectors from the digital surface models, thereby adding rock-glacier-wide spatial context to the point-scale block displacement measurements. Based on velocities, surface elevation changes, analyses of morphological features, and computations of the bulk creep factor and strain rates, we assess the combined datasets in terms of rock glacier destabilization. To additionally investigate potential rotational components of the movement of the destabilized section of the rock glacier, we integrate in situ data of block displacement with ULS point clouds and compute changes in the rotation angles of single blocks during recent years. The time series shows two cycles of destabilization in the lower section of the rock glacier. The first lasted from the early 1950s until the mid-1970s. The second began around 2017 after approximately 2 decades of more gradual acceleration and is currently ongoing. Both destabilization periods are characterized by high velocities and the development of morphological destabilization features on the rock glacier surface. Acceleration in the most recent years has been very pronounced, with velocities reaching 20–30 m a−1 in 2020–2021. These values are unprecedented in the time series and suggest highly destabilized conditions in the lower section of the rock glacier, which shows signs of translational and rotational landslide-like movement. Due to the length and granularity of the time series, the cyclic destabilization process at the Äußeres Hochebenkar rock glacier is well resolved in the dataset. Our study highlights the importance of interdisciplinary, long-term, and continuous high-resolution 3D monitoring to improve process understanding and model development related to rock glacier rheology and destabilization.

Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive, this direction of comparison is nevertheless user-defined and becomes less relevant where the movement direction deviates from the surface normal. Measured change therefore also becomes less meaningful, as it is a projection onto this direction. Sliding of a failing slope, for example, is predominantly surface parallel rather than along the surface normal. We present an approach that derives a dominant movement direction (DMD) at each point based on multi-scale, multi-directional change quantifications. The DMDs differ from the surface normals in three LiDAR-derived test cases; a rockfall, an avalanche, and rock glacier movement, providing more accurate measures of rockfall depth and boulder movement across the rock glacier. When the direction of change detection is orthogonal to local relief (i.e. across the surface), a variable length search cylinder that intersects only a single (corresponding) surface is necessary during change detection. Where movement results in new regions of occlusion in the second point cloud, we show that the proportion of points for which no valid change could be recorded decreases by up to 15% using the DMD rather than the surface normal. We emphasise the importance of examining the direction over which change is measured, and highlight that a comparison direction that adapts to movement rather than to the local surface can provide more relevant and accurate measures of change where the movement is not orthogonal to the surface. Our approach represents a supplementary tool for cloud-to-cloud comparison, where a choice of tool should be made based on the expected DMD deviation from the surface normal.

Virtual Laser Scanning (VLS) provides a remote sensing method to generate 3D point clouds, which can, in certain cases, replace real data acquisition. A prerequisite is a suitable substitute of reality for modelling the 3D scene, the scanning system, the platform, the laser beam transmission, the beam-scene interaction, and the echo detection. The suitability of simulated laser scanning data largely depends on the application, and simulations that are more realistic come with stricter requirements on input data quality and higher computational costs. It is therefore important to have a good capability for corresponding trade-offs in the simulation software.With the scientific software HELIOS++ [1], we provide an open source solution to acquire VLS data, where this trade-off can be tuned easily. HELIOS++ is implemented in C++ for optimized runtimes, and provides bindings in Python to allow integration into scripting environments (e.g. GIS plugins, Jupyter Notebooks).The HELIOS++ VLS concept is based on a modular design. This allows the user to quickly exchange single simulation components, such as the scanner or the 3D scene. The simulation of diverging laser beams and the recording of full waveforms is supported via a subray tracing approach: depending on the desired physical realism and accuracy, a user-defined number of concentric circles approximate a single laser beam. On each circle, individual subrays are cast into the scene, which can then intersect with a single object and produce a hit. The returned waveforms are subsequently added together. This allows the simulator to detect multiple echoes for each pulse. The waveforms can be exported for further analysis.In this contribution, we present main applications of HELIOS++ as a general-purpose LiDAR simulator. The first application is forestry, where green vegetation can be represented by different 3D model types. As the simulation of individual leaves as 3D mesh models requires high computational power, voxel-based methods have recently been proposed. HELIOS++ also supports simulation of semitransparent voxels, where a subray has a certain probability of creating a return when traversing. This probability depends on the incidence angle and the leaf angle distribution (e.g., planophile, erectophile …), the traversal length through the voxel, and the leaf area density of the voxel, which can, e.g., be derived from a terrestrial laser scanning point cloud. Tuning of the subray-parameters allows recreating vertical point density profiles of real surveys.A second use case is the generation of training data for machine learning algorithms. Recently, several methods for Deep Learning on point clouds have been presented. However, such methods require immense amounts of training data to achieve acceptable performance. We present how VLS can be used to generate training data in machine learning classifiers, and how different sensor settings influence the classification results.This contribution provides an introduction to VLS, possible use cases, pitfalls and best practices for successful application of laser scanning simulation.[1] Winiwarter et al., 2021, DOI: 10.1016/j.rse.2021.112772

Recent advances in repeated data acquisition by UAV-borne photogrammetry and laser scanning for geoscientific monitoring extend the possibilities for analysing surface dynamics in 3D at high spatial (centimeter point spacing) and temporal (up to daily) resolution. These techniques overcome common challenges of ground-based sensing (occlusion, heterogeneous measurement distribution, limited spatial coverage) and provide a valuable additional data source for topographic change analysis between successive epochs.We investigate point clouds derived from UAV-borne photogrammetry and laser scanning as input for change analysis. We apply and compare two state-of-the-art methods for pairwise 3D topographic change quantification. Our study site is the active rock glacier Äußeres Hochebenkar in the Eastern Austrian Alps (46° 50’ N, 11° 01’ E). Whereas point clouds derived from terrestrial laser scanning (TLS) have become a common data source for this application, point clouds derived from UAV-borne sensing techniques have emerged only in recent years and their potential for methods of 3D and 4D (3D + time) change analysis is yet to be exploited.We perform change analysis using (1) the Multi Scale Model to Model Cloud Comparison (M3C2) algorithm [1] and (2) the correspondence-driven plane-based M3C2 [2]. Both methods have shown to provide valuable surface change information on rock glaciers when applied to successive terrestrial laser scanning point clouds of different time spans (ranging from 2 weeks to several years). The considerable value of both methods also lies in their ability to quantify the uncertainty additionally to the associated change. This allows to distinguish between significant change (quantified magnitude of change > uncertainty) and non-significant or no change (magnitude of change ≤ uncertainty) and hence enables confident analysis and geographic interpretation of change.We will extend the application of the two methods by using point clouds derived using (1) photogrammetric techniques on UAV-based images and (2) UAV-borne laser scanning. We investigate the influence of variations in measurement distribution and density, completeness of spatial coverage and ranging uncertainty by comparing UAV-based point clouds to TLS data of the same epoch. Using TLS-TLS-based change analysis as reference, we examine the performance of the two methods with respect to their capability of quantifying surface change based on point clouds originating from different sensing techniques.Results of this assessment can support the theoretical and practical design of future measurement set-ups. Comparing results of both methods further aids the selection of a suitable method (or combination) for change analysis in order to meet requirements e.g., regarding uncertainty of measured change or spatial coverage of the analysis. To ease usability of a broad suite of state-of-the-art methods of 3D/4D change analysis, we are implementing an open source Python library for geographic change analysis in 4D point cloud data (py4dgeo, www.uni-heidelberg.de/3dgeo-opensource). Finally, our presented study provides insights how methods for 3D and 4D change analysis should be adapted or developed in order to exploit the full potential of available close-range sensing techniques.[1] https://doi.org/ 10.1016/j.isprsjprs.2013.04.009[2] https://doi.org/10.1016/j.isprsjprs.2021.11.018

Timely and reliable information on earthquake-induced building damage plays a critical role for the effective planning of rescue and remediation actions. Automatic damage assessment based on the analysis of 3D point cloud (e.g. from photogrammetry or LiDAR) or georeferenced image data can provide fast and objective information on the damage situation within few hours. So far, studies are often limited to the distinction of only two damage classes (e.g. damaged or not damaged) and to information provided by 2D image data. Beyond-binary assessment of multiple grades of damage is challenging, e.g. due to the variety of damage characteristics and the limited transferability of trained algorithms to unseen data and other geographic regions. The detailed damage assessment based on full 3D information is, however, required to enable efficient use and distribution of resources and for evaluation of structural stability of buildings. Further, the identification of slightly damaged buildings is essential to estimate the vulnerability for severe damage in potential aftershock events. In our work, we propose an interdisciplinary approach for timely and reliable assessment of multiple building-specific damage grades (0-5) from post- (and pre-) event UAV point clouds and images with high resolution (centimeter point spacing or pixel size). We combine expert knowledge of earthquake engineers with fully automatic damage classification and human visual interpretation from web-based crowdsourcing. While automatic approaches enable an objective and fast analysis of large 3D data, the ability of humans to visually interpret details in the data can be used as (1) validation of the automatic classification and (2) alternative method where the automatic approach showed high levels of uncertainty. We develop a damage catalogue that categorizes typical geometric and radiometric damage patterns for each damage grade. Therein, we consider influences of building material and region-specific building design on damage characteristics. Moreover, damage patterns include observations of previous earthquakes to ensure practical applicability. The catalogue serves as decision basis for the automatic classification of building-specific damage using machine learning, on the one hand. On the other hand, the catalogue is used to design quick and easy single damage mapping tasks that can be solved by volunteers within seconds (Micro-Mapping, Herfort et al. 2018). A further novelty of our approach consists in the combination of strengths of machine learning approaches for point cloud-based damage classification and visual interpretation by human contributors through Micro-Mapping tasks. The optimal combination of operation and weighted fusion of both methods is thereby dependent on event-specific conditions (e.g. data availability and quality, temporal constraints, spatial scale, extent of damage). By considering observations from previous earthquakes and influences of building design and structure on potential damage characteristics, our approach shall be applicable to events in different geographic regions. By the combination of automated and crowdsourcing methods, reliable and detailed damage information at the scale of large cities shall be provided within a few days. References Herfort, B., Höfle, B. & Klonner, C. (2018): 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 137, pp. 73-83.

Change analysis of rock glaciers is crucial to analyzing the adaptation of surface and subsurface processes to changing environmental conditions at different timescales because rock glaciers are considered as potentially unstable slopes and solid water reservoirs. To quantify surface change in complex surface topographies with varying surface orientation and roughness, a full three‐dimensional (3D) change analysis is required. This study therefore proposes a novel approach for accurate 3D point cloud‐based quantification and analysis of geomorphological activity on rock glaciers. It is applied to the lower tongue area of the Auseres Hochebenkar rock glacier, Otztal Alps, Austria. Multi‐temporal and multi‐source topographic LiDAR data are used to quantify surface changes and to reveal their spatial and temporal characteristics at different timescales within the period 2006–2018. LiDAR‐based examinations are complemented with subsurface characteristics obtained from electrical resistivity tomography. This combined approach reveals active and variable spatial and temporal surface dynamics in the investigated area, with minimum detectable change between 0.09 and 0.65 m at 95% confidence. Given that this approach overcomes current uncertainties in established methods of differentiating complex rock glacier surfaces, we consider it a valuable addition that can be applied to objects of similar properties such as landslides or glaciers.

Earthquakes play a major role worldwide regarding economic and social consequences. In the event of an earthquake, many lives are at risk and the impact on the built and natural environment may be significant. Until now, estimations of damage and losses and the assessment of the stability of buildings are, however, only available several days to months after the event and are often based on the subjective assessment of experienced engineers.For the effective planning of rescue measures and the best possible use of available resources, a fast, (semi-)automatic and accurate detection of the situation and an objective assessment of damage to critical infrastructures is indispensable. This requires a combination of innovative methods and technologies (UAVs, Machine Learning and Crowdsourcing combined with earthquake engineering knowledge) covering a wide range of spatial and temporal scales.The interdisciplinary system LOKI (www.uni-heidelberg.de/loki) consists of the following procedure: After the occurrence of an earthquake, an initial damage forecast is made within a few minutes based on the Global Dynamic Exposure model and integrated vulnerability functions in combination with the ground-motion field to identify areas with potential high/low damage. Missing building footprints and required building information are recorded via a crowdsourcing approach to complete the OpenStreetMap building database, which serves as input to the exposure model. In parallel, mission plans for overview flights are created and transferred to fixed-wing UAVs, which record low to medium-resolution photos and 3D point clouds of the entire affected area. These data are used for damage detection, in which a binary distinction is made at building level between visible and non-visible damage using Machine Learning approaches. Thus, after a few hours, first orthophotos and the location of potentially damaged buildings can already be transmitted to emergency response teams. Thereafter, mission planning focuses on the capture of high-resolution 3D information of individual buildings. Fleets of multicopter drones provide highly detailed 3D imagery following mission plans that can be modified in real time by the emergency response teams. The mission planning algorithms support prioritization of specific areas or buildings for data acquisition, so that rescue measures can be optimally supported. The acquired high-resolution images and point clouds serve as input for damage classification, which is carried out per building using a combination of automatic procedures and Micro-Mapping. This offers the possibility to combine the advantages of fast automated procedures with the human ability to visually interpret details. Potential global and building material-related damage characteristics, which are based on observations of previous earthquakes, are included in a damage catalogue and allow building damage to be classified into five damage grades. In an iterative process, a timely and objective building-level classification of damage with an indication of the reliability of the specified degree of damage is achieved.The integration of various disciplines and the combination of different concepts and technologies allows supporting disaster relief in different temporal and spatial resolutions with timely and reliable information on earthquake-induced damage.