The analysis and interpretation of 3D topographic change requires methods that achieve low uncertainties in change quantification. Many recent geoscientific studies that perform point cloud-based topographic change analysis have used the Multiscale Model to Model Cloud Comparison (M3C2) algorithm to consider the associated uncertainty. Change measured with the M3C2 approach, however, is difficult to interpret where (1) change occurs in directions different to the direction of change computation or (2) the quantified magnitudes of change are exceeded by the associated uncertainty due to a rough surface morphology. We present a correspondence-driven plane-based M3C2 approach that is tailored to quantifying small-magnitude (< 0.1 m) 3D topographic change of rough surfaces by reducing the uncertainty of quantified change. The approach (1) extracts planar surfaces in point clouds of successive epochs, (2) identifies corresponding planar surfaces between two point clouds using a binary random forest classification, and (3) calculates M3C2 distances and the associated uncertainty between the corresponding planar surfaces. This correspondence-driven plane-based M3C2 does not require recognition or reconstruction of geometrically complex objects but instead quantifies change between less complex, homologous planar surfaces. The approach further allows to relate change directly to a moving object. We apply our approach to a bi-weekly time series of terrestrial laser scanning point clouds acquired at a rock glacier in the Austrian Alps. The approach enables a sevenfold reduction in the uncertainty associated with topographic change compared to standard M3C2. Significant change is therefore detected in 72.62% to 76.41% of the area of change analysis, whereas standard M3C2 detects significant change in only 16.21% (2-week timespan) to 59.96% (10-week timespan) of the same area. The correspondence-driven plane-based M3C2 complements 3D change analysis in applications that aim to quantify small-magnitude topographic change in photogrammetric or laser scanning point clouds with low uncertainties in natural scenes which are characterised by overall rough surface morphology and by individual rigid objects with planar surfaces (e.g., rock glaciers, landslides, debris covered glaciers).

Topographic change at a given location usually results from multiple processes operating over different timescales. However, interpretations of surface change are often based upon single values of movement, measured over a specified time period or in a single direction. This work presents a method to help separate surface change types that occur at different timescales related to the deformation of an active rock glacier, drawing on terrestrial lidar monitoring at sub-monthly intervals. To this end, we derive 3D topographic changes across the Äußeres Hochebenkar rock glacier in the Ötztal Alps. These changes are presented as the relative contribution of surface change during a 3-week period (2018) to the annual surface change (2017–2018). They are also separated according to the spatially variable direction perpendicular to the local rock glacier surface (using point cloud distance computation) and a single main direction of rock glacier flow, indicated by movement of individual boulders. In a 1500 m2 sample area in the lower tongue section of the rock glacier, the contribution of the 3-week period to the annual change perpendicular to the surface is 20 %, compared with 6 % in the direction of rock glacier flow. Viewing change in this way, our approach provides estimates of surface change in different directions that are dominant at different times of the year. Our results demonstrate the benefit of more frequent lidar monitoring and, critically, the requirement for novel approaches to quantifying and disaggregating surface change, as a step towards rock glacier observation networks focusing on the analysis of 3D surface change over time.

Coseismic landslides are a major hazard associated with large earthquakes in mountainous regions. Despite growing evidence for their widespread impacts and persistence, current understanding of the evolution of landsliding over time after large earthquakes, the hazard that these landslides pose, and their role in the mountain sediment cascade remains limited. To address this, we present the first systematic multi‐temporal landslide inventory to span the full rupture area of a large continental earthquake across the pre‐, co‐ and post‐seismic periods. We focus on the 3.5 years after the 2015 M w 7.8 Gorkha earthquake in Nepal and show that throughout this period both the number and area of mapped landslides have remained higher than on the day of the earthquake itself. We document systematic upslope and northward shifts in the density of landsliding through time. Areas where landslides have persisted tend to cluster in space, but those areas that have returned to pre‐earthquake conditions are more dispersed. While both pre‐ and coseismic landslide locations tend to persist within mapped post‐earthquake inventories, a wider population of newly activated but spatially dispersed landslides has developed after the earthquake. This is particularly important for post‐earthquake recovery plans that are typically based on hazard assessments conducted immediately after the earthquake and thus do not consider the evolving landslide hazard. We show that recovery back to pre‐earthquake landsliding rates is fundamentally dependent on how that recovery is defined and measured. Clarity around this definition is particularly important for informing a comprehensive approach to post‐earthquake landslide hazard and risk.

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.

Abstract. Topographic change at a given location usually results from multiple processes operating over different timescales. However, interpretations of surface change are often based upon single values of movement, measured over a specified time period and in a single direction. This work presents a method to help separate surface change mechanisms related to the deformation of an active rock glacier, drawing on terrestrial lidar monitoring at sub-monthly intervals. We derive 3D topographic changes across the Äußeres Hochebenkar rock glacier in the Ötztal Alps. These are presented as the relative contribution of surface change during a three-week period of snow-free conditions (2018) to the annual surface change (2017–2018). They are also separated according to the direction perpendicular to the local rock glacier surface (using point cloud distance computation) and the direction of rock glacier flow, indicated by movement of individual boulders. In a 1500 m2 sample area in the lower tongue section of the rock glacier, the contribution of the three-week period to the annual change perpendicular to the surface is 20 %, as compared to 6 % in the direction of rock glacier flow. This shows that different directions of surface change are dominant at different times of the year. Our results demonstrate the benefit of more frequent lidar monitoring and, critically, the requirement of novel approaches to detecting change, as a step towards interpreting the mechanisms that underlie the surface change of rock glaciers.

Landslide area density grid used in the analysis of the spatial evolution of landsliding following the 2015 Mw 7.8 Gorkha earthquake in Nepal. The data include 11 epochs of landslide area density, including bi-annual assessments between 2014 and 2018 (pre- and post-monsoon), and an additional coseismic assessment from 2015. This research has been supported by the UKRI-DFID SHEAR program (201844-112).

Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a pool of unlabeled data, and gully objects are detected where high densities of gully pixels are enclosed by an alpha shape. Gully objects are used in subsequent iterations following a mechanism where the algorithm uses the most reliable pixels as gully training samples. The gully class continuously grows until an optimal scenario in terms of accuracy is achieved. Results are benchmarked with manually tagged gullies (initial gully labeled area <0.3% of the total study area) in two different watersheds (408 and 302 km2, respectively) yielding total accuracies of >98%, with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation Coefficient >0.5, and receiver operating characteristic Area Under the Curve >0.89. Hence, our method outlines gullies keeping low false-positive rates while the classification quality has a good balance for the two classes (gully/no gully). Results show the most significant gully descriptors as the high temporal radar signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (21.8%). This research builds on previous studies to face the challenge of identifying and outlining gully-affected areas with a shortage of training data using global datasets, which are then transferable to other large (semi-) arid regions.

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 Äußeres Hochebenkar rock glacier, Ötztal 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.

Rockfalls commonly exhibit power law volume-frequency distributions, where fewer large events are observed relative to more numerous small events. Within most inventories, the smallest rockfalls are the most difficult to detect and so may not be adequately represented. A primary challenge occurs when neighboring events within a single monitoring interval are recorded as one, producing ambiguity in event location, timing, volume, and frequency. Identifying measurement intervals that minimize these uncertainties is therefore essential. To address this, we use an hourly data set comprising 8,987 3-D point clouds of a cliff that experiences frequent rockfalls. Multiple rockfall inventories are derived from this data set using change detections for the same 10-month period, but over different monitoring intervals. The power law describing the probability distribution of rockfall volumes is highly sensitive to monitoring interval. The exponent, beta, is stable for intervals >12 hr but increases nonlinearly over progressively short timescales. This change is manifested as an increase in observed rockfall numbers, from 1.4 x 10(3) (30 day intervals) to 1.4 x 10(4) (1 hr intervals), and a threefold reduction in mean rockfall volume. When the monitoring interval exceeds 4 hr, the geometry of detected rockfalls becomes increasingly similar to that of blocks defined by rock mass structure. This behavior change reveals a time-dependent component to rockfall occurrence, where smaller rockfalls (identifiable from more frequent monitoring) are more sensitive to progressive deformation of the rock mass. Acquiring complete inventories and attributing discrete controls over rockfall occurrence may therefore only be achievable with high-frequency monitoring, dependent upon local lithology.

Time series of topographic data are becoming increasingly widespread for monitoring geomorphic activity. Dense 3D time series are now obtained by near-continuous terrestrial laser scanning (TLS) installations, which acquire data at high frequency (e.g. hourly) and over long periods. Such datasets contain valuable information on topographic evolution over varying spatial and temporal scales. Current analyses however are mostly conducted based on pairwise surface or object-based change, which typically require the selection of thresholds and intervals to identify the processes involved and fail to account for the full history of change. Detected change may therefore be difficult to attribute to one or more underlying geomorphic processes causing the surface alteration. We present an automatic method for 4D change analysis that includes the temporal domain by using the history of surface change to extract the period and spatial extent of changes. A 3D space-time array of surface change values is derived from an hourly TLS time series acquired at a sandy beach over five months (2967 point clouds). Change point detection is performed in the time series at individual locations and used to identify change processes, such as the appearance and disappearance of an accumulation form. These provide the seed to spatially segment ‘4D objects-by-change’ using a metric of time series similarity in a region growing approach. Results are compared to pairwise surface change for three selected cases of anthropogenic and natural processes on the beach. The obtained information reflects the evolution of a change process and its spatial extent over the change period, thereby improving upon the results of pairwise analysis. The method allows the detection and spatiotemporal delineation of even subtle changes induced by sand transport on the surface. 4D objects-by-change can therefore provide new insights on spatiotemporal characteristics of geomorphic activity, particularly in settings of continuous surfaces with dynamic morphologies.

Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3-D data. Our aim is to fully characterise geomorphological change typified by an event magnitude–frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than 7 orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval.The data reported have been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3-D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3-D change to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ∼ 9 × 103 surveys acquired at ∼ 1 h intervals over 10 months. The magnitude–frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m3) rockfalls increases from 67 to 98 % of the total, and the number of individual rockfalls observed increases by over 3 orders of magnitude. High-frequency monitoring therefore holds considerable implications for magnitude–frequency derivatives, such as hazard return intervals and erosion rates. As such, while high-frequency monitoring has potential to describe short-term controls on geomorphological change and more realistic magnitude–frequency relationships, the assessment of longer-term erosion rates may be more suited to less-frequent data collection with lower accumulative errors.

Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment, and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved, and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end-users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.

Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment, and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 15 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved, and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, 20 timescales, and end-users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response. 25

Abstract. We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3D data. Our aim is to fully characterise geomorphological change typified by an event magnitude frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than seven orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval. The data reported has been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3D change, to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ~ 9 × 103 surveys acquired at ~ 1 hour intervals over 10 months. The magnitude-frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (

Terrestrial laser scanning (TLS) has provided new capabilities for real-time slope failure monitoring.For the mining industry, the advantages of TLS are not limited to deformation monitoring but also include a wide range of geotechnical and surveying applications such as characterising rockfall, 3D rock mass structure, and delineating features.Precision, repeatability and accuracy are crucial for deformation measurement.However, TLS data is affected by local climatic variations, such as fluctuations in temperature, rainfall, humidity, irradiance, and ambient light conditions.This is widely observed in open pit mines where high ranges (>2 km), often high temperature gradients and extremes, and steep topography result in highly variable measurement quality.As such, diurnal variations in ranging precision from TLS systems in such locations can reach up to 30-40 millimetres in some cases.A direct-Fourier transformation method applied to sequentially captured time-series of TLS data is proposed to overcome such variability.By modelling diurnal fluctuations in range from independently monitored environmental variables, it is possible to estimate the frequency and phase characteristics of these effects.Discrimination between deformation and environmentally modulated cyclicity is achieved by modelling out trends in the TLS data.Results contribute to improving the precision of slope failure warning systems as a tool to enhance safety procedures for sustainable mining activities.

Each year in Australia, hundreds of referrals and environmental plans (EPs) are submitted to the regulatory agencies by oil and gas explorers. The plans assess multiple spill response options. One assessment tool is NEBA. This extended abstract presents a new approach for NEBAs and an automated NEBA process. This NEBA method has two scenarios: pre-spill and spill response. The pre-spill NEBA uses EP-defined sensitive receptors as appropriate examples in the region of interest, uses the modelling outputs to identify potential exposure zones, defines critical inputs (season, spill size), defines and ranks priority response according to the modelling output, and assesses spill response strategies. The output of the pre-spill NEBA is a table of response tactics to be assessed with respect to reducing risk to as low as reasonably practicable (ALARP). The spill response NEBA is similar to the pre-spill, but it uses real-time spill modelling data as an additional input to strengthen the data evaluation. Traditionally, both pre- and spill response NEBAs require considerable data input, extensive mathematical modelling, and human interpretation. Automating the NEBA process saves time, reduces errors, and minimises human biases in interpretation. ConocoPhillips has recently developed a tool to quickly conduct NEBAs. The NEBA tool also provides a means to document the progress of operational and scientific monitoring programs to measure the performance of the spill response. The automated process output can be time/date stamped to ensure critical steps are fully documented and auditable.