LiDAR-based Digital Elevation Model Research Articles

Uncertainty analysis of potential population exposure within the coastal lowlands of mainland China

With accelerating global sea level rise driven by climate change, accurate estimates of potential population exposure (PPE) within the low-elevation coastal zones (LECZ) are critical for coastal planning and assessing the benefits of climate mitigation. Multiple digital elevation models (DEM) and population grid datasets have been used for the PPE assessment of coastal lowlands. However, the uncertainty arising from differences in data sources and production methods results in poorly guided estimates. In this study, four global DEM and five population datasets were used to estimate the PPE in the LECZ of China and to assess the uncertainty of PPE estimation. Based on the DEM and population grid with the best accuracy, we found that more than 13.82% of China’s residents lived in the LECZ in 2010. Different DEM-population combinations yielded significantly different PPE estimates, ranging between 3.59–24.61 million and 31.56–112.24 million people in the LECZ below 1 m and 4 m elevation, respectively. The satellite Lidar-based DEM improves the estimates of the LECZ and obtains the PPE within LECZ below 4 m elevation that far exceeds those of other DEM datasets. The usage of WorldPop and LandScan population datasets leads to an underestimation of PPE within the LECZ of China. In contrast, integrating more geospatial big data helps generate better population grids, thus reducing the uncertainty of coastal PPE estimates. There is still a need to improve the availability and accuracy of coastal geospatial data and to deepen the understanding of coastal vulnerability.

Multi-annual and multi-decadal evolution of sediment accretion in a saltmarsh of the French Atlantic coast: Implications for carbon sequestration

Coastal marshes offer natural solutions for adapting to and mitigating the effects of climate change and sea level rise. However, the resilience of the marsh physical system and, with it, the ecosystem services that it provides, is largely site specific. This calls for the increase in the spatial cover of coastal marsh studies in order to assess the controlling factors of marsh evolution, and their long-term carbon storage capacities. Here, we study the spatio-temporal variations in sedimentation rates and organic carbon (OC) sequestration capacity of the macrotidal minerogenic saltmarshes in Aiguillon Bay, belonging to one of the largest French coastal marshes. Supported by aerial photographs and satellite image analysis, we first show that saltmarshes of the Aiguillon Bay have prograded at very high rates, up to 14 m yr−1 since 1950. Sediment accumulation rates (SAR) were estimated at both multi-annual to multi-decadal scales based on two approaches: (i) LiDAR-based digital elevation models from multiple acquisition dates (2010–2021); and (ii) depth profiles of 210Pb in excess and 137Cs in sediment cores collected along cross-shore transects in the saltmarshes. Long-term SAR range from 0.8 to 2.2 cm yr−1 and are among the highest reported worldwide for equivalent systems. The positive accretion balance (accretion rate minus local sea-level rise rate) provides important clues on marsh resilience suggesting that the Aiguillon Bay is currently able to adapt to rising sea level. Despite relatively low organic carbon content (1.3–6.0%), high SAR leads to high carbon sequestration rates (99–345 gC m−2 yr−1; or a mean value of 2.5 Mg C ha−1 yr−1). The isotopic signature of sediment OC reveals a significant and rapid decomposition of organic material in surface cores, while allochthonous sediment of marine origin dominates the signature of chemically-stable OC of marsh sediments. This implies that the carbon sequestration capacity of minerogenic saltmarshes, such as those of the Pertuis Charentais, also depends upon the wealth of adjacent coastal environments through high sediment supply and primary productivity.

Evaluating the Drainage Density Characteristics on Climate and Drainage Area Using LiDAR Data

The purpose of this study is to identify the relationship between drainage density and climate, as represented by the climate aridity index, and to understand the relationship between drainage density and drainage area. A total of 121 study sites with low human impact, and a wide range of climate aridity index 0.3 (humid)–10.4 (arid), were selected based on the availability of light detection and ranging (LiDAR) data, producing a digital elevation model (DEM) with a spatial resolution of 1 m. A curvature-based method, incorporating both positive and negative curvature information, was used to extract the valley (drainage) network from the LiDAR-based DEMs. Drainage density and climate aridity index exhibited a monotonically increasing trend, contrary to the previous results that have shown a U-shaped relationship. This discrepancy was caused by the selection of watersheds with extensive human activity in the previous study. One-meter resolution DEM produced greater drainage density than the previous studies with a coarse spatial resolution of 30 m as small valleys are not detectable in low-resolution topography datasets. The discrepancy between the previous study and the current study results encouraged further investigation of the impact of the drainage area (watershed size). A negative correlation between drainage density and drainage area was reconfirmed, while a stronger decreasing trend was observed in arid regions than in humid regions.

Radargrammetry DEM Generation Using High-Resolution SAR Imagery Over La Palma During the 2021 Cumbre Vieja Volcanic Eruption

This letter aims at investigating the potential of high-resolution (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7\times0.5\,\,\text{m}^{2}$ </tex-math></inline-formula> ) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

The Potential and Implications of Automated Pre-Processing of LiDAR-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis

Abstract LiDAR-derived digital elevation models (DEMs) have transformed the archaeological study of landscape features, broadened our technical capabilities, and enhanced the accuracy with which terrain relief is described. These models also place demands on how researchers and analysts interpret DEM content in the context of the modern landscape. LiDAR-based DEMs contain modern man-made structures that can significantly influence model properties. Although data are usually filtered and some of these artificial features are removed during bare-earth classification, many terrain interventions remain visible. This large-scale case study applies established methods to a freely available DEM of the Czech Republic in an attempt to evaluate differences between original and filtered DEMs. It applies a fully automated filtering procedure using vector topographic maps to avoid manual corrections that would make the procedure problematic when used on a macro scale. The results of our archaeological GIS analysis demonstrate that this procedure, despite its relative simplicity, can achieve a significantly better representation of a landscape compared to that offered by an unfiltered DEM. Finally, we propose a series of future steps with a view to developing a more comprehensive and accurate model and overcoming its limitations.

An alternative approach to estimate river cross-sections using LIDAR-based digital elevation model

ABSTRACT Topographic LIDAR can be used to estimate elevation values for dry areas down to the river water level during the extraction of river cross-sections (XS). However, LIDAR cannot accurately predict the submerged topography, which causes uncertainty in river XS area estimation. This uncertainty affects the channel water level and flood inundation depth estimation in in situ sparse data. Therefore, an alternative approach is presented to estimate unknown submerged topography (UST) using topographic LIDAR. The one dimension/two dimension Hydrologic Engineering Center River Analysis System (1D/2D HEC-RAS) model is used to simulate the estimated river XS with the help of in situ river water level and flow data which is later validated using in situ data. The results show that the proposed approach accurately estimates water level (error >0.5 m), channel flow areas, and floodplain water depths. Notably, the extent of the estimated floodplain overflow by UST models was in 94% agreement with the real XS.

Energy mass balance and flow modeling of early Holocene glaciers in the Queshque valley, Cordillera Blanca, Peru

Our limited knowledge of the timing and pattern of early Holocene climate variability in the tropical Andes hinders our ability to evaluate any potential linkages between low and high latitude oceanic-atmospheric dynamics. There is mounting evidence that glaciers in the Peruvian Andes stabilized at times during the early Holocene, suggesting there were periods of colder and/or wetter conditions that interrupted an overall pattern of a warmer and drier climate. Evaluating the global significance of these glacial fluctuations requires that the nature of these apparent cooling events and any shifts in the hydrologic cycle be better quantified. Here we apply a physically-based glacier model to reconstruct and interpret early Holocene-aged paleoglaciers in steady-state with a range of tropical climatic conditions in the Queshque valley of the Cordillera Blanca, Peru (9.8°S). This model uses a LiDAR-based digital elevation model (DEM) and hourly meteorological data as inputs to calculate the fully-distributed surface energy and mass balance (SEMB). This approach allows us to better capture the diurnal range of environmental variability at a spatial resolution that has not been achieved with previous paleo-glacier modeling efforts in this region. A 3-D rendition of paleoglaciers was then developed based on a flow model that responds to the SEMB input, accounting for the valley topography. The model was validated by simulating glaciers that match both field and satellite observations of modern glacier area limits, as well as ice thicknesses that are consistent with recently measured ground-penetrating radar (GPR) profiles of the main glacier tongue. After adjusting for changes in early Holocene global radiation values, and using regional paleoclimate records as constraints for the model, the maximum early Holocene ice limit in the valley dating to ∼10.8 (±0.1) ka was reconstructed using a 3.0 °C cooling and a 25% increase in precipitation relative to today. An up-valley ice extent dating to ∼9.4 (±0.3) ka was reconstructed using a 2.8 °C cooling and no change in precipitation amounts relative to today. These results suggest that conditions were cold and wet enough to maintain glaciers at times during the early Holocene, even though a shift to drier conditions combined with modestly warmer temperatures drove a phase of ice retreat from at least ∼10.8 to ∼9.4 ka. These results suggest that southern tropical temperatures and the hydrologic cycle rapidly reorganized during the early Holocene in conjunction with diminishing ice sheets and shifting environmental conditions in the high latitudes.

Can high-resolution topography and forest canopy structure substitute microclimate measurements? Bryophytes say no

Increasingly available high-resolution digital elevation models (DEMs) facilitate the use of fine-scale topographic variables as proxies for microclimatic effects not captured by the coarse-grained macroclimate datasets. Species distributions and community assembly rules are, however directly shaped by microclimate and not by topography. DEM-derived topography, sometimes combined with vegetation structure, is thus widely used as a proxy for microclimatic effects in ecological research and conservation applications. However, the suitability of such a strategy has not been evaluated against in situ measured microclimate and species composition. Because bryophytes are highly sensitive to microclimate, they are ideal model organisms for such evaluation.To provide this much needed evaluation, we simultaneously recorded bryophyte species composition, microclimate, and forest vegetation structure at 218 sampling sites distributed across topographically complex sandstone landscape. Using a LiDAR-based DEM with a 1 m resolution, we calculated eleven topographic variables serving as a topographic proxy for microclimate. To characterize vegetation structure, we used hemispherical photographs and LiDAR canopy height models. Finally, we calculated eleven microclimatic variables from a continuous two-year time- series of air and soil temperature and soil moisture. To evaluate topography and vegetation structure as substitutes for the ecological effect of measured microclimate, we partitioned the variation in bryophyte species composition and richness explained by microclimate, topography, and vegetation structure.In situ measured microclimate was clearly the most important driver of bryophyte assemblages in temperate coniferous forests. The most bryophyte-relevant variables were growing degree days, maximum air temperature, and mean soil moisture. Our results thus showed that topographic variables, even when derived from high-resolution LiDAR data and combined with in situ sampled vegetation structure, cannot fully substitute effects of in situ measured microclimate on forest bryophytes.

Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling

The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data. Unfortunately, cities often do not have or cannot release complete infrastructure data, and high-resolution terrain data products are not available everywhere. In this study, we quantify how the accuracy and precision of urban hydrologic-hydrodynamic models vary as a function of data completeness and model resolution. For this aim, we apply the one-dimensional (1D) and coupled one- and two-dimensional (1D-2D) versions of the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) in an urban catchment in the city of Phoenix, Arizona. Here, we have collected detailed infrastructure data, a high-resolution 0.3-m LiDAR-based digital elevation model, and catchment properties data. We tested several model configurations assuming different levels of (i) availability of stormwater infrastructure data (ranging from 5% to 75% of attribute-values missing) and (ii) terrain aggregation (i.e., 4.6 m and 9.7 m). These configurations were generated through random Monte Carlo sampling for SWMM 1D and selective sampling with four cases for SWMM 1D-2D. We ran simulations under the 50-year return period design storm and compared simulated flood metrics assuming the highest-resolution and complete data model configuration as a reference. The study found that the model may over or underestimate flood volume and duration with different levels of missing data depending on the parameters — roughness, diameter or depth, and that model performance is more sensitive to missing data that is downstream and closer to the outfall as opposed to missing data upstream. Errors in flood depth, area and volume estimation are functions of both the data completeness and model resolution. Missing feature data leads to overestimation of flood depth, while lower model resolution results in underestimating flood depth and overestimating flood extent and volume.

Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis

Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis

Evidence of Surface Rupture Associated With Historical Earthquakes in the Lower Tagus Valley, Portugal. Implications for Seismic Hazard in the Greater Lisbon Area

The Lower Tagus Valley Fault, Portugal, has long been associated with the damaging earthquakes that affected the Greater Lisbon Area in historical times. These include a poorly documented earthquake that occurred in 1344, the relatively well-documented 1531 earthquake, and the most recent M6.0 1909 earthquake. In this work, we use a 0.5 m resolution LiDAR-based digital elevation model and a 0.5 cm resolution digital surface model based on UAV photogrammetry to accurately locate the fault scarps in the northernmost portion of the western fault strand and to select sites to perform paleoseimolological investigations. The paleoseismological and geochronological analysis performed in the Alviela trench site document the fault activity in the last 3000 years, including two earthquakes during historical times. We performed ground motion scenarios for 20 km, 40 km, and 60 km ruptures including the trench site. The ground motion fields obtained for the 40 km and 60 km ruptures are in agreement with most macroseismic intensity data available for the 1531 earthquake, implying a magnitude in the range M6.8–7.4. However, the degree of deformation preserved in the trench suggests a value closer to the lower magnitude bound. The intensity level observed in Lisbon in 1531 (IX) is lower than the modeled intensities for all considered scenarios and could be related to a particularly high level of vulnerability of the building stock.

Comparison of LiDAR-based morphometric analysis of a drainage basin with results obtained from UAV, TOPO, ASTER and SRTM-based DEMs

Morphometric parameters are important elements in water and land resource management studies. In this study, 23 morphometric parameters of drainage networks were extracted using a Shuttle Radar Topography Mission (SRTM)–based digital elevation model (DEM), an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)–based DEM, a 1:25,000 scale topographical map (TOPO)–based DEM, an unmanned aerial vehicle (UAV) –based DEM and an airborne light detection and ranging (LiDAR)–based DEM. Generally, LiDAR sensors provide more accurate results than other DEM sources, and for that reason, we used LiDAR-based DEM data as a reference for comparing results of analyses made by the other DEM sources. According to the results, as resolution increases, many morphometric parameter values also tend to increase. In addition, for all of the morphometric parameter values (basic, derived and shape), the values obtained using the TOPO, UAV, ASTER and SRTM DEMs closely resembled the values obtained using LiDAR DEM-based parameters.

Morphometric indicators of insular and marginal morainic uplands (based on LiDAR data) of the Last and pre-Last Glaciations, case of Lithuania

The LiDAR-based digital elevation models of representative sample areas of morainic uplands of the Last (Weichselian) and pre-Last (Saalian) Glaciations in Lithuania were selected, and the terrain ruggedness index (TRI) and slope angles (SAs) were calculated. Former hypsometric studies of the topography in Lithuania were mainly dealing with the indication of maximum, minimum or average altitudes of relief derived from topographical maps. The SAs and TRI were calculated for the pattern areas (16 × 16 km) and their central smaller parts (5 × 5 km). In order to test how much morphometric parameters are dependent on the size of a sample area, smaller areas (located in the central parts of all five patterns) were analysed in a similar way, calculating SA and TRI values. The same order of the mean values of SAs was determined: the steepest slopes are characteristic of the Tauragnai, Plateliai and Vištytis patterns, and the gentlest slopes of the Varniai and Medininkai patterns. The steepest slopes and the highest TRI were determined for the marginal morainic uplands of the Last (Late Weichselian) Glaciation. The age of the insular Žemaičiai (Samogitian) Upland is under discussions so far. It was proposed by other researchers earlier that the core of the insular Žemaičiai Upland height formed during the Saalian Glaciation and this core is covered by a thin cover of Weichselian deposits. The morphometric parameters display that the highest maturity of the relief is characteristic of the southern slope of the insular Žemaičiai Upland and the Medininkai Upland of the Saalian age. This indicates a likely similar age of both uplands.

Identifying Hydro-Geomorphological Conditions for State Shifts from Bare Tidal Flats to Vegetated Tidal Marshes

High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km²). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes.

Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments.

Systematic sample subdividing strategy for training landslide susceptibility models

Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model’s hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs are useful in explaining the information content in the training subset and their differences from the original inventory datasets. The quantitative evaluation with KNN, SVM and DT shows that the proposed method outperforms the RS and SRS in all the models and the CV method in KNN and DT models. The proposed sampling strategy enables new applications in landslide modelling, such as measuring inventory data content and complexity and selecting effective training samples to improve the predictive capability of landslide susceptibility models.

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

DEMs (Digital Elevation Model) generated with different remote sensing techniques and technologies are used to determine the changes of vegetation in forests depending on topographical factors. The accuracy of DEMs has a major impact on the planning and management of forests.In this study, the accuracy of two different DEM data sources, which are frequently used in the modeling of topographic changes in large field studies in forestry, was compared with the LiDAR-based DEM dataset on a forest site. In this context, three different DEM source were used. One of them was the 10 m interval contour lines of 1:25,000 scale aerial photogrammetry based standard topographical maps which are produced by National General Directorate of Mapping. Topomap contour lines are transformed to grid based DEMs by using TIN and ANUDEM based approaches at 2.5, 5, 10 and 30 m resolutions. The other data was ASTER GDEM (1 arc-second ASTER GDEM Version 2, approximately 30 m resolution). The final and reference data is the LiDAR based, 0.25 m resolution DEM. In total, 33 DEM datasets are compared with the LiDAR-based DEM dataset. For these data sets, five difference metrics were calculated: pixel based difference models, the areal and volumetric difference of surface models, the areal difference of slope classes and the areal difference of aspect classes. According to the results of the analysis, the resolution, according to the topographic characteristics of the area and selected interpolation approaches has an effect on DEM modeling and DEM –derived metrics. In addition, the forest structure has a major impact on the accuracy of ASTER GDEM data.

Evidence of multiple slip events on postglacial faults in northern Fennoscandia

An analysis of high-resolution LiDAR-based digital elevation models (DEMs), trenching of Quaternary deposits, diamond drilling and a structural analysis of the bedrock were conducted in order to characterise the geological structure and to derive full slip profiles of the Venejärvi–Jauhojärvi postglacial fault complex located in northern Finland. The Venejärvi-Jauhojärvi fault complex consists of 17 surface ruptures striking in a SW–NE direction, cutting through Late Weichselian till and traceable for a total distance of 29.9 km. Based on stratigraphical and structural analysis we show that the Naamivitikko fault segment, which is part of the Venejärvi–Jauhojärvi complex, hosted up to three distinct slip events and it is therefore argued that the prevailing single-rupture hypothesis of postglacial faults should be treated with caution. Based on the measured surface rupture dimensions, it is conservatively estimated that the maximum moment magnitude of the paleoseismic event that the complex hosted, ranged between Mw ≈ 5.8–8.4. If uncertainties in the scaling laws equating surface rupture dimensions to moment magnitudes are accounted for, a more realistic maximum moment magnitude range between Mw ≈ 5.8–7.6 is given.

Integration of Corner Reflectors for the Monitoring of Mountain Glacier Areas with Sentinel-1 Time Series

Glacier flow and slope instabilities in Alpine mountain areas represent a hazard issue. Sentinel-1 satellites provide regular Synthetic Aperture Radar (SAR) acquisitions that are potentially useful to monitor these areas, but they can be affected by temporal decorrelation due to rapid changes in the surface. The application of interferometric synthetic aperture radar (InSAR) therefore seems difficult due to loss of coherence. On the other hand, Corner Reflectors (CR) can be used as coherent targets in SAR images for accurate displacement measurement thanks to their strong backscattering property and temporal stability. The use of CRs in multi-temporal InSAR analysis in Alpine mountain areas can thus be beneficial. In this study, we present a comparison between triangular and rectangular CRs, based on Radar Cross Section (RCS) measurements in an anechoic chamber and on long-term experiments over the Argentière glacier and the surrounding slopes and moraine. The visibility in both summer and winter of 10 CRs installed on the test site was investigated. As this area is exposed to heavy precipitation including snow falls, two perforated CRs were tested. The amplitude stability and the phase error of each CR were estimated. A precise tracking of two CRs installed at the glacier surface was also able to measure the displacement of the Argentière glacier, giving results close to previous GPS measurements. Furthermore, a Persistent Scatterer Interferometry (PSI) study was conducted, using the most stable CR as reference point to estimate slope instabilities, which led to the identification of an area corresponding to a tectonic fault called “Faille de l’angle”. The precise absolute locations of the CRs were successfully estimated and PS heights were compared with a LiDAR-based (Light Detection And Ranging) digital elevation model (DEM) and GPS measurements.