Interpretation of spatiotemporal gravity changes accompanying the earthquake of 21 August 2017 on Ischia (Italy)

We analyse spatiotemporal gravity changes observed on the Ischia island (Italy) accompanying the destructive earthquake of 21 August 2017. The 29 May 2016 to 22 September 2017 time-lapse gravity changes observed at 18 benchmarks of the Ischia gravimetric network are first corrected for the gravitational effect of the surface deformation using the deformation-induced topographic effect (DITE) correction. The co-seismic DITE is computed by Newtonian volumetric integration using the Toposk software, a high-resolution LiDAR DEM and the co-seismic vertical displacement field derived from Sentinel-1 InSAR data. We compare numerically the DITE field with its commonly used Bouguer approximation over the island of Ischia with the outcome that the Bouguer approximation of DITE is adequate and accurate in this case. The residual gravity changes are then computed at gravity benchmarks by correcting the observed gravity changes for the planar Bouguer effect of the elevation changes at benchmarks over the same period. The residual gravity changes are then inverted using an inversion approach based on model exploration and growing source bodies, making use of the Growth-dg inversion tool. The found inversion model, given as subsurface time-lapse density changes, is then interpreted as mainly due to a co-seismic or post-seismic disturbance of the hydrothermal system of the island. Pros and weak points of such interpretation are discussed.

High-Resolution LiDAR Digital Elevation Model Referenced Landslide Slide Observation with Differential Interferometric Radar, GNSS, and Underground Measurements

The area of Taiwan is 70% hillsides. In addition, the topography fluctuates wildly, and it is active in earthquakes and young orogenic movements. Landslides are a widespread disaster in Taiwan. However, landslides are not a disaster until someone enters the mountain area for development. Therefore, landslide displacement monitoring is the primary task of this study. Potential landslide areas with mostly slate geological conditions were selected as candidate sites in this study. The slate bedding in this area is approximately 30 to 75 degrees toward the southeast, which means that creep may occur due to gravity deformation caused by high-angle rock formation strikes. In addition, because the research site is located in a densely vegetated area, the data noise is very high, and it is not easy to obtain good results. This study chose ESA Sentinel-1 data for analysis and 1-m LiDAR DEM as reference elevation. The 1-m LiDAR DEM with high accuracy can help to detect more complex deformation from DInSAR. The Sentinel-1 series of satellites have a regular revisit period. In addition, the farm areas of roads, bridges, and buildings in the study area provided enough reflections to produce good coherence. Sentinel-1 images from March 2017 to June 2021 were analyzed, obtaining slope deformation and converting it to the vertical direction. Deformation derived from SAR is compared with other measurements, including GNSS and underground slope inclinometer. The SBAS solution process provides more DInSAR pairs to overcome the problem of tremendous noise and has increased accuracy. Moreover, the SBAS method’s parameter modification derives more candidate points in the vegetated area. The vertical deformation comparison between the GNSS installation location and the ascending SBAS solution’s vertical deformation is consistent. Moreover, the reliable facing of the slope toward the SAR satellite is discussed. Due to the limitations of the GNSS stations, this study proposes a method to convert the observed deformation from the slope inclinometer and convert it to vertical deformation. The displacement of the slope indicator is originally a horizontal displacement. It is assumed that it is fixed at the farthest underground, and the bottom-to-top movement is integrated with depth. The results show that the proposed equation to convert horizontal to vertical displacement fits well in this condition. The activity of landslides within the LiDAR digital elevation model identified as scars is also mapped.

Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams

Defining stream channels in a watershed is important for assessing freshwater habitat availability, complexity, and quality. However, mapping channels of small tributary streams becomes challenging due to frequent channel change and dense vegetation coverage. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to obtain a 3D Digital Surface Model (DSM) to estimate the total in-stream channel and channel width within grazed riparian pastures. We used two methods to predict the stream channel boundary: the Slope Gradient (SG) and Vertical Slope Position (VSP). As a comparison, the same methods were also applied using low-resolution DEM, obtained with traditional photogrammetry (coarse resolution) and two more LiDAR-derived DEMs with different resolution. When using the SG method, the higher-resolution, UAV-derived DEM provided the best agreement with the field-validated area followed by the high-resolution LiDAR DEM, with Mean Squared Errors (MSE) of 1.81 m and 1.91 m, respectively. The LiDAR DEM collected at low resolution was able to predict the stream channel with a MSE of 3.33 m. Finally, the coarse DEM did not perform accurately and the MSE obtained was 26.76 m. On the other hand, when the VSP method was used we found that low-resolution LiDAR DEM performed the best followed by high-resolution LiDAR, with MSE values of 9.70 and 11.45 m, respectively. The MSE for the UAV-derived DEM was 15.12 m and for the coarse DEM was 20.78 m. We found that the UAV-derived DEM could be used to identify steep bank which could be used for mapping the hydrogeomorphology of lower order streams. Therefore, UAVs could be applied to efficiently map small stream channels in order to monitor the dynamic changes occurring in these ecosystems at a local scale. However, the VSP method should be used to map stream channels in small watersheds when high resolution DEM data is not available.

Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment

The hydrological information derived from a digital elevation model is very important in distributed hydrological modeling. As part of alpine hydrological research on stream network modeling using remote sensing data in the northeast of the Tibetan Plateau, three digital elevation model (DEM) datasets were obtained for the purpose of hydrological features, mainly including channel network, watershed extent and terrain character. The data sources include the airborne light detection and ranging (LiDAR) with point spacing of 1 m, the High Mountain Asia (HMA) DEM and the Shuttle Radar Topography Mission (SRTM) DEM. Mapping of the watershed and stream network was conducted using each of the three DEM datasets. The modeled stream networks using the different DEMs were verified against the actual network mapped in the field. The results show that the stream network derived from the LiDAR DEM was the most accurate representation of the network mapped in the field. The SRTM DEM overestimated the basin hypsometry relative to the LiDAR watershed at the lowest elevation, while the HMA DEM underestimated the basin hypsometry relative to the LiDAR watershed at the highest elevation. This may be because, compared with the SRTM DEM and the HMA DEM, the LiDAR DEM has higher initial point density, accuracy and resolution. It can be seen that the LiDAR data have great potential for the application in hydrologic modeling and water resource management in small alpine catchments.

Application of Geological Mapping Using Airborne-Based LiDAR DEM to Tunnel Engineering: Example of Dongao Tunnel in Northeastern Taiwan

The use of digital elevation models (DEMs) that use airborne-based light detection and the ranging technique (airborne-based LiDAR) to understand large-scale geological structures has become important in geological surveying and mapping. Taking the Dongao Tunnel area in northeastern Taiwan as the study area, this study used the airborne-based LiDAR DEM and related value-added maps to interpret the topographic and geomorphic features of the area and identify locations for geological investigation. The characteristics of the rock mass were observed on-site and revealed by excavation of the highway tunnel in the study area; they were compared with the interpreted topographic and geomorphic features to determine the potential of using 1 m-resolution LiDAR DEM in geological surveys and in the evaluation of engineering characteristics of underground rock masses. The results of this study demonstrated that the DEM accurately captured geomorphic features: the strata composed of slate and schist had distinct appearances in both the clinometric map and the hillshade map; the locations of faults, lineaments, and drainage were consistent with those observed on-site, and the positions of these features were captured more accurately than those on conventional maps. Evident microrelief features, including the distribution of scarps, erosion gullies, and mini-drainage systems provide an effective basis for interpreting a deep-seated gravitational deformation slope and for an on-site inspection for validation. The use of high-resolution LiDAR DEM to interpret geomorphic features along with geological surveys provides a more comprehensive understanding of the survey area, supporting surveys and geological mapping, revealing the locations of potential slope failures, and enabling the assessment of tunnel engineering risks.

Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality and methodology, ARA studies to date have not achieved effective computer-based ARA-component delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To achieve framework refinement and determine the optimal input elevation data for future ARA studies, this study tested a novel Digital Elevation Model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-meter spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions.

Little Ice Age advance of Kvískerjajöklar, Öræfajökull, Iceland. A contribution to the assessment of glacier variations in Iceland since the late 18th century

We describe the changes of the Kvískerjajöklar outlet glaciers in SE Iceland (presently ranging 600–1600 m a.s.l.), from their Little Ice Age maximum (LIAmax) to the present. We assume that glacier extent of the late 19th century approximately describes LIAmax although the glaciers already reached their peak extent in the 18th century. The former glacier margins were delineated from moraines, historical descriptions, topographical maps, aerial and oblique photographs, Landsat images and a lidar DEM. Along the previous glacier margins, elevation differences with respect to the lidar DEM of 2011 were estimated and contour maps of the glacier drawn at selected dates, maintaining the shape of the glacier surface as available maps. During the period 1890 to 2011, the outlets lost -0.4 m a-1 water equivalent evenly distributed over their surface and their area was reduced by 37% (from 10 km2 to 6.4 km2, 0.03 km2 a-1, 0.43 km3 water equivalent in total, i.e. 0.003 km3 w.e. a-1).

Dynamics and physics-based rainfall thresholds for a deep-seated landslide

<p>The Hooskanaden landslide is a large (~600 m wide × 1,300 m long), deep (~30 – 45 m) slide located in southwestern Oregon, United States. Since 1958, it has had five moderate/major movements that catastrophically damaged the intersecting U.S. Highway 101, along with persistent slow wet‐season movements and a long‐term accelerating trend due to coastal erosion. Multiple remote sensing approaches, borehole measurements, and hydrological observations were integrated to interpret the motion behaviors of the slide. Pixel offset tracking of both Sentinel‐1 and Sentinel‐2 images was carried out to reconstruct the 3D displacement field of the 2019 major event, and the results agree well with field measurements. A 12‐year displacement history of the landslide from 2007 to 2019 was retrieved by incorporating offsets from LiDAR DEM gradients and InSAR (Interferometric Synthetic Aperture Radar) processing of ALOS and Sentinel‐1 images. Comparisons with daily/hourly ground precipitation reveal that the motion dynamics are predominantly controlled by intensity and temporal pattern of rainfall. A new empirical threefold rainfall threshold was therefore proposed to forecast the dates for the moderate/major movements. This threshold relies upon antecedent water‐year and previous 3‐day and daily precipitation, and was able to represent observed movement periods well. Adaptation of our threshold methodology could prove useful for other large, deep landslides for which temporal forecasting has long been generally intractable. The averaged characteristic hydraulic conductivity and diffusivity were estimated as 6.6 × 10<sup>−6</sup> m/s and 6.6 × 10<sup>−4</sup> m<sup>2</sup>/s, respectively, based on the time lags between rainfall pulses and slide accelerations. Hydrologic modeling using these parameters helps to explain the ability of the new rainfall threshold.</p>