Derived Digital Elevation Model Research Articles

Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA

Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.

EVALUATING THE INFLUENCE OF SPATIAL RESOLUTION ON LANDSLIDE DETECTION: A CASE STUDY IN THE CARLYON BEACH PENINSULA, WASHINGTON

Abstract. Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification.

LiDAR-change-based mapping of sediment movement from an extreme rainfall event

ABSTRACT Mapping landscape change at fine scales (e.g. <1.0 m resolution) using airborne LiDAR data from manned aircraft is a significant challenge. This challenge is magnified in disaster response contexts. A combination of collection and processing factors contributes to horizontal and vertical errors (and resulting uncertainty) in each pre- and post-LiDAR derived digital elevation model (DEM). Subsequently, the errors in the change surface from the two (or more) DEMs are an accumulation of the errors in the individual DEMs. Thus, reliable mapping erosion/deposition changes at sub-meter precision in change detection studies using LiDAR data is largely the domain of terrestrial LiDAR or sUAS with LiDAR scanners rather than manned aircraft. Unfortunately, terrestrial and sUAS LiDAR scanners are not well suited for mapping large areas and sUAS collections are subject to additional airspace constraints compared to manned aircraft. In this study, we probed one of the significant issues in airborne LiDAR change projects – vertical height errors from sequential flight lines. A simplified solution for determining flight line vertical biases in areas of low topographic relief with natural cover types was developed and tested for normalizing point clouds. The approach was tested in a fine-scale erosion/deposition study from an extreme rainfall event that eroded and deposited sand at depths of about 1.0 m. Airborne LiDAR had been collected prior to the rainfall event, and another airborne LiDAR collection was made 1 month after the event. Eleven field campaigns to collect reference data and visit anomalies in the change surface were conducted in a 15-month period after the event, beginning 25 February 2016 and ending 8 May 2017. The validation results indicate accuracies for the pre-event and post-event LiDAR derived DEMs were 7.8 cm and 13.0 cm RMSE, respectively. After modeling vertical errors and corrections applied to the post-event point clouds, the RMSE for the post-event DEM was 8.3 cm. In the depositional use case, 27 locations were sampled with auger boreholes/sand pits and compared with LiDAR-based change. The LiDAR-based change detection analysis resulted in predicted sand depth accuracies of 94% with a mean error of 4.7 cm.

LIDAR DERIVED SALT MARSH TOPOGRAPHY AND BIOMASS: DEFINING ACCURACY AND SPATIAL PATTERNS OF UNCERTAINTY

Abstract. As valuable and vulnerable blue carbon ecosystems, salt marshes require adaptable and robust monitoring methods that span a range of spatiotemporal scales. The application of unmanned aerial vehicle (UAV) based remote sensing is a key tool in achieving this goal. Due to the particular characteristics of tidal wetlands, however, there are challenges in obtaining research and management relevant data with the requisite level of accuracy. In this study, the spatial patterns in uncertainty stemming from scan angle, binning method, vegetation structure and platform surface morphology are examined in the context of UAV light detection and ranging (LiDAR) derived digital elevation models (DEM). The results demonstrate that overlapping the UAV flight paths sufficiently to avoid sole reliance on LIDAR data with scan angles exceeding 15 degrees is advisable. Furthermore, the spatial arrangement of halophyte species and marsh morphology has a clear influence on DEM accuracy. The largest errors were associated with sudden structural transitions at the marsh channel boundaries. The DEMmean was found to be the most accurate for bare ground, while the DEMmin was the most accurate for channels and the middle to high marsh vegetation (MAEs = −0.01m). For the low to middle vegetation, all the trialled DEMs returned a similar magnitude of mean error (MAE = ± 0.03m). The accuracy difference between the two vegetation associations examined appears to be connected to variations in coverage, height and biomass. Overall, these findings reinforce the link between salt marsh biogeomorphic complexity and the spatial distribution and magnitude of LiDAR DEM error.

Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.

Observing and Predicting Coastal Erosion at the Langue de Barbarie Sand Spit around Saint Louis (Senegal, West Africa) through Satellite-Derived Digital Elevation Model and Shoreline

Coastal erosion at Saint Louis in Senegal is affecting the local population that consists of primarily fishermen communities in their housing and their access to the sea. This paper aims at quantifying urban beach erosion at Saint Louis, Senegal, West Africa which is located on the northern end of the 13 km long Langue de Barbarie sand spit. The coastal evolution is examined quantitatively over a yearly period using Pleiades sub-metric satellite imagery that allows for stereogrammetry to derive Digital Elevation Models (DEMs). The comparison with ground truth data shows sub-metric differences to the satellite DEMs. Despite its interest in remote areas and developing countries that cannot count on regular surveys, the accuracy of the satellite-derived topography is in the same order as the coastal change itself, which emphasizes its current limitations. These 3D data are combined with decades-long regular Landsat and Sentinel-2 imagery derived shorelines. These observations reveal that the sand spit is stretching, narrowing at its Northern part while it is lengthening downdrift Southward, independently from climatological changes in the wave regime. A parametric model based on a stochastic cyclic sand spit behaviour allows for predicting the next northern opening of a breach and the urban erosion at Saint Louis.

Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review

Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms.

Using 2D HEC-RAS Modeling and Embankment Dam Break Scenario for Assessing the Flood Control Capacity of a Multi-Reservoir System (NE Romania)

Using hydraulic modeling techniques (e.g., one-dimensional/two-dimensional (1D/2D) hydraulic modeling, dam break scenarios) for extracting the flood settings is an important aspect of any action plan for dam failure (APDF) and flood mitigation strategy. For example, the flood hydraulic models and dam break scenario generated based on light detection and ranging (LiDAR)-derived digital elevation models (DEMs) and processed in the dedicated geographic information systems (GIS) and hydraulic modeling software (e.g., HEC-RAS—Hydrologic Engineering Center River Analysis System, developed by USACE HEC, Davis, CA, USA) can improve the flood hazard maps in case of potentially embankment dam failure. In this study, we develop a small-scale conceptual approach using 2D HEC-RAS software according to the three embankment dam break scenarios, LiDAR data (0.5 m spatial resolution), and 2D hydraulic modeling for the Başeu multi-reservoir system which belongs to the Başeu River (NE Romania) including R1—Cal Alb reservoir, R2—Movileni reservoirs, R3—Tătărăşeni reservoirs, R4—Negreni reservoirs, and R5—Hăneşti reservoirs. In order to test the flood control capacity of the Bașeu multi-reservoir system, the Cal Alb (R1) dam break scenario (piping failure) was taken into account. Three 2D stream flow modeling configurations based on R1 inflow rate with a 1% (100 year), 0.5% (500 year), and 0.1% (1000 year) recurrence interval and the water volume which can be accumulated with that specific inflow rate (1% = 10.19 × 106 m3; 0.5% = 12.39 × 106 m3; 0.1% = 17.35 × 106 m3) were computed. The potential flood wave impact was achieved on the basis of different flood severity maps (e.g., flood extent, flood depth, flood velocity, flood hazard) generated for each recurrence interval scenario and highlighted within the built-up area of 27 settlements (S1–S27) located downstream of R1. The results showed that the multi-reservoir system of Bașeu River has an important role in flood mitigation and contributes to the APDF in the context of climate change and the intensification of hydrological hazard manifestation in northeastern Romania.

Drainage Ditch Berm Delineation Using Lidar Data: A Case Study of Waseca County, Minnesota

Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the boundary of drainage ditches. In this landscape planning study, a geospatial modeling framework was established to identify these drainage system landforms and the boundary that separates these landforms from their surrounding areas across Waseca County in south-central Minnesota. By employing almost 2000 GPS spot elevation measurements from five ditch systems and one-meter Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) data, the drainage ditch berm polygons were delineated. Eight low light angle hillshade rasters at 45-degree azimuth intervals were used to construct the model. These hillshade rasters were combined to form a composite raster so that the effect of multiple azimuths can be captured during ditch berm delineation. The GPS points identified as the top of the berm were used to extract cell values from the combined hillshade. These cell values were modeled further using statistical distribution graphs. The statistical model derived +0.5 and +1 standard deviation values (cell values 812 and 827, respectively) of the combined hillshade raster were utilized to obtain complete berm polygons. In this semi-automated method, between 67.30% to 79.80% of ditch berm lengths were mapped with an average error that is less than the resolution of the DEM. Demarcation of these boundaries are important for local governments in Minnesota and throughout the world, as it could help guide land–water management and aid sustainable agriculture.

Using ArcGIS hydrologic modeling and LiDAR digital elevation data to evaluate surface runoff interception performance of riparian vegetative filter strip buffers in central Iowa

The Midwest is well known for agriculture, and Iowa is a leader in corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production. Fertilizers and chemical pesticides used to increase crop production can adversely affect the soil and water health. Midwest farmers also produce livestock and graze cattle on pastureland that can lead to excessive surface runoff and soil erosion. Establishing vegetative filter strips (VFSs) along the edge of farmland is one of the best management practices (BMPs) to reduce nutrient and sediment loss. However, studies have revealed that the classic VFS design along the length of an agricultural field does not adequately address nonuniform flow through the buffer. New designs are being researched to increase the efficiency of the VFS. In order to accurately implement new design strategies, the runoff flowpaths into the VFS need to be accurately modeled. This research assesses the performance of existing established VFS buffers of selected sites by modeling and analyzing the flow accumulation from the field into the VFS using geographic information system (GIS) and light detection and ranging (LiDAR) derived digital elevation model (DEM) 5 × 5 m data. This study also employed the new coefficient of flow interception (CFI) approach that improves the process of identifying areas where flow is concentrated and designing more efficient filter strips to account for concentrated runoff. In this study, the performance of VFS in three sites was evaluated by developing and using the CFI. Among the three sites, site 1 had very poor efficiency and no flow passes through the VFS, site 2 had low efficiency, and site 3 had excellent efficiency.

Terrestrial structure-from-motion: Spatial error analysis of roughness and morphology

Structure-from-Motion (SfM) photogrammetry is rapidly becoming a key tool for morphological characterisation and change detection of the earth surface. This paper demonstrates the use of Terrestrial Structure-from-Motion (TSfM) photogrammetry to acquire morphology and roughness data at the reach-scale in an upland gravel-bed river. We quantify 1) spatially-distributed error in TSfM derived Digital Elevation Models (DEMs) and 2) identify differences in roughness populations acquired from TSfM photogrammetry versus TLS. We identify an association between local topographic variation and error in the TSfM DEM. On flatter surfaces (e.g. bar and terrace surfaces), the difference between the TSfM and TLS DEMs are generally less than ±0.1m. However, in areas of high topographic variability (>0.4m) such as berm or terrace edges, differences between the TSfM and TLS DEMs can be up to ±1m. Our results suggest that grain roughness estimates from the TSfM point cloud generate values twice those derived from the TLS point cloud on coarse berm areas, and up to four-fold those derived from the TLS point cloud over finer gravel bar surfaces. This finding has implications when using SfM data to derive roughness metrics for hydrodynamic modelling. Despite the use of standard filtering procedures, noise pertains in the SfM DEM and the time required for its reduction might partially outweigh the survey efficiency using SfM. Therefore, caution is needed when SfM surveys are employed for the assessment of surface roughness at a reach-scale.

A GIS-Based Water Balance Approach Using a LiDAR-Derived DEM Captures Fine-Scale Vegetation Patterns

Topography exerts strong control on microclimate, resulting in distinctive vegetation patterns in areas of moderate to high relief. Using the Thornthwaite approach to account for hydrologic cycle components, a GIS-based Water Balance Toolset is presented as a means to address fine-scale species–site relationships. For each pixel within a study area, the toolset assesses inter-annual variations in moisture demand (governed by temperature and radiation) and availability (precipitation, soil storage). These in turn enable computation of climatic water deficit, the amount by which available moisture fails to meet demand. Summer deficit computed by the model correlates highly with the Standardized Precipitation–Evapotranspiration Index (SPEI) for drought at several sites across the eastern U.S. Yet the strength of the approach is its ability to model fine-scale patterns. For a 25-ha study site in central Indiana, individual tree locations were linked to summer deficit under different historical conditions: using average monthly climatic variables for 1998–2017, and for the drought year of 2012. In addition, future baseline and drought-year projections were modeled based on downscaled GCM data for 2071–2100. Although small deficits are observed under average conditions (historical or future), strong patterns linked to topography emerge during drought years. The modeled moisture patterns capture vegetation distributions described for the region, with beech and maple preferentially occurring in low-deficit settings, and oak and hickory dominating more xeric positions. End-of-century projections suggest severe deficit, which should favor oak and hickory over more mesic species. Pockets of smaller deficit persist on the landscape, but only when a fine-resolution Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) is used; a coarse-resolution DEM masks fine-scale variability and compresses the range of observed values. Identification of mesic habitat microrefugia has important implications for retreating species under altered climate. Using readily available data to evaluate fine-scale patterns of moisture demand and availability, the Water Balance Toolset provides a useful approach to explore species–environment linkages.

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data.

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

Icebergs in Sea Ice With TanDEM-X Interferometry

In this paper, the advantages of using an interferometric method for detecting and characterizing icebergs in sea ice were demonstrated. Iceberg topography was analyzed using single-pass TanDEM-X interferometric synthetic aperture radar (InSAR) data. Multiple InSAR data sets in bistatic mode were acquired over icebergs in sea ice in the Arctic region. InSAR processing was used to extract 3-D elevation information of the sea ice surface. The results firmly demonstrate the capability of TanDEM-X data to characterize the shape of icebergs. Very high resolution (VHR) optical satellite data were collected by Pleiades 1A over the same area to derive digital elevation models (DEMs) of ice features for validation. The accuracy of the extracted topography over icebergs was evaluated by comparing InSAR and optical DEMs. The quantitative comparison demonstrated good correspondence between InSAR and optical DEMs with root-mean-square value values of 2.2 m for icebergs and 0.6 m for sea ice, respectively. Using DEMs derived from VHR optical imagery, it was possible to calculate receiver operating characteristics (ROC) for detecting icebergs in sea using InSAR. The resulting ROC analysis illustrates a good detection performance.

Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain

Digital Elevation Models (DEMs) are frequently used in geology, environment, engineering and architecture related studies. Along with the technological advances, there are also improvements in the production techniques of these data. The use of low cost UAVs and what they can achieve, are therefore important for researchers. This study was designed to find answers to two main questions. Firstly, in areas of challenging conditions, if it is feasible to produce DEM with the low-cost UAV imagery, and if the obtained precision would be comparable enough to those of the proven methods. Secondly, if the addition of Ground Control Points (GCPs) would further increase the precision. As a result of this research, the uncertainty, in other words the RMSE of the DEM data obtained from the area with challenging topography, was found to be 0.57 m. In addition, the use of GCPs provided 6 cm decrease in the overall error margin.

Historic reconstruction of reservoir topography using contour line interpolation and structure from motion photogrammetry

ABSTRACTThe geometry of impounded surfaces is a key tool to reservoir storage management and projection. Yet topographic data and bathymetric surveys of average-aged reservoirs may be absent for many regions worldwide. This paper examines the potential of contour line interpolation (TOPO) and Structure from Motion (SfM) photogrammetry to reconstruct the topography of existing reservoirs prior to dam closure. The study centres on the Paso de las Piedras reservoir, Argentina, and assesses the accuracy and reliability of TOPO- and SfM- derived digital elevation models (DEMs) using different grid resolutions. All DEMs were of acceptable quality. However, different interpolation techniques produced different types of error, which increased (or decreased) with increasing (or decreasing) grid resolution as a function of their nature, and relative to the terrain complexity. In terms of DEM reliability to reproduce area–elevation relationships, processing-related disagreements between DEMs were markedly influenced by topography. Even though they produce intrinsic errors, it is concluded that both TOPO and SfM techniques hold great potential to reconstruct the bathymetry of existing reservoirs. For areas exhibiting similar terrain complexity, the implementation of one or another technique will depend ultimately on the need for preserving accurate elevation (TOPO) or topographic detail (SfM).

Mapping freshwater marsh species in the wetlands of Lake Okeechobee using very high-resolution aerial photography and lidar data

ABSTRACTAccurate marsh species maps are needed to study their abundance, distribution, habitat change, and to support the ongoing and future restoration activities in Lake Okeechobee, Florida, U.S.A. In this study, we integrated very high-resolution aerial photography with a light detection and ranging (LiDAR)-derived digital elevation model (DEM) for the discrimination of six freshwater marsh species (Salix caroliniana, Spartina bakeri, Polygonum spp., Typha spp., Phragmites australis, and Cladium jamaicense). Four techniques were combined in the mapping procedure, including object-based image analysis, machine learning classifiers, texture analysis, and ensemble analysis. The results showed that both texture and topography features were invaluable for marsh species mapping. A synergy of spatial, spectral, and topographical features achieved an overall accuracy (OA) of 85.3% and kappa coefficient of 0.83 using the Random Forest (RF) classifier. Ensemble analysis of the outputs from Support Vector Machine (SVM), RF, and Artificial Neural Network (ANN) did not increase the classification accuracy, but produced an uncertainty map to identify regions with a robust classification and areas difficult to map. Ensemble analysis was beneficial in getting further insight into marsh species mapping. The developed digital procedure is a promising alternative to traditional field survey and manual interpretation methods for generating marsh maps to support the lake restoration.

Shape and Size Complexity of Deep Seafloor Mounds on the Canary Basin (West to Canary Islands, Eastern Atlantic): A DEM-Based Geomorphometric Analysis of Domes and Volcanoes

Derived digital elevation models (DEMs) are high-resolution acoustic technology that has proven to be a crucial morphometric data source for research into submarine environments. We present a morphometric analysis of forty deep seafloor edifices located to the west of Canary Islands, using a 150 m resolution bathymetric DEM. These seafloor structures are characterized as hydrothermal domes and volcanic edifices, based on a previous study, and they are also morphostructurally categorized into five types of edifice following an earlier classification. Edifice outline contours were manually delineated and the morphometric variables quantifying slope, size and shape of the edifices were then calculated using ArcGIS Analyst tools. In addition, we performed a principal component analysis (PCA) where ten morphometric variables explain 84% of the total variance in edifice morphology. Most variables show a large spread and some overlap, with clear separations between the types of mounds. Based on these analyses, a morphometric growth model is proposed for both the hydrothermal domes and volcanic edifices. The model takes into account both the size and shape complexity of these seafloor structures. Grow occurs via two distinct pathways: the volcanoes predominantly grow upwards, becoming large cones, while the domes preferentially increase in volume through enlargement of the basal area.

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

Geodetic mass balance of surge‐type Black Rapids Glacier, Alaska, 1980–2001–2010, including role of rockslide deposition and earthquake displacement

AbstractWe determine the geodetic mass balance of surge‐type Black Rapids Glacier, Alaska, for the time periods 1980–2001 and 2001–2010 by combining modern interferometric synthetic aperture radar (InSAR)‐derived digital elevation models (DEMs), DEMs derived from archival aerial imagery, laser altimetry data, and in situ surface elevation measurements. Our analysis accounts for both the large rockslides and terrain displacements caused by the 2002 M7.9 earthquake on the Denali fault, which runs through Black Rapids Glacier. To estimate uncertainties, we apply Monte Carlo simulations. For the earthquake‐triggered rockslides we find a volume of 56.62 ± 2.86 × 106 m3, equivalent to an average debris thickness of 4.44 ± 0.24 m across the 11.7 km2 deposit area on the glacier. Terrain displacement due to the earthquake corresponds to an apparent glacier volume change of −53.1 × 106 m3, which would cause an apparent specific mass balance of −0.19 meter water equivalent (mwe) if not taken into account. The geodetic mass balance of Black Rapids Glacier is −0.48 ± 0.07 mwe a−1 for the entire 30 year period, but more negative for the period 2001–2010 (−0.64 ± 0.11 mwe a−1) than the period 1980–2001 (−0.42 ± 0.11 mwe a−1), in agreement with trends indicated by in situ mass balance measurements. Elevation data indicate no net thickening of the surge reservoir between 1980 and 2010, in contrast to what is expected during the quiescent phase. A surge of Black Rapids Glacier in the near future is thus considered unlikely.