The Effect of Multi-Source DEM Accuracy on the Optimal Catchment Area Threshold

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

This study applied existing methods of landslide susceptibility modelling of the mountainous area of the Cordillera Blanca (Peru), which is prone to landslides. In heterogeneous regions as in the Cordillera Blanca, the performance of a physically based approach Stability Index Mapping (SINMAP) was compared to empirical statistical models using logistic regression and a landslide density model. All models were applied to three different digital elevation models (DEMs): ASTER GDEM, SRTM (both 30-m spatial resolution), and TanDEM-X (12-m spatial resolution). Obtained results were evaluated using the area under the receiver operating characteristic curve (AUC) approach, once for a landslide inventory which extends over the whole study area and once using an inventory of a smaller area. The physically based approach (AUCs between 0.567 and 0.625) performed worse than the statistical models (AUCs from 0.672 to 0.759) over the large area. Additionally, all models received higher performances within the small area. This coincided with differences of the variability of the DEM-derived characteristics (e.g. slope angle and curvature) from the small to the large evaluation area. Using the smaller evaluation area, all models received higher AUC values (0.743–0.799), and the impact of the DEMs was less visible. The analysis of the susceptibility showed that mainly the same slopes are considered as most or least susceptible by all models, but SINMAP is classifying larger areas as unstable or stable. Overall, this study showed that regional-scale landslide susceptibility modelling can lead to reasonable results even in regions with scarce model input data, but performances of different DEMs and models need to be evaluated carefully.

This study investigates the impact of different digital elevation model (DEM) resolutions on the topological attributes and simulated runoff, as well as the sensitivity of runoff parameters in the Mahabad Dam watershed in Iran. The watershed and streamlines were delineated in ArcGIS, and the hydrologic analyses were performed using the Soil and Water Assessment Tool (SWAT). The sensitivity analysis on runoff parameters was performed, using the Sequential Uncertainties FItting Ver. 2 algorithm, in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) program. The results indicated that the sensitivity of runoff parameters, watershed surface area, and elevations changed under different DEM resolutions. As the distribution of slopes changed using different DEMs, surface parameters were most affected. Furthermore, higher amounts of runoff were generated when DEMs with finer resolutions were implemented. In comparison with the observed value of 8 m3/s at the watershed outlet, the 12.5 m DEM showed more realistic results (6.77 m3/s). Comparatively, the 12.5 m DEM generated 0.74% and 2.73% more runoff compared with the 30 and 90 m DEMs, respectively. The findings of this study indicate that in order to reduce computation time, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from −772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines.

The present study aimed to delineate a robust watershed boundary and extract its morphometric parameters in the Karnaphuli watershed, Bangladesh, using different digital elevation models (DEMs). Two DEMs, the shuttle radar topographic mission (SRTM) and the terra advanced spaceborne thermal emission and reflection radiometer (ASTER), were employed to delineate watershed boundaries and evaluate various morphometric characteristics. Raster data such as fill sinks, flow direction, and flow accumulation were utilized to delineate the watershed. The morphometric analysis included the estimation of linear, areal, and relief parameters. The findings revealed a noticeable difference between the datasets; ASTER delineated a larger watershed area than SRTM. Regarding stream order, there were streams ranging from 1st to 6th order, encompassing numerous small, medium, and main channels of the river. Drainage density was calculated as 0.52 km/km² for SRTM and 0.51 km/km² for ASTER, both having a spatial resolution of 30 m. The bifurcation ratio ranged from 1.94 for SRTM to 2.45 for ASTER, indicating varying degrees of structural disturbance influenced by geological factors. The form factor indicated an elongated shape of the study area. The overall dimensions of the streams and the watershed extent suggest moderate mean annual rainfall discharge. The watershed exhibited relatively high denudation rates, attributed to the basin relief. A low drainage density indicated the importance of infiltration processes. The findings of this study provide valuable insights into the spatial variability of watershed characteristics, highlighting the need for tailored management strategies for the Karnaphuli River. Implementing targeted conservation measures and watershed management practices based on these morphometric parameters can enhance water resource sustainability and ecological health in the region.

Evaluating short-term morphological changes in a gravel-bed braided river using terrestrial laser scanner

<strong class="journal-contentHeaderColor">Abstract.</strong> Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and K&auml;&auml;b (2011) and Rosenholm and Torlegard (1988) . The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using 23 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM pairs from the western margin of the Greenland Ice Sheet. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (4.6 % on average and 15.3 % maximum) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and K&auml;&auml;b considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.

Using historical aerial photographs to measure earthquake deformation: Testing the effects of scan resolution

In this paper, the study area was the Inner Niger Delta (IND) in Mali, West Africa. The IND is threatened by climate change, increasing irrigation, and dam operations. 2D hydrodynamic modelling was used to simulate water levels, discharge, and inundation extent in the IND. Three different digital elevation models (DEM) (SRTM, MERIT, and a DEM derived from satellite images were used as a source of elevation data. Six different models were created, with different sources of elevation data and different downstream boundary conditions. Given that the performance of the models varies according to the location in the IND, the variable under consideration and the performance criteria, Bayesian Model Averaging (BMA) was used to assess the relative performance of each of the six models. The BMA weights, along with deterministic performance measures, such as the Nash Sutcliffe coefficient (NS) and the Pearson’s correlation coefficient (r), provide quantitative evidence as to which model is the best when simulating a particular hydraulic variable at a particular location. After the models were combined with BMA, both discharge and water levels could be simulated with reasonable precision (NS &gt; 0.8). The results of this work can contribute to the more efficient management of water resources in the IND.

Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

Topographic correction models (TCMs) are valid on satellite image data preprocessing steps. Theillumination angle may be sensitive to different terrain slope and aspect conditions base on sun-terrain-sensorgeometry. Although the topographic correction is influenced by the sun azimuth and zenith angle, the correction resultcan be equally in the same image status. By contrast, the terrain factors change with different digital elevation model(DEM) resolution in the topographic correction equations and cause a significant effect. Slope is sensitive in ruggedterrain, and aspect is impressionable at flat surface at a coarse DEM resolution data. As the DEM resolution lead adistinct result on TCMs, this research is aimed to examine the impact of DEM resolution on the accuracy of terrainrepresentation and of the gradient determined. In this study, five TCMs, including cosine correction, C correction, SCS correction, SCS+C correction and Minnaert correction models are compared by different resolutions using SPOTimage data. The 5 meter DEM obtained from Ministry of the interior will be resampled to 10 to 500 meters to testthose topographic models sustainability on Lienhuachih Research Center. The accuracy of five topographic correctionmodels base on different DEM resolution will be evaluated by root-mean-square error (RMSE).

For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected as the study case, and an inflow flood forecast scheme was designed for the reservoir by a physically based distributed hydrological model, the Liuxihe model. The results show that the Liuxihe model has strong applicability for flood forecasting in the studied reservoir basin and that the simulation results are very accurate. This study also found that the use of different Digital Elevation Model (DEM) data sources has a certain impact on the structure of the Liuxihe model, but the constructed models can both simulate the inflow flood process of the Baipenzhu Reservoir well. At the same time, the Liuxihe model can reflect the spatial variation in rainfall well, and using the Particle swarm optimization (PSO) algorithm to optimize the initial model parameters can greatly reduce the uncertainty of the model forecasts. According to China’s hydrological information forecast standards, the Liuxihe model forecast schemes constructed by the two data sources are rated as Grade A and can be used for real-time flood forecasting in the Baipenzhu Reservoir basin.

&amp;lt;p&amp;gt;Nowadays Unmanned Aerial Vehicle (UAV) is one of the most utilized tools in the field of coastal geomorphology studies due to its efficiency and cost-effectiveness to carry out high-spatial and temporal resolution topographic surveys. The images produced by UAV surveys can be processed using Structure from Motion (SfM) photogrammetry, which allows 3D reconstruction of the terrain from the series of overlapping images. This research aimed to utilize UAV topographic surveys to characterize the geomorphological evolution of a portion of the dune belt that was subjected to a restoration in 2016. The study site is located in the protected natural area of the Bevano River mouth in Ravenna, (Northern Adriatic Coast, Italy). The reinforcement of the dune system was initiated since the zone is primarily characterized as a low-lying coastal area and is subjected to increasing environmental risks, such as coastal erosion, storm surge, groundwater and soil salinization. The restoration measure included two windbreak wooden fences, which were installed in front of the dune foot and parallel to the coast to stop wind and facilitate sand deposition and accumulation, to favor embryo dune formation and prevent sand loss toward the inland, out of the sedimentary cell. UAV topographic survey, coupled with GPS ground survey using Real-time Kinematic (RTK) positioning, were carried out from 2016 to 2021 in order to assess the geomorphological evolution of the area over time. SfM photogrammetry was carried out to generate the point cloud and orthomosaic images for each survey year using Agisoft Metashape Professional. Point cloud data were interpolated in ArcMap to create Digital Elevation Models (DEMs), while the orthomosaic images were utilized to confirm the possible sources of data noise in the model and assess vegetation changes. The collected GPS data points, including Ground Control Points (GCPs) and several dune profiles, were used to validate the DEMs. Then, the volumetric changes in sediment storage over time were calculated by using the DEM of Difference (DoD) approach under the Geomorphic Change Detection (GCD) extension toolbar in ArcMap. Probabilistic thresholding was used as the uncertainty analysis method for the volume calculation. The changes in dune height and slope were assessed using both the GCD and 2D profiler toolbar as well. The results show that the windbreak fence has proven to be an effective intervention to prevent dune erosion since significant geomorphological changes and vegetation colonization have occurred. Based on volume calculations, main sand accumulation was observed along the dune foot where the fences were established. The sand deposition has also reduced the slope steepness of the dune and some profiles exhibit embryo dune development. Erosion has only been evident in the northern beach portion towards the end of the fence. An increase in both pioneer species and stabilizing plants were also noticed on the dune front and crest, respectively.&amp;lt;/p&amp;gt;

The use of preevent and postevent digital elevation models (DEMs) to estimate the volume of rock avalanches on glaciers is complicated by ablation of ice before and after the rock avalanche, scour of material during rock avalanche emplacement, and postevent ablation and compaction of the rock avalanche deposit. We present a model to account for these processes in volume estimates of rock avalanches on glaciers. We applied our model by calculating the volume of the 28 June 2016 Lamplugh rock avalanche in Glacier Bay National Park, Alaska. We derived preevent and postevent 2‐m resolution DEMs from WorldView satellite stereo imagery. Using data from DEM differencing, we reconstructed the rock avalanche and adjacent surfaces at the time of occurrence by accounting for elevation changes due to ablation and scour of the ice surface, and postevent deposit changes. We accounted for uncertainties in our DEMs through precise coregistration and an assessment of relative elevation accuracy in bedrock control areas. The rock avalanche initially displaced 51.7 ± 1.5 Mm3of intact rock and then scoured and entrained 13.2 ± 2.2 Mm3of snow and ice during emplacement. We calculated the total deposit volume to be 69.9 ± 7.9 Mm3. Volume estimates that did not account for topographic changes due to ablation, scour, and compaction underestimated the deposit volume by 31.0–46.8 Mm3. Our model provides an improved framework for estimating uncertainties affecting rock avalanche volume measurements in glacial environments. These improvements can contribute to advances in the understanding of rock avalanche hazards and dynamics.

Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Besides, the characteristics of the river crossing bridges may deteriorate due to flood induced scouring around bridge piles or due to flood loads. Thus, in this study high-resolution topography of the study region was acquired during two consecutive years by unmanned aerial vehicle (UAV) based surveys using Structure-from-Motion (SfM) processing. Following the extraction of digital elevation models (DEM), repeat data that were obtained at each UAV survey were compared using Geomorphic Change Detection (GCD) to calculate volumes of deposition and erosion via DEM of difference (DoD) algorithm. Thus, detailed high-resolution maps of the river channels can be rapidly and efficiently generated by low cost UAV based measurement methods in order for continuous tracking of stream channel morphology for the rivers sensitive to floods.