Topographic Data Research Articles

Effect of powder properties, process parameters, and recoating speed on powder layer properties measured by in-situ laser profilometry and part properties in laser powder bed fusion

In laser-based powder bed fusion of metals (PBF-LB/M), the powder layer is the link between the powder properties and the resulting part quality. Powder layer quality is a key metric related to powder spreadability and ultimately part quality, yet it is still unclear how it can be quantified. This is due to the difficulty of studying powder layer properties during the process. This study investigates the influence of powder properties, process parameters, and recoating speed on the surface roughness of the powder layer and the part, as well as on the effective thickness of the powder layer and solidified layer, and the resulting relative part density. Utilizing in-situ laser profilometry, high-resolution topographical data of the powder layer and the part surface were acquired, with minimal interference to the PBF-LB/M process. Six AlSi10Mg powders with varying particle size distribution, morphology, and flowability were processed using a wide range of recoating speeds and scan speeds to create powder layers with a wide range of properties. The results reveal a strong correlation between energy input and the effective powder layer thickness where lower scan speed results in an increased effective powder layer thickness due to material losses. Additionally, faster recoating decreases the powder layer density, which is moderated by the median particle size where the effect is strongest for fine powders. The surface roughness of the powder layer and top part surface are influenced by the recoating speed, energy input, and particle size, and they are strongly linked to each other. This highlights the importance of considering realistic substrate surface roughnesses in both powder spreading experiments and simulations. Finally, layer properties affect the process stability, resulting in small differences in relative part density.

Water and carbon fluxes from a supra-permafrost aquifer to a stream across hydrologic states

Supra-permafrost aquifers within the active layer are present in the Arctic during summer. Permafrost thawing due to Arctic warming can liberate previously frozen particulate organic matter (POM) in soils to leach into groundwater as dissolved organic carbon (DOC). DOC transport from groundwater to surface water is poorly understood because of the unquantified variability in subsurface properties and hydrological environments. These dynamics must be better characterized because DOC transport to surface waters is critical to predict the long-term fate of recently thawed carbon in permafrost environments. Here, we used distributed Darcy’s Law calculations to quantify groundwater and DOC fluxes into Imnavait Creek, Alaska, a representative headwater stream in a continuous permafrost watershed. We developed a statistical ensemble approach to model the parameter variability and range of potential contributions of steady-state groundwater flow to the creek. We quantified the model prediction uncertainty using statistical sampling of in-situ, active-layer soil hydro-stratigraphy (water table, ice table, and soil stratigraphy), high-resolution topography data, and DOC data. Moreover, the predicted groundwater discharge values representing all possible hydrologic conditions towards the end of the thawing season were also considered given the potential variability in saturation. The model predictions were similar to and span most of the observed range of Imnavait Creek streamflow, especially during recession periods, and also during saturation excess overland flow. As the Arctic warms and supra-permafrost aquifers deepen, groundwater flow is expected to increase. This increase is expected to impact stream, river, and lake biogeochemical processes by dissolving and mobilizing more soil constituents in continuous permafrost regions. This study highlights how quantifying the uncertainty of hydro-stratigraphical input parameters helps understand and predict supra-permafrost aquifer dynamics and connectivity to aquatic systems using a simple, but scalable, modeling approach.

Seafloor Topographic Data Processing in Near-Seafloor Acoustic Field Simulation

Near-seafloor acoustic field characteristics are essential prior knowledge for near-seafloor underwater acoustic engineering. Scholte waves are a crucial component of the near-seafloor acoustic fields. These fields, when considering Scholte waves, are susceptible to seafloor relief. However, open-source bathymetric datasets generally lack sufficient precision. Therefore, acoustic field simulations using open-source data can contain significant errors or even introduce erroneous propagation characteristics. The spectral element method (SEM) is an example of exploring the influence of an inadequate spatial-sampling rate and sea-depth precision on acoustic field simulations. In this article, appropriate methods for topographic processing are presented. The results indicate that the terrain can be corrected using cubic spline interpolation in cases of an inadequate spatial-sampling rate. Where there is insufficient sea-depth precision, this study proposes a terrain processing method. The first step involves sequentially determining the interpolation points for the rising and falling edge, depressions, bulges, and horizontal segments. Then, it adopts cubic spline interpolation. The SEM examples effectively verify this effect. Given the limited research on terrain correction in acoustic field simulations, this study introduces a low-complexity method that can effectively support exploring acoustic fields affected by seafloor terrain.

Topographic and Geodetic Survey for Samarkand's Administrative Boundaries

General Background: Administrative and territorial boundary delineation is vital in the effective governance and economic planning of urban regions globally, with digital technologies increasingly integrated into these processes for accuracy and efficiency. Specific Background: In Uzbekistan, the city of Samarkand’s administrative boundaries are undergoing significant realignment as part of a broader initiative to incorporate strategic urban centers and enable digital land management across administrative zones. Knowledge Gap: Existing methods lack precise integration with digital geospatial frameworks, leaving a gap in high-resolution topographic data essential for clear administrative delineation. Aims: This study seeks to develop and apply an accurate, digitally supported methodology for determining the boundaries of Samarkand's administrative units using advanced geodetic and topographic techniques. Results: By utilizing the CREDO DAT program, this study achieved precise boundary coordinates through the automated processing of survey points, and further analysis was conducted using the Panorama program for detailed map orientation. Novelty: This research presents a unique approach by integrating CREDO DAT with geospatial tools in administrative boundary demarcation, enabling accurate and reproducible results that surpass traditional methods in efficiency and scalability. Implications: The findings support the strategic planning of Samarkand’s infrastructure and resource allocation while also laying the groundwork for applying similar methodologies in other urban regions, advancing Uzbekistan’s digital transformation efforts in land management.Highlights: Digital tools enhance accuracy in administrative boundary mapping. CREDO DAT and Panorama software streamline data processing for precise mapping. This method supports effective urban planning and resource management in Samarkand. Keywords: Administrative Zones, Border, Territory, District, City

Ku-Band SAR-Drone System and Methodology for Repeat-Pass Interferometry

In recent years, drone-based Synthetic Aperture Radar (SAR) systems have emerged as flexible and cost-efficient solutions for detecting changes in the Earth’s surface, retrieving topographic data, or detecting ground displacement processes in localized areas, among other applications. These systems offer a unique combination of short and versatile revisit times and flexible acquisition geometries that are not achievable with space-borne, airborne, or ground-based SAR sensors. However, due to platform limitations and flight stability issues, they also present significant challenges regarding instrument design and data processing, particularly when generating interferometric repeat-pass datasets. This paper demonstrates the feasibility of repeat-pass interferometry using a Ku-band drone-based SAR system. The system integrates a dual-channel Ku-band Frequency Modulated Continuous Wave (FMCW) radar with cross-track single-pass interferometric capabilities, mounted on a drone platform. The proposed repeat-pass interferometric processing chain leverages an accurate Digital Elevation Model (DEM), generated from the single-pass interferograms, to precisely coregister the entire stack of acquisitions, thereby producing repeat-pass interferograms free from residual motion errors. The results underscore the potential of this system and the processing chain proposed for generating multi-temporal repeat-pass stacks suitable for repeat-pass applications.

Evaluating potential landing sites for the Artemis III mission using a multi-criteria decision making approach

The selection of a landing site within the Artemis Exploration Zone (AEZ) involves multiple factors and presents a complex problem. This study evaluates potential landing sites for the Artemis III mission using a combination of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methodologies, specifically the TOPSIS algorithm. By integrating topographic, illumination, and mineralogy data of the Moon, we assess 1247 locations that meet the Human Landing System (HLS) requirements within 13 candidate regions and Site 004 near the lunar south pole. Criteria considered include surface visibility, HLS-astronaut line of sight, Permanently Shadowed Regions (PSRs), sunlight exposure, direct communication with Earth, geological units, and mafic mineral abundance. Site DM2 (Nobile Rim 2), particularly the point at latitude 84°12’5.61”S (−84.20156°) and longitude 60°41’59.61”E (60.69989°), is the optimal location for landing. Sensitivity analysis confirms the robustness of our approach, validating the suitability of the best location despite the MCDM method employed and variations in criteria weightings to prioritize illumination and PSRs. This research demonstrates the applicability of GIS-MCDM techniques for lunar exploration and the potential benefits they can bring to the Artemis program.

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy

The topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. This has proven beneficial in order to distinguish the focus region from out-of-focus areas where outliers are frequently detected. This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. Additionally, a simulation model of focus variation microscopy is introduced to validate both the measuring system and the proposed algorithm, thereby enhancing the overall performance of focus variation microscopy. Finally, comparisons of measurement results on rough metal additive manufacturing workpieces with and without self-adaptive thresholds are discussed to demonstrate the algorithm’s effectiveness.The utilization of self-adaptive thresholds demonstrably reduces the uncertainty range in roughness parameter calculations. For example, in the case of an additive manufactured metal sample due to outlier elimination, the Sz roughness value reduces from 543 µm to 413 µm.

Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea

Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface.

Mapping of soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria

This study centres on mapping soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria. A multi-criteria research design was adopted in carrying out this study by using remote sensed images, topographic, soil and rainfall data. K Factor, R Factor, P Factor, K Factor and Digital Elevation Model were generated. SWAT model was used to generate surface runoff and sediment yield and AHP model were used to identify and map levels of soil erosion risk in the study area. Loamy sandy soil with a K Factor value of 0.23 – 0.34 (t h MJ-1 mm-1) and loam soil with a K factor Value of 0.08 – 0.14 (t h MJ-1 mm-1) dominates the study area. The organic matter contents for all the soils were rated low with an average measured organic matter of 0.97 (%). The rainfall erosivity factor (R-factor) results showed variations across the study area from 1029.295 to 1343.092, 1343.092 to 1589.713, 1589.713 to 1744.922, 1744.922 to 1965.777, and 1965.777 to 2060.864 MJ mm ha year-1, h-1 year-1. Areas with dense vegetation had lower P-factors indicating better protection against erosion, while areas under cultivation, especially those with poor management practices and without cover crops show higher P-factors. Almost all the area has a moderate (0.5-0.6) C factor which means moderate protection, areas with higher protection (0.6-1) C factor occupied a small portion towards the east while patches of low C factors are found scattered in the north, west northwest, south and eastern part of the study area. The eastern part of the study area are more susceptible to soil erosion with value ranging from 3.2-5.8, 5.9-9.9 and 10.0-19. Slope steepness and slope length is lower in the northern, western and south-west of the study area. Areas with annual soil loss between 0 to 10 grams, 10 to 20 grams and 30 to 40 grams dominates the northern, western and southern parts of the study area. The erosion risk map depicts that very low-risk area coverage of 24.06%, low risk covers 20.29%, moderate risk occupies 17.75% and high risk erosion risk covers 21.30% while very high risk occupies 16.61% of the total area. This study recommends terracing and gabions in areas identified at risk of erosion, promotion and adoption of agronomic soil conservation methods of mulching, cover cropping and planting plants to enhance soil stability and reduce erosion.

Assessment of coastal flood risk scenarios on infrastructure in the Keta municipality in Ghana using a GIS approach

Coastal flooding and erosion, caused by climate change-induced sea level rise, pose significant threats to low-lying coastal areas worldwide. The African continent, including Ghana, has experienced severe impacts from these hazards, affecting the socio-economic development of coastal communities. This research focuses on the Keta municipality in Ghana. The Keta municipality is highly vulnerable to coastal flooding and erosion due to its low elevation and the construction of the Akosombo hydroelectric dam. This research aims to predict the impact of coastal flooding on infrastructure in the Keta municipality using different flood scenarios. It also fills the existing knowledge gap in understanding how future flood risk can impact on critical infrastructure and consequently on other vital socio-economic sectors in the coastal community of Keta. The study utilizes a Geographic Information System (GIS)-based approach, combining various datasets, including topographic data, administrative shapefiles, field survey points, and infrastructure data. Two flood scenarios were considered to assess areas at risk: a 2.5-m sea level flood scenario representing potential climate change events and a worst-case 5-m sea level flood scenario. The findings reveal that 3.3 km2 (9 %) of coastal land area, 3.9 km of roads, and 69 structures are at risk under a 2.5m sea level rise scenario, increasing to 7.1 km2 (19.4 %), 13.6 km of roads, and 667 structures under a 5m scenario. The study highlights the urgency of addressing these risks to protect coastal communities' socio-economic development and livelihoods. Recommendations include implementing appropriate coastal management strategies, improving infrastructure resilience, and promoting sustainable land-use practices. By understanding and addressing the future impact of coastal flooding, decision-makers can mitigate climate change's adverse effects on coastal areas and ensure the region's long-term sustainability.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.

Preoperative nTMS analysis: a sensitive tool to detect imminent motor deficits in brain tumor patients.

One of the challenges in surgery of tumors in motor eloquent areas is the individual risk assessment for postoperative motor disorder. Previously a regression model was developed that permits estimation of the risk prior to surgery based on topographical and neurophysiological data derived from investigation with nTMS (navigated Transcranial Magnetic Stimulation). This study aims to analyze the impact of including additional neurophysiological TMS parameters into the established risk stratification model for motor outcome after brain tumor surgery. Biometric and clinical data of 170 patients with glioma in motor eloquent areas were collected prospectively. In addition, the following nTMS parameters were collected bihemispherically prior to surgery: resting motor threshold (RMT), recruitment curve (RC), cortical silent period (CSP) and a nTMS based fibertracking to measure the tumor tract distance (TTD). Motor function was quantified by Medical Research Council Scale (MRCS) preoperatively, seven days and three months postoperatively. Association between nTMS parameters and postoperative motor outcome was investigated in bivariate and multivariable analyses. The bivariate analysis confirmed the association of RMT ratio with the postoperative motor outcome after seven days with higher rates of worsening in patients with RMT ratio > 1.1 compared to patients with RMT ratio ≤ 1.1 (31.6% vs. 15.1%, p = 0.009). Similarly, an association between a pathological CSP ratio and a higher risk of new postoperative motor deficits after seven days was observed (35.3% vs. 16.7% worsening, p = 0.025). A pathological RC Ratio was associated postoperative deterioration of motor function after three months (42.9% vs. 16.2% worsening, p = 0.004). In multiple regression analysis, none of these associations were statistically robust. The current results suggest that the RC ratio, CSP ratio and RMT ratio individually are sensitive markers associated with the motor outcome 7days and 3months after tumor resection in a presumed motor eloquent location. They can therefore supply valuable information during preoperative risk-benefit-balancing. However, underlying neurophysiological mechanisms might be too similar to make the parameters meaningful in a combined model.

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.

Ecological Impact Assessment of Permanent Site of Federal Polytechnic Oko Using Topographic Survey Method

Topographic survey based ecological impact assessment provides a mechanism for data integration, development, management and output presentation in a spatial environment. This research involved incorporating spatial data of all salient points at the Permanent Site of Federal Polytechnic, Oko, Anambra state to find a solution to the erosion menace. Differential Global Positioning System (DGPS) receiver was used to acquire spatial data of buildings, roads, spot height, drainages, catchment pits, etc. The topographic data were processed using ArcGIS software to analyze and produce the topographic maps. Presentations from the topographic survey revealed that the catchment topography runs between 160m – 198m height above the datum, the total area of the catchment area being 216320.653m2, perimeter is 2604.449m and the length of the mainstream is 1125.428m. Topographic maps of the area were used to assess the impact of the ecology on the area of study by analyzing the built up area, surface roughness, impervious and pervious surfaces. Hydraulic design of the drainage using best hydraulic section principle for most economic section to carry the flow was determined to solve the erosion problems in the catchment area. The flow rate obtained was 2.55m3/s and dimensions of the channel to be 1.1m depth and 2.2m width. The design of the selected structural members was done to mitigate against the erosion menace in the study area.

Characterisation of surface morphology of high voltage DC relay contacts under arc erosion

Abstract High voltage DC relays are a crucial component in new energy vehicles. Due to the high failure rate caused by contact sticking (fusion welding), existing technologies can only analyze contact morphology after failure. There is an urgent need to determine the quantitative characterization value of contact surface morphology and the correlation between morphology, failure mechanisms, and working conditions. In this study, an Olympus DSX1000 microscope is used to extract surface topography data, and a quantitative topography characterization method based on a machine vision system is proposed to quantitatively analyze contact surface changes after arc erosion. Grey correlation analysis was applied to explore the relationship between electrical parameters such as voltage, current, and capacitance, and the contact morphology features. The results show that current and capacitance have the greatest influence on contact surface features after arc erosion, while voltage has a relatively smaller influence. This conclusion is consistent with the experimentally observed arc erosion features. Finally, an arc ion sputtering deposition model was developed to analyze the material transfer mode and its influence on the contact failure mechanism. The results provide a scientific basis for the analysis and design improvement of high voltage DC relays.

Identification of Spatial Patterns of Soil Erosion Based on the Combination of RUSLE and MCDA in the Ahferom District, Northern Ethiopia

Soil erosion is a widespread concern that is indeed considered to be a significant environmental issue, and it has particularly severe consequences in less developed countries like Ethiopia. An effective watershed management procedure for establishing priority is supported by the identification of erosion-susceptible areas. Therefore, the main objective of the study was to assess soil erosion dynamics and its spatial pattern using a novel methodological framework combining the RUSLE and MCDA. The study used data on land use and cover, topography, soil, and climatic data. The analytical hierarchy process (AHP) were used to identify soil erosion-susceptible areas and the factors were weighted using a pairwise comparison matrix, and weights were combined using weighted overlay in GIS. Our results indicated that the mean annual soil loss rate was 27.10 t ha−1 yr−1, while the total soil loss from the entire study area was 3.11 Mt. The highest soil loss was observed in bare land (30.54 t ha−1 yr−1) and farmland (23.65 t ha−1 yr−1), which were considered as the most susceptible land types to erosion. Likewise, 10.3% of the study area is very highly susceptible; 20.2% is highly susceptible, 24.2% of the area is moderately susceptible, 27.1% is low, and 18.2% has very low susceptibility. The district’s most significant erosion-susceptible areas are characterized by steep slopes that are composed of farmland and bare land. This suggests the majority of the area is susceptible to erosion, requiring interventions to reverse the alarming degradation level. The presented framework has a board application to estimate regional soil erosion and to identify spatial patterns of soil erosion.