Terrain Variables Research Articles

An Innovative Dynamic Model for Predicting Typhoon Track Deflections over Complex Terrain

This study presents an innovative, dynamic model for predicting typhoon track deflections over complex terrain. Based on potential vorticity conservation, the model incorporates a topographic adjusting parameter (α) and a meridional adjusting velocity (MAV) to capture the vortex’s response to terrain variations. Simulations using an idealized bell-shaped mountain and Taiwan’s realistic topography reveal that steeper terrain gradients consistently deflect typhoon tracks southward. This steering effect intensifies with increasing vortex strength due to a larger α, leading to enhanced MAV. Shallower approach angles also amplify deflections due to prolonged terrain interaction. Results highlight the significant role of Taiwan’s Central Mountain Range in shaping typhoon trajectories. This model offers a refined approach for predicting typhoon behavior near complex terrain, advancing forecasting capabilities, and enhancing disaster preparedness strategies.

Geological study of the seafloor applied to marine renewable energies

ABSTRACT This paper shows an example of how to create morphological and geological maps of the seabed combining high-resolution bathymetric data and onshore geological information. The mapped area corresponds to the BIMEP offshore test site, located in the south-eastern part of the Bay of Biscay, in the northern part of the Iberian Peninsula. In the Morphological map, using semi-automatic methods, the seafloor is categorized in 10 classes, obtained by the combination of terrain variables and a decision table. In the Geological map, onshore-defined geological units are extended into the continental shelf, and bedding traces, folds, and fractures are depicted. These maps could serve as valuable tools for stakeholders involved in marine energy projects, enabling informed decision-making and promoting sustainable development practices.

Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR.

The Prediction Model of Seismic Variation in Complex Terrain based on the BP Neural Network with Cavities

The Prediction Model of Seismic Variation in Complex Terrain based on the BP Neural Network with Cavities

A novel flood conditioning factor based on topography for flood susceptibility modeling

Flood is one of the most devastating natural hazards. Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management. Existing flood conditioning factors inadequately account for regional characteristics of flood in the depiction of topography, potentially leading to an overestimation of flood susceptibility in flat areas. Addressing this gap, this study proposes a novel flood conditioning factor, local convexity factor (LCF), to enhance the accuracy of flood susceptibility modeling. Initially, LCF is computed based on a standard normal Gaussian surface to highlight elevation variations in local terrain. Subsequently, LCF is applied to flood susceptibility modeling using seven machine learning models across four distinct basins. Comparative analysis is conducted between flood susceptibility maps with and without the application of LCF to evaluate its impact on flood susceptibility modeling. The results demonstrate that the proposed LCF can enhance the accuracy of flood susceptibility modeling to varying degrees, across the four basins investigated. The Fujiang basin exhibited the most substantial improvement, with its AUC improved from 0.861 to 0.886, Producer’s Agreement improved from 0.869 to 0.899, and Overall Agreement improved from 0.778 to 0.811. Comparation with hydrodynamic inundation maps shows that particularly in relatively flat terrain areas, flood susceptibility maps incorporating LCF offer more precise delineation between flood-prone and non-flood-prone zones. This research holds potential for widespread application in the prediction of flood susceptibility using machine learning models, providing a novel perspective for enhancing their accuracy

Large eddy simulation of near-surface boundary layer dynamics over patchy snow

The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions.

Hidden Archaeological Remains in Heterogeneous Vegetation: A Crop Marks Study in Fortified Settlements of Northwestern Iberian Peninsula

This study evaluates the effectiveness of multispectral imaging via Unmanned Aerial Systems (UAS), in combination with advanced digital image processing techniques, for the detection and mapping of archaeological sites within diverse landscapes. The research focuses on six case studies located in the northwest of the Iberian Peninsula, a region marked by complex vegetation patterns and varying topography. The primary objective is to assess the potential of these non-invasive remote sensing techniques in identifying crop marks associated with buried structures from ancient, fortified settlements. By means of Principal Component Analysis (PCA) and vegetation indices, the study aims to pinpoint areas of interest that may indicate the presence of archaeological features, while effectively distinguishing them from modern disturbances or natural terrain variations. The research encountered several challenges, including seasonal variations in crop conditions and recent land-use changes. The methodology successfully identified distinct archaeological features. In some instances, natural vegetation variability, typically seen as an obstacle, enhanced the visibility of crop marks, aiding in the detection of underlying structures. These results offer a cost-effective and scalable option for preliminary archaeological surveys, particularly in refining survey methodologies and guiding future excavation efforts aimed at uncovering and preserving ancient, fortified settlements in the region.

Severity of a megafire reduced by interactions of wildland fire suppression operations and previous burns

Abstract Burned area and proportion of high severity fire have been increasing in the western USA, and reducing wildfire severity with fuel treatments or other means is key for maintaining fire-prone dry forests and avoiding fire-catalyzed forest loss. Despite the unprecedented scope of firefighting operations in recent years, their contribution to patterns of wildfire severity is rarely quantified. Here we investigate how wildland fire suppression operations and past fire severity interacted to affect severity patterns of the northern third of the 374 000 ha Dixie Fire, the largest single fire in California history. We developed a map of the intensity and type of suppression operations and a statistical model of the Composite Burn Index (CBI) including weather, fuels, and terrain variables during the fire to quantify the importance of operations and prior fires on wildfire severity. Wildfire severity was estimated without operations and previous fires and then compared with modeled severity under observed conditions. Previous low and moderate-severity fire without operations decreased CBI by 38% and 19% respectively. Heavy operations and offensive firing in the footprint of past fires lowered fire severity even more compared to prior fire alone. Medium operations and defensive firing reduced but did not eliminate the moderating effects of past fires. This analysis demonstrates important interactions between suppression operations and previous burns that drive patterns of fire severity and vegetation dynamics in post-fire landscapes. Given the need to reduce wildfire severity to maintain forest resilience, particularly with a warming climate, increased attention to using operations and severity patterns of previous fires known to reduce wildfire severity in megafires are likely to increase forest resilience and improve ecological outcomes.

Joint Inversion of DC Resistivity and Gravity Data with Undulating Terrain Based on Deformed Hexahedral Mesh

In the field of mineral resource exploration, accurate imaging of subsurface structures is key to discovering and assessing potential mineral deposits. Traditional single geophysical methods, limited by terrain variations and their own constraints, can lead to divergent solutions and structural inconsistencies, affecting the reliability of exploration outcomes. To address these challenges, this paper presents a joint inversion method for three-dimensional direct current (DC) resistivity and gravity data based on a deformed hexahedral mesh. The article begins by outlining the current state of development of the method under study and proposes a research plan, followed by a detailed explanation of the theoretical basis and algorithmic implementation of the proposed method. Model tests confirm the advantages of the deformed hexahedral mesh in reducing terrain impacts and enhancing model resolution, demonstrating the optimization and complementarity of the resolution between the two methods after joint inversion. Finally, applying this method to actual data from the Huaniu Mountain area shows that joint inversion not only improves the consistency of the ore belt structure but also provides a more precise analysis for the quantitative interpretation of the distribution of underground mineral resources. This confirms the method’s effectiveness and potential in practical geological exploration.

Three-dimensional numerical simulation and experimental validation of airborne ground-penetrating radar based on CNCS-FDTD method under undulating terrain conditions

Traditional Finite Difference Time Domain (FDTD) approaches face challenges with increased computational demands and errors as terrain complexity and flight altitude rising. This study introduces the Crank-Nicolson-Cycle-Sweep-FDTD (CNCS-FDTD) method, enhancing airborne ground penetrating radar (GPR) simulations over undulating terrains. CNCS-FDTD, as an unconditionally stable implicit algorithm, overcomes these by allowing larger time steps without the constraints of the Courant-Friedrichs-Lewy (CFL) condition. Our research aims to assess how CNCS-FDTD can improve computational efficiency and accuracy in modeling airborne GPR responses across varied terrains. Initial simulations using a three-dimensional graben model indicate that CNCS-FDTD can maintain calculation stability with significantly larger time steps, reducing computational time by 42%. To further verify the reliability of the numerical simulation results, the paper also presents experimental tests of the undulating terrain model. By comparing the numerical and physical simulation results of models under different flight altitudes and terrain conditions, the accuracy of the simulation results is validated.

Susceptibility assessment of glacier-related debris flow on the southeastern Tibetan Plateau using different hybrid machine learning models

The southeastern Tibetan Plateau (SETP) is a construction area of several key infrastructure projects in China, such as the Sichuan-Tibet Railway and hydropower developments, which has historically faced the threat of glacier-related debris flows. However, a robust assessment of such debris flow susceptibility is a challenge due to the complex and variable climate, terrain and glacial environment. In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regression, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accurately identify debris flow susceptible (DFS) areas. Topography, geology, and hydrological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of the study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accuracy of the DFS assessments. The CF-RF model showed the greatest improvement, with an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2nd and 9th in the factor importance, respectively. The results of this research could provide valuable assistance and guidance in mitigating glacier-related debris flow hazards.

Numerical Analysis of Terrain Variabilities and Their Impact on the Traction Performance of Agricultural Tire

This study uses numerical analysis to examine how terrain variability affects agricultural tire traction performance, utilizing ANSYS simulations and the Wong and Preston-Thomas tire model. Tractor tire performance was evaluated across ten different clay soils with varying mechanical properties. The ANSYS simulations modeled tire-soil interactions, and the Wong and Preston-Thomas model predicted traction performance based on stress data. Results showed notable variations in thrust, drawbar pull, and motion resistance, with certain soils delivering better traction. The study offers insights for optimizing tire design to improve vehicle productivity on diverse terrains. Keywords: Wong and Preston-Thomas tire model, numerical analysis, traction performance, ANSYS simulation, clay soil parameters, tire-soil interaction.

UAV path planning based on bird flock migration

Unmanned aerial vehicle, generally referred to as UAV, due to its high-performance and low-cost characteristics, it is particularly widely used in both military and civil applications, and is often used to perform a variety of complex missions. Trajectory planning is the basis for UAVs to realize autonomous flight, which ensures that UAVs avoid obstacles and threatening areas when performing tasks, and guarantees the safe execution of tasks. The operation of a UAV requires finding the safest and most efficient trajectory from a starting point to a specified end point based on several specific constraints. Current UAV trajectory planning algorithms, such as swarm algorithm, particle swarm algorithm, and ant colony algorithm, have been widely recognized, but still have many limitations when they are applied to complex and variable terrain. Therefore, this paper proposes an improved UAV trajectory planning algorithm based on simulated bird migration, and uses matlab for simulation and algorithm evaluation. The simulation results indicate that the improved search strategy is effective, the algorithm can find a better solution in each iteration, and the search strategy of the algorithm can effectively guide the algorithm towards a more optimized region. Overall, this algorithm has high research value in both military and civilian fields.

Investigation of the Relationship between Topographic and Forest Stand Characteristics Using Aerial Laser Scanning and Field Survey Data

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment.

Review of biodiversity assessment and resource management orientation for sustainable rural development in Lang Son province, Vietnam

This study examines the conservation and biodiversity management efforts in Lang Son province, Vietnam, in the context of global environmental concerns. Under Decision No 2447/QD-UBND, the province’s Special-use Forest Planning focuses on conserving areas such as the Huu Lien Nature Reserve and the Mau Son special-use forests. This initiative covers a substantial planned land area to sustain biodiversity and foster rural development up to 2030. Recent surveys indicate a notable increase in plant species within these areas, from 842 species in 2017 to 961 in 2019, and significant efforts towards conserving endangered species like the Asiatic black bear and the Annam flying frog. The province encompasses diverse ecosystems spread across 832,075.82 ha, characterized by varied terrains and climates contributing to its rich biodiversity. These regions are crucial for various species, including those listed in conservation documents, such as the 77 plant species recorded. RAP-Plant analysis and Monte Carlo simulation indicate that Lang Son’s biodiversity, with a Shannon index of 2.85 and Simpson index of 0.75, is threatened by a projected forest area decline to 514,578.61 ha by 2040, risking extinction for rare species like Hoang dan and Lan kim tuyen. Furthermore, Lang Son’s strategic initiatives towards sustainable agricultural practices, forest management, and restoration efforts highlight the integration of biodiversity conservation into broader socio-economic development plans. The province’s approach aligns with global strategies emphasizing the protection of endangered and endemic species as essential to maintaining ecological balances and supporting sustainable development. This study underscores the importance of comprehensive regional planning and community engagement in biodiversity conservation efforts to mitigate economic development, illegal activities, and climate change challenges. Threats such as illegal logging and habitat fragmentation persist, necessitating more vital community-based conservation efforts and robust legal frameworks.

Applications of GANs to Aid Target Detection in SAR Operations: A Systematic Literature Review

Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages.

MACHINE LEARNING ALGORITHM FOR RAPID OBJECT DETECTION BASED ON COLOR FEATURES

The identification of objects is of utmost importance in a wide range of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring. Accurate and efficient object recognition methods are crucial in pastoral environments, characterized by the prominent presence of cattle and other objects, to provide effective analysis and decision-making processes. The present study paper introduces an innovative methodology for efficient identification of objects in pastoral landscapes through the utilization of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM method integrates color characteristics with the ELM algorithm to attain enhanced object detection accuracy while preserving computational economy. The experimental findings provide empirical evidence supporting the efficacy and efficiency of the suggested approach in the detection of items within pastoral landscapes. In addition to the CF-ELM, an algorithm for desktop-based categorization of items within a pastoral environment is provided, with individual speeds ranging from 0.05 s to 0.17 s for a single image, evaluated in each color space. The algorithm is intended for usage in scenarios with challenging and variable terrain, making it appropriate for application in agricultural or pastoral settings.

Sustainability Constraints on Rural Road Infrastructure

Research on the sustainability of rural roads is of great significance to the integrated promotion of rural habitat improvement, the maintenance of regional ecological patterns, and the implementation of the rural revitalization strategy. This paper examines the constraints to ensuring the sustainability of road infrastructure in rural Shaanxi, China. Rural road infrastructure plays an important role in poverty alleviation. Rural Shaanxi Province is situated among a variety of terrains and spans a large area. Therefore, using the current situation of rural roads in the province as a starting point, the constraints and their rankings that impede the sustainability of road infrastructure are identified through the fuzzy Delphi method (FDM) and structural equation modeling (SEM), which in turn suggests governance measures that can be taken. The data in this paper come from 498 valid questionnaires obtained from 50 townships and 101 sample villages in Shaanxi Province. Due to the huge number of questionnaires, the questionnaires were tested with the help of SPSS 26.0 software, which showed that the questionnaires had high reliability and validity, and then the structural equation model was validated and modified according to the recommendations of goodness-of-fit statistics measurement and the modification index. Finally, the key factors affecting the sustainable development of rural road infrastructure were finally identified.

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Downscaling TRMM Monthly Precipitation in Cloudy and Rainy Regions and Analyzing Spatiotemporal Variations: A Case Study in the Dongting Lake Basin

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin.