Flat Terrain Research Articles

Snow Avalanche Hazards and Avalanche-Prone Area Mapping in Tibet

Snow avalanche is one of the major natural hazards in the mountain region, yet it has received less attention compared to other mountain hazards, such as landslides, floods, and droughts. After a comprehensive overview of snow avalanche hazards in Tibet area, the spatial distribution and main driving factors of snow avalanche hazards in the high mountain region in Tibet were presented in the study first. Snow avalanche-prone areas in Tibet were then mapped based on the snow cover distribution and DEM data and were validated against in situ observations. Results show that there are the highest frequencies of avalanche occurrences in the southeastern Nyainqentanglha Mountains and the southern slope of the Himalayas. In the interior of plateau, avalanche development is constrained due to less precipitation and much flatter terrain. The perennially snow avalanche-prone areas in Tibet account for 1.6% of the total area of the plateau, while it reaches 2.9% and 4.9% of the total area of Tibet in winter and spring, respectively. Snow avalanche hazards and fatalities appear to be increasing trends under global climate warming due to more human activities at higher altitudes. In addition to the continuous implementation of engineering prevention and control measures in pivotal regions in southeastern Tibet, such as in the Sichuan–Tibet highway and railway sections, enhancing monitoring, early warning, and forecasting services are crucial to prevent and mitigate avalanche hazards in the Tibetan high mountain regions, which has significant implications for other global high mountain areas.

Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation

Clouds and aerosols, as important factors in the Earth’s climate system, have significant impacts on the atmospheric environment and global climate. This study investigated the optical and physical properties of clouds and aerosols over South America from 2006 to 2021 using CALIPSO Level 2 products. South America was divided into four regions: A (Western Andean Mountains), B (Northern Orinoco and Amazon plains), C (Southern La Plata Plains), and D (Eastern Brazilian Highlands). Seasonal variations in the optical properties of low clouds and their interactions with the lowest-layer aerosols were analyzed and compared. The results indicate that Region C had the highest OPlc (probability of low clouds) and AODlc (AOD (Aerosol Optical Depth) of low clouds, likely due to its flat terrain and westerly influences. Both AODlc and OPlc were higher in September–November compared to other seasons. DRlc (depolarization ratio of low clouds) values were higher in Regions C and D, particularly in September–February, possibly due to topographic effects and more precipitation and higher humidity during this period. The elevated CRlc (color ratio of low clouds) in Region A may be attributed to the Andes blocking warm, moist air, leading to increased precipitation and cloud particle content. HLlc (top height of low clouds) and BLlc (base altitude of low clouds) were positively correlated with geographic elevation, while Tlc (thickness of low clouds) was greater at night, potentially due to enhanced atmospheric stability. Furthermore, strong correlations among certain parameters suggested significant interactions between aerosols and clouds.

Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models

Abstract. Distributed energy and mass balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well-observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet-snow maps could potentially help refine the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction (SCF) pertains predominantly to the ablation season. In this work, wet-snow maps derived from Sentinel-1 and SCF retrieval from Sentinel-2 are compared against model results from a fully distributed energy balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet-snow line (WSL) to compare Sentinel-1 wet-snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals a delayed snowmelt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed for the achievement of earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow-line (SL) observations were also substantially reduced. These results suggest that wet-snow maps contain valuable real-time information for snowpack models, complementing flat-field snow depth observations well, particularly in complex terrain and at higher elevations. The persisting correlation between wet-snow-line and snow-line biases provides insights into refined development, tuning, and data assimilation methodologies for operational snow-hydrological modelling.

Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters

This study aimed to identify key biomechanical and physiological parameters affecting cross-country skiing performance and develop a neural network model for predicting skiing speed. Biomechanical attributes (cycle length and rate, vertical displacement of the center of mass, and angular kinematics) and physiological factors (maximal oxygen uptake, 30 s anaerobic power), along with physical fitness (standing long jump, pull-ups) were assessed for 82 cross-country skiers (52 men and 30 women). Random forest analysis was utilized to identify the most influential parameters on skiing speed, which were subsequently used as input parameters to develop a neural network aimed at predicting this speed. The findings identified the primary predictors of skiing speed as the cycle length on both flat and uphill terrains, vertical displacement of the center of mass during the poling phase on uphill terrain, maximal oxygen uptake, and 30 s anaerobic power. The developed neural network model demonstrated high precision in predicting skiing speeds, evidenced by a strong correlation with actual speeds (correlation coefficient of 0.953) and 97.1% of predictions falling within the 95% Bland–Altman agreement limits, affirming the model’s reliability and effectiveness in forecasting skiing performance.

Study of Orthophotos Spatial Quality from Integrated Physical Data Collection in Samarinda City

Abstract Integrated physical data collection as a mapping instrument and an up-to-date and comprehensive base map have been carried out on a massive scale by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency throughout Indonesia. There are facts in the field that the results of orthophoto products still have a spatial quality that could be more optimal. In Samarinda City, this activity’s subdistrict was indicated to have a geometric accuracy of above 0.5 m. This condition does not meet the accuracy tolerances stated in the Service Level Agreement (SLA) or relevant regulations. Therefore, alternative solutions are needed to improve the quality of orthophotos by paying attention to economic value. This research aims to evaluate, and improve the spatial quality of orthophotos resulting from integrated physical data collection in 2023 in Samarinda City without re-acquisition. In other words, this research aims to determine alternative low-cost and efficient solutions to improve orthophotos’ spatial data quality. Data in the form of orthophotos and a 2023 Ground Control Point (GCP) coordinates list in Samarinda City. The approach method is the addition of GCP in the reprocessing of orthophotos. The study area is hilly and flat terrain. Results analysis uses geometric accuracy (horizontal and vertical) and semantic accuracy tests. The geometric accuracy test results before reprocessing had a CE90 value of 0.438 m based on the SLA attachment. However, they exceeded the CE90 threshold of ≥ 0.5 m based on the results of field suppletion measurements. The results of the semantic accuracy test have a National Imagery Interpretability Rating Scale (NIIRS) level 6, which means that orthophotos can make it easier to interpret geographical objects in detail. reprocessing with the addition of GCP can increase geometric accuracy with a CE90 value of 0.240 m and LE90 1.01 m. The spatial quality of orthophotos before and after reprocessing has a relatively significant difference. Adding GCP followed by reprocessing can be used as an alternative solution to improve the quality of orthophotos at a low cost.

Artificial rabbits optimization–based motion balance system for the impact recovery of a bipedal robot

Research on the control of bipedal robots has predominantly focused on ensuring stability and balance during locomotion, often neglecting the robot’s ability to respond to unexpected external disturbances. In the present study, an algorithm is proposed to enable humanoid robots to maintain balance when they experience external impacts. In evaluation experiments, a robot was placed on flat surfaces and sloped terrain, where it experienced impacts from five angles. To evaluate the robot’s stability, data were collected before, during, and after each impact. The study utilized the artificial rabbits optimization (ARO) algorithm to optimize parameters and trained the robot’s control model by using a five-layer multilayer perceptron (MLP) neural network. Notably, the joint use of ARO and MLP yielded computational savings relative to conventional reinforcement learning methods. The proposed hybrid approach allowed the robot to adapt quickly to external forces and maintain balance effectively. The findings of this research hold considerable promise for enhancing the practical applications of bipedal robots in real-world scenarios, where unpredictable forces or impacts are common. By improving a robot’s ability to react dynamically and maintain balance, the proposed method enables humanoid robots to operate in highly challenging and dynamic environments, such as those associated with disaster response, industrial tasks, or everyday human interaction, without falling because of unexpected disturbances. Thus, the present study contributes to the field of humanoid robotics by addressing real-world challenges and providing a robust solution for impact resistance.

Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach

This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing literature by detailing the dynamics of injury severity in single-vehicle large truck crashes but also announces the utility of partially temporally constrained models in enhancing traffic safety management strategies.

Gait planning of a 4–5R rolling mechanism based on the planar 6R single-loop chain

Abstract. This paper proposes a 4–5R rolling mechanism based on the spatial extension design of a planar 6R single-loop chain. By analyzing the locomotion of the planar equivalent form, a modular gait theory integrating different modes of gait with high efficiency, low energy consumption, and high speed is established. A unified kinematic strategy expression, encapsulated in the form of the gait period table, is tailored for the kinematic chain's gait on the flat terrain. A contrast gait is conducted to ascertain its velocity parameters and volatility of the center of mass (CM). By optimizing the corresponding indicators, two distinct gait patterns are achieved: a faster speed gait that prioritizes increased speed and a steady gait that emphasizes stability with reduced CM volatility. Drawing from the mobility analysis and simulation outcomes of the planar 6R single-loop kinematic chain, a theory of locomotion for a closed-chain linkage mechanism in space is proposed. A locomotion strategy on the flat ground is derived, and a unified evaluation index is proposed. Finally, the feasibility of the two working modes is verified using a physical prototype. The theoretical work in this paper simplifies the design process of closed-chain linkage robots and improves the mobility performance of closed-chain linkage robots. It lays the foundation for researching new types of closed-chain linkage robots.

Prehistoric Cultural Migration in the Middle–Lower Lishui Catchment of Central China in Response to Environmental Changes

Climate change and geological shifts were pivotal in the survival and development of ancient human societies, especially in densely populated regions like the middle and lower Lishui River Basin. This study explored the dynamic interactions between ancient human cultures and the region’s natural environment, using field research alongside geological, geomorphological, and archaeological data spanning from the late Paleolithic to the Neolithic periods. Our findings showed that prehistoric sites in the middle and lower Lishui River Basin were primarily located in the low hilly areas surrounding the lower basin during the Paleolithic era, a pattern shaped by the region’s geomorphology. Early human settlements were strategically positioned near the Lishui River, offering access to vital resources while minimizing flood risk. These locations provided flat terrain, abundant vegetation, and materials for tool-making, factors that supported a hunting and gathering lifestyle. As the coldest phase of the last glacial period approached, the transition from the Paleolithic to the Neolithic eras marked a period of significant behavioral adaptation. In response to the harsher environment, humans began settling on lower terraces and miniaturizing their stone tools, signaling a shift to more specialized hunting techniques. This adaptability and resilience marked the refinement of hunting economies during this period. With the onset of the Holocene epoch and a warmer climate, conditions for human habitation became more favorable. Societies began migrating from the hills to the fertile lower Lishui River Basin, heralding the Neolithic period. This era saw the emergence of settlements and the onset of early rice cultivation, marking the transition from a hunting–gathering economy to one centered on agriculture. By the Daxi period, these settlements had expanded, extending their influence throughout the region. In conclusion, this study underscores the critical roles of climate change and geological features in shaping human settlement patterns, economic activities, and cultural evolution in the middle and lower Lishui River Basin. Our findings offer valuable insights into the evolutionary processes of ancient human cultures in the region and provide a foundation to understand future challenges in sustainable development.

CHIMBO Air Quality Modeling System: Verification and Processes Analysis

This study presents an evaluation of the CHIMBO modeling chain applied to the Italian domain, specifically focusing on the Po Valley subdomain over the one-year period of 2019. The comparison between simulated and observed data indicates that the performance of the CHIMBO model aligns well with existing literature on other state-of-the-art models. The results demonstrate that the CHIMBO chain is particularly effective for regional-scale quantitative assessments of pollutant distribution, comparable to that of CAMS ensemble models. The analysis of key chemical species in particulate matter reveals that the CHIMBO model accurately represents the average concentrations of organic and elemental carbon, as well as secondary inorganic compounds (sulfate, nitrate, and ammonium), particularly at background monitoring stations in the flat terrain of the Po Valley, with the exception of Aosta, a city located at about 500 m asl. However, seasonal discrepancies were identified, especially during winter months, when significant underestimations were observed for several species, including elemental and organic carbon, predominantly at background sites. These underestimations are likely attributed to various factors: (i) inadequate estimations of primary emissions, particularly from domestic heating; (ii) the limited effectiveness of secondary formation processes under winter conditions characterized by low photochemical activity and high humidity; and (iii) excessive dilution of pollutants during calm wind conditions due to overestimation of wind intensity. In conclusion, while the CHIMBO modeling chain serves as a robust tool for mesoscale atmospheric composition investigations, limitations persist related to emissions inventories and meteorological parameters, which remain critical drivers of atmospheric processes.

Experimental Analysis of Long-Term Fuel Trim Performance in a Gasoline Engine Under Various Operating Conditions

This paper analyzes the effect of operating conditions on the enrichment of the fuel-air mixture in a car engine due to a decrease in air mass with increasing altitude above sea level. This study focuses on fuel trim, particularly long-term fuel trim, in optimizing the composition of the fuel-air mixture before it enters the engine's combustion chamber. Experimental studies were conducted on the Bishkek–Osh highway in the Kyrgyz Republic, a road characterized by flat, mountainous, and high-altitude sections with complex terrain. Based on the research objectives, experimental results were obtained, and graphs of long-term fuel trim as a percentage of terrain altitude were constructed and analyzed for flat, mountainous, and high-altitude sections of the Bishkek–Osh highway. The study established that fuel trim indicators can be used to analyze changes in the air-fuel mixture's air excess coefficient (λ = 1, stoichiometric ratio). The reduction in long-term fuel trim was observed in flat, mountainous, and high-altitude sections as air density and pressure decreased with increasing altitude above sea level. The results show that with a reduction in long-term fuel trim to -6.25% to -7.81% at altitudes between 1500 and 3000 meters above sea level, the air excess coefficient (λ) drops to 0.92–0.94. At an altitude of 770 meters (flat terrain), λ was measured at 0.97. The difference in λ (0.03–0.05) indicates that an increase in altitude significantly affects the air-fuel ratio (AFR), impacting mixture formation during vehicle operation in mountainous and high-altitude conditions.

Verification of the Wind Resource Data Contained in the NASA SSE Database for a Flat Terrain in the Territory of Russia’s United Power System of South

Verification of the Wind Resource Data Contained in the NASA SSE Database for a Flat Terrain in the Territory of Russia’s United Power System of South

Traction Assistance of a Forwarder in Flat Terrain

Traction assistance of forest machines via traction aid winches has gained widespread application in steep-terrain forest operations as it can mitigate soil disturbance by reducing wheel or track slip of the assisted machine. Since slip affects machine operations in flat terrain as well, especially on fine-grained and moist soils, the effectiveness of traction assistance under such conditions was evaluated. At a forest site, a forwarder with a total mass of 28.6 t was driven over two plots in 15 passes. The machine travelled unassisted over one plot, while on the second plot traction assistance was manually adjusted to keep slip close to 0%. Wheel slip and winch tractive force were recorded during each pass, and rut depth was measured after each pass. Soil density was measured pre-impact and at three times after different traffic increments. Although the mean wheel slip was low even during unassisted travel, traction assistance was found to cause a significant reduction. While both a decrease in rut depth and soil compaction were observed with traction assistance, only the latter was significant after three machine passes. A potential influence of inhomogeneous soil reinforcement due to roots suggests repeating the experiment on a more homogenous soil.

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.

Enhancing postural stability in musculoskeletal quadrupedal locomotion through tension feedback for CPG-based controller

A central pattern generator (CPG)-based controller enhances the adaptability of quadrupedal locomotion, for example, by controlling the trunk posture. The conventional CPG-based controllers with attitude control often utilized the posture angle as feedback information. However, if the robot's body is as soft as a musculoskeletal structure, it can detect the over-tilting of the trunk based on proprioceptive information of the muscles. In general, proprioceptive information such as muscle tension changes more rapidly than posture angle information. Therefore, a feedback loop based on proprioceptive information has great potential to respond to sudden disturbances that occur during locomotion over uneven terrain. In this research, we proposed a CPG-based controller utilizing the tension of soft pneumatic artificial muscles (PAMs). Musculoskeletal quadruped robots driven by PAMs are so soft, which prevents over-tilting of the trunk because the soft leg acts like a suspension. In addition, tension, one of the proprioceptive information of PAMs, exhibits high sensitivity to changes in trunk posture because the soft body's motion easily is affected by over-tilting of the trunk. To validate the efficacy of the proposed controller, we conducted numerical simulations with a simple quadruped model and experiments with a musculoskeletal quadruped robot. As a result, the tension feedback is not effective for posture stabilization on flat terrain whereas it is effective on uneven terrain. Moreover, the tension feedback improved the running velocity over uneven terrain. These results will enhance the locomotion capability of musculoskeletal quadruped robots, advancing their practical application.

Design and analysis of a multi-motion self-balancing lower limb exoskeleton based on flat terrain

In this study, a multi-motion self-balancing lower limb exoskeleton is designed to provide crutches-free rehabilitation training for quadriplegic patients on flat terrain without hand support. First, based on the biomechanical characteristics of the human body and the bionic mechanism configuration of the human lower limb, an exoskeleton with a full-drive series-parallel hybrid structure was established. Secondly, the D-H method, geometric method and Newton iteration method were used to establish the forward and reverse kinematic solutions of the exoskeleton. In addition, the static self-balancing principle was used to complete the planning of the multi-motion self-balancing trajectory. Finally, in order to verify the effectiveness of the proposed method, some numerical simulations were performed in this paper. The theoretical trajectories of the waist, ankle joint and foot center were compared with the simulated trajectories. The research results show that the forward and reverse kinematics model of the exoskeleton is feasible. Under the condition of contact force parameters, the exoskeleton has the ability of multi-motion self-balancing walking on flat terrain.

Natural or man-made disaster? Lessons from the extreme rain and flood disaster in Zhengzhou, China on "2021.7.20"

Driven by extreme weather events, precipitation-induced flood disasters are occurring more frequently, posing substantial threats to both economic development and public safety. Increased human production activities have further intensified the impact of flood disasters. However, understanding of the factors influencing flood disasters and their associated risk assessments remains limited. To better formulate flood prevention and disaster reduction strategies suited to local conditions, this paper takes the "7.20" extreme rainfall event in the main urban area of Zhengzhou, Henan Province, China, on July 20, 2021, as a case study and uses geographic detectors to incorporate human activities into the quantitative analysis of influencing factor contributions. Based on the three aspects of disaster incubation, disaster causation, and disaster bearing, this study proposes using the influence values obtained from geographic detectors as objective weight assignments for risk assessment, thereby conducting a locally adapted risk assessment of Zhengzhou's main urban area. Research results indicate that the "7.20" heavy rainstorm in Zhengzhou's main urban area involved a superposition of natural and human factors. Impervious surfaces had the highest explanatory power, followed by population density, confirming that this flood disaster arose from the interaction of multiple factors. The flood disaster risk assessment for Zhengzhou's main urban area shows higher risk levels in the central area and relatively lower levels in peripheral areas. The central area features flat terrain, with high population density and a large proportion of impervious surfaces. Among these, 22 subdistricts are located in extremely high-risk flood zones, while 6 subdistricts, classified as low-risk, account for over 60% of the low-risk area, making them relatively lower-risk communities. Comparison with flood waterlogging point data verifies the accuracy of the risk assessment through statistical analysis, allowing for further analysis of the spatial differentiation of flood risk. The findings of this study offer valuable insights for preventing and managing urban extreme rainfall disasters.

Cooperative Object Transport Via Non-Contact Prehensile Pushing by Magnetic Forces

Abstract Cooperative robot systems are an essential candidate for object transportation solutions. They offer cost-efficient and flexible operation for various types of robotic tasks. The benefits of cooperative robot systems have triggered the improvement of the object transportation field. In this study, a new way of transporting objects by cooperative robots is presented. The proposed method is performed by the pushing action of the magnetic forces of the robots. The permanent magnets mounted on the mobile robots and the cart create this repelling force. The rectangular object carrier cart equipped with passive caster wheels can be manipulated on flat terrains easily and be assigned to carry different shapes of objects. Using a carrier cart has the advantage of eliminating the vertical loads on the robots. Controlling a non-contact pushing method offers a low computational burden since simple velocity and position updates are adequate for operation management. Compared with the other methods of object transportation systems, the non-contact pushing method provides a faster operation with less sensitivity to control errors. Both simulations and real-world experiments are conducted and the performances are given comparatively with a generalized frictional contact object-pushing method. The results show that the proposed method provides 10.48% faster and 20.03% more accurate object transportation compared to the frictional contact method. It is envisioned that the presented method can be a promising candidate for object transportation tasks in the industry.

Assessing watershed vulnerability to erosion and sedimentation: Integrating DEM and LULC data in Guyana's diverse landscapes

This study assesses watershed vulnerability to erosion and sedimentation in Guyana using Digital Elevation Model (DEM) and Land Use/Land Cover (LULC) classification. It aims to assess erosion risk by examining rainfall erosivity, soil types, and spatial variability. The integration of DEM-derived slope data with LULC and rainfall erosivity shows a strong correlation between high rainfall (20,809.02–31,262.35 mm) and erosivity (21,433.29–32,200.22 MJ mm ha−1 h−1 yr−1), indicating increased erosion potential, particularly in southwestern regions. Soil analysis reveals Plinthic Acrisols, with high clay content, are more prone to erosion, whereas Orthic Ferralsols are less vulnerable. Slope analysis suggests flat terrain (89.16 %) has low erosion risk, but steeper slopes require interventions. Conservation strategies like reforestation, sustainable agriculture, contour plowing, and terracing are recommended to reduce erosion impacts. This integrated assessment provides insights for prioritizing soil conservation and adaptive management to address erosion risks in Guyana.

Research on the response of different flight modes under concave and convex terrain by Semi-Airborne Transient Electromagnetic Method (SATEM)

With the development and utilization of various mineral resources in horizontal terrain areas gradually becoming saturated, the study of transient electromagnetic method has gradually shifted from flat terrain areas to complex fluctuating and high altitude areas. Due to the characteristics of transmitting source on the ground and receiving point in the air, the Semi-Airborne transient electromagnetic method (SATEM) has advantages such as superior working efficiency, large exploration depth and higher signal-to-noise ratio of collected signals. At present, there are few researches on the influence of the flight mode of the UAV on the collected data. In this paper, the concave terrain and convex terrain are described by using unstructured tetrahedral mesh, and the response of terrain model is calculated. Then the terrain response under different flight modes has been researched and analyzed systematically. The corresponding relative error graph is obtained and compared to summarize the law. Through simulation with different influence parameters, it is found that when the flight mode of the UAV is the same altitude, the SATEM response is less affected by the fluctuating terrain, and when the flight mode of the UAV is along the terrain, the SATEM response is more affected by the fluctuating terrain. The study of the response of UAV to the undulating terrain under different flight modes can provide a reference for the processing and interpretation of SATEM data in the undulating terrain region.