Rugged Terrain Research Articles

Selection of den sites and chronology of denning by black bears in the eastern Sierra Nevada and western Great Basin.

Use of dens during winter is an important strategy for American black bears (Ursus americanus) for both energy conservation and reproduction; and occupancy of suitable den sites has implications for reproductive fitness. Denning strategies may change as a result of changing climatic conditions and habitat loss. Black bears occupy arid environments in the eastern Sierra Nevada and the western ranges of the Great Basin Ecosystem. Our objectives were to identify: (1) which physical characteristics of habitat influenced selection of den sites at multiple spatial scales and (2) which environmental factors influenced timing of entrance and exit of dens by females and males. We evaluated selection of den sites by black bears at three spatial scales (300, 1000, and 4000 m) from 2011 to 2022. Terrain ruggedness was important for selection of den sites at all spatial scales. Within a 300-m buffer from the den, bears selected den sites with rugged terrain, lower horizontal visibility, and greater canopy cover, resulting in more concealment and protection than that of the surrounding environment. Within 1000- and 4000-m buffers around each den, bears selected den sites with rugged terrain, northern aspects, and steep slopes. At the 4000-m scale, we observed interactions between sex with slope and distance to roads; females selected den sites on steeper slopes and closer to roads than did males. Females remained in the dens longer than males by entering earlier in the autumn and exiting later in the spring. Male bears exited their dens earlier with increasing consecutive days above freezing temperatures, but that relationship was weak for females. Knowing what characteristics are important for selection of den sites, and influence timing of denning, will be important for understanding how shifting climatic patterns will affect bears, particularly in arid environments that may be prone to wider fluctuations in climatic drivers of denning in the future.

Stereo visual-inertial localization algorithm for orchard robots based on point-line features

The vision-based localization of orchard mobile robots is an indispensable component of orchard intelligent management, applicable to scenarios such as navigation and harvesting. Due to the rugged terrain and lack of texture features in orchard environments, the accuracy of Visual-Inertial Odometry (VIO) based on feature point is compromised, making it challenging to meet practical application requirements. Addressing these issues, this paper proposes an algorithm for stereo visual-inertial localization based on point-line features, by enhancing VINS-Fusion. The algorithm incorporates the line feature detector algorithm (LSD) into the odometry front-end to detect line features, addressing the insufficient effective feature points and low-quality feature matching issues in orchard environments. Simultaneously, strategies for length filtering and feature matching are introduced for line features, eliminating poor-quality line features and matches caused by rugged terrain, thereby reducing adverse impacts on localization accuracy. To prevent keyframe redundancy due to a scarcity of stable feature points in areas with missing textures, such as tree trunks and leaves, a keyframe criteria optimization method based on point-line features is designed. Back-end optimization minimizes the cost function containing residuals of point-line features, prior information, and IMU, ultimately improving the localization accuracy of mobile robots. To validate the effectiveness and practicality of the proposed algorithm, a self-built dataset was created in real orchard environments, including scenes from banana plantation, mango orchard, and wampee orchard. Localization experiments were conducted using Rosario, EuRoC public datasets and self-built orchard dataset. Results indicate that the proposed algorithm consistently achieves lower ATE-RMSE than VINS-Fusion, with a maximum reduction of 79.2 % in the Rosario dataset sequence, 23.3 % in the EuRoC dataset sequence, and ATE-RMSE values of 0.091 m, 0.107 m, and 0.093 m in banana plantation, mango orchard, and wampee orchard, respectively. These represent reductions of 22.9 %, 7.6 %, and 27.3 % compared to VINS-Fusion. In the self-built orchard dataset, the average frame processing times for the algorithm front-end is 56.3 ms, while for the back-end is 31 ms, meeting the practical localization requirements. The proposed algorithm effectively enhances the localization accuracy of orchard mobile robots, offering a viable solution for autonomous navigation in orchard robot systems.

Influence of groundwater on micro-cracking behaviour and failure mechanism of deep hard rock

To address the water-inrush hazards encountered in deep tunnels in the rugged terrain of Southwest China, the uniaxial compression tests and acoustic emission (AE) monitoring were conducted on granite samples taken from a deep tunnel with different durations of saturation (2 days and 30 days), and the results were compared with those of natural granite samples. The results revealed the influence of groundwater on the mechanical properties, micro-cracking behavior, and failure modes of granite, which provided guidance for the deep tunnel construction. Based on the previous work, an optimized method for extracting the dominant frequency of rock AE signals considering the effect of groundwater on hard granite was proposed, which corrected the inaccurate extraction problem in the traditional method. The optimally extracted high dominant frequency AE signals could also be used as a precursor of rock failure.

Revealing functional responses in habitat selection of rocky features and rugged terrain by Eurasian lynx (Lynx lynx) using LiDAR data

ContextMany carnivores are attracted to rugged terrain, rocky areas, and conspicuous relief features. However, most of the previous research is limited to general topographical habitat characteristics and rarely consider the effects of microhabitat characteristics.ObjectivesWe used the Eurasian lynx (Lynx lynx) as a model species to investigate the effects of microhabitat characteristics and human infrastructure on habitat selection. We also tested whether there is evidence for a functional response in habitat selection across a large gradient of habitat availability.MethodsWe developed a new approach for detecting rocky outcrops from airborne LiDAR data. In combination with other remote sensing techniques and GPS-telemetry data, we assessed lynx habitat selection and functional responses across two geologically contrasting areas in Europe.ResultsWe detected > 1 million rocky outcrops and confirmed their strong selection by lynx. Lynx also selected steep, rugged, and rocky areas, especially for day-resting sites. Furthermore, lynx avoided paths during the day but selected them and other linear anthropogenic infrastructure during the night, indicating the behaviour-specific impact of human infrastructure. We also observed a functional response in the selection of rocky and rugged areas, as lynx’ selection of such habitats increased with their lower availability. This highlights the importance of preserving such terrains, especially when they are rare in a landscape.ConclusionsOur results highlight the importance of incorporating remote sensing techniques and data on microhabitat features in animal habitat selection research. We also recommend caution when developing new infrastructure for human recreation or promoting its use near geomorphological features and in rugged terrain.

GEOGRAPHIC DIVERSITY AND LANDSCAPE IN TRANSITION: ANALYZING THE PHYSICAL FEATURES OF GILGIT BALTISTAN REGION

This study presents multidimensional analysis of the Gilgit Baltistan region, focusing upon its geographical, environmental, and socio-economic aspects. Employing both qualitative and quantitative methodologies, the research defines the region's physical features, climatic conditions, and hydrological characteristics. It quantitatively assesses distribution of glaciers, rivers, and forests, utilizing statistical data to realize trends in hydrology and ecology. Qualitatively, study explores hazards and vulnerabilities inherent in region, including natural, man-made hazards, their intensity, and socio-economic implications. Also, it conducts a comparative analysis to examine strategic significance of mountain passes in facilitating the trade, travel and cultural exchange. Study reveals region's significant hydrological resources, with glaciers and snowmelt serving as principal water sources. Strategic status of mountain passes in facilitating trade and connectivity, with key passes like Khunjerab and Babusar serving as vital links between Pakistan to China, as well as connecting the region with other provinces of Pakistan. The analysis further reveals that among natural hazards the region is vulnerable to flash floods, landslides, and snow avalanches, intensified by its rugged terrain and climatic conditions.

Assessing satellite-derived wind speeds: insights from buoy measurements and analysis of deviation factors

ABSTRACT To evaluate the latest generation of satellite wind speed from AMSR2, ASCAT, GMI, SSMIS, SMAP, and WindSat, buoy measurements were obtained from buoys in coastal and tropical open ocean areas. Root mean square errors were calculated, resulting in values of 1.19 m/s for AMSR2, 1.25 m/s for ASCAT, 1.37 m/s for GMI, 1.38 m/s for SSMIS, 2.48 m/s for SMAP, and 1.18 m/s for WindSat. Numerous data pairs fall above the 45-degree line, mainly due to decreased buoy ac-curacy during high winds. Factors contributing to this include underestimation of buoy readings due to rough sea states and wind profile distortion. Satellite biases exhibit unimodal fluctuations over a one-year cycle, aligning with annual sea surface temperature variations. Statistical effects in extreme wind speed range cause abnormal fluctuations in statistics. Higher latitudes experi-ence larger deviations due to rough sea state. Ocean currents affect satellite wind speed observa-tions, particularly when aligned with wind direction. Coastal wind speed gradients primarily influenced from land contamination like rugged mountainous terrain along the West Coast of North America. Coastal upwelling also contributes to bias, with the west coast showing higher bias. A global comparison between SMAP and WindSat retrieval wind speeds demonstrates SMAP’s poor ability to monitor general wind speeds and validates the satellite-buoy experiment.

Comparison of machine learning and statistical approaches for Digital Elevation Model (DEM) correction: interim results

Abstract. The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests.

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

One size does not fit all: Differential predation on livestock species by leopards (Panthera pardus) in a multiuse landscape in the gangetic plains, India

There are underlying patterns involved in predation, particularly for an adaptable species like leopard. One of them is size of the prey. The selection of a prey by a carnivore is typically determined by the trade-off between energy spent and gained. Here size becomes even more consequential when conflict is involved, but most studies typically map all species as a single entity at a single spatial scale. This study aimed at understanding the multiscale nature of the conflict based on the size livestock attacked by leopards (large stock − cattle and small stock − goat and sheep). The methodology encompassed targeted sampling which included the collection of the GPS locations of the attack sites. Spatial layers of 14 environmental variables that represented three spatial scales i.e. proximate (cache-distance), local (daily-movement), and landscape (home-range) scale were then created through remotely sensed data. After that Generalized additive models were utilized for analysis, with ΔAIC used for model selection and evaluation was performed via AUC and TSS statistic. Our results indicate a spatial distinction in predation strategies. In the final model the statistically significant variables for large stock included a higher proportion of cover, and a lower proportion of settlements at the home-range scale, very low human population density at daily-movement scale, and NDVI representing moderately dense vegetation and terrain ruggedness index representing flat terrain at cache-distance scale. Similarly, for small stock the variables included a higher proportion of agricultural land and NDVI values representing open terrain. The effective degrees of freedom was higher than 2 for most of the variables, indicating non-linear relationships. We inferred that the leopards’ targeted large stock in more secluded dry deciduous forests farther from human settlements, where they may be found grazing alone. In contrast, smaller stock, being easier to catch, are killed more frequently in rugged terrain across a wider scrub landscape with scantier cover. Consequently, this selective selection based on size might have its basis in the perceived risk of being chased off by people and the reward in terms of the size of the acquired meal.

Identification of priority areas for the restoration of high Andean mountain forests under climate change: a case study using potential distribution models

Paleontological studies in the central Andes have uncovered the vulnerability of Polylepis forests to climate shifts, attributed to limited seed dispersal capabilities and the rugged terrain. Given the ecological significance of these forests‐forming species as biodiversity hotspots and carbon sinks, their restoration is imperative for the preservation of endemic species and ecosystem services. At the extreme south of the genus' range, we find Polylepis australis, a forest‐forming tree species endemic to Argentina and uniquely extending beyond the Andes into the Sierras Grandes of the Córdoba province. This study utilizes potential distribution models to help inform on restoration strategies considering projected future climate scenarios. Employing Maxent 3.4.3 with 453 presence locations, we estimated a present potential distribution spanning 13,923 km2. Our analysis of future climate projections reveals important poleward contractions alongside minor uphill shifts (20 m above sea level), ultimately resulting in a net habitat loss of 4596 km2 (34%). This underscores the critical role of climate simulations in identifying species specific priority sites for restoration efforts and also highlighting Polylepis forests susceptibility to both climate change and anthropogenic impacts.

Enhancing Communication Reliability: Designing Microwave Links for Bahir Dar-Woretta Connectivity

This paper explores the need for establishing a microwave link between Bahir Dar and Woretta as an alternative communication solution to the existing optical fiber infrastructure. Microwave links offer an effective way to overcome challenges posed by rugged terrains and unfavorable environmental conditions that hinder the deployment of fiber optics. As Woretta emerges as a key economic and investment hub within the Amhara Region, demand for reliable and efficient communication is expected to grow significantly. The study encompasses various aspects of planning and designing the microwave link, including site surveys, consideration of fade margins, frequency planning, link budget calculations, and assessing the feasibility and reliability of the proposed link. The paper employs LINKPlanner 5.4.1 software to simulate and validate the results. Due to terrain constraints, a direct link between Bahir Dar and Woretta is not feasible. Instead, a two-hop link is proposed, involving transmission from Bahir Dar to Zege, and then from Zege to Woretta. This alternative configuration ensures optimal connectivity while addressing the terrain limitations. By presenting a comprehensive analysis and simulation of the microwave link, this paper provides valuable insights into the planning and implementation of a robust communication infrastructure. The proposed microwave link will offer a reliable and efficient alternative to the existing optical fiber network, ensuring uninterrupted connectivity to support the region’s growth and development.

The Beamline Layout Design Based on the Mathematical Model for Different Water Terrain

Multi-beam sounding is one of the important components of marine surveying. For different water terrain, if the line layout scheme can be reasonably formulated according to the principle of multi-beam sounding, it can greatly improve the sounding efficiency and reduce the loss of manpower, material resources and time while ensuring the accuracy of the sounding results.Based on the different water terrain, the definition of overlap rate and the calculation method in simple cases are given first.For the water with flat bottom terrain, the main selection of isobath, control different spacing and Angle to meet the overlap rate requirements as much as possible.For waters with relatively rugged and changeable underwater terrain, the water area is first rationally divided into several sub-areas, in which the underwater depth and slope change little. Then, the plane fitting is carried out for each sub-area, and the wiring idea of flat underwater terrain is adopted for wiring. For two adjacent areas with similar water depth, the water area is divided into several sub-areas.The starting end of the survey line in the latter area should be made to coincide with the end end of the survey line in the previous area. For the calculation of the coincidence rate, the data points at each depth of the bottom are iterated several times to determine whether they are in the overlapping part of the measurement area of the two survey lines, so as to arrange the multi-beam survey line as far as possible to meet the corresponding requirements.

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

Retrieval of At-Surface Upwelling Radiance and Albedo by Parameterizing Cloud Scattering and Transmittance over Rugged Terrain

Accurate and continuous estimation of surface albedo is vital for assessing and understanding land–surface–atmosphere interactions. We developed a method for estimating instantaneous all-sky at-surface shortwave upwelling radiance and albedo over the Tibetan Plateau. The method accounts for the complex interplay of topography and atmospheric interactions and aims to mitigate the occurrence of data gaps. Employing an RTLSR-kernel-driven model, we retrieved surface shortwave albedo with a 1 km resolution, incorporating direct, isotropic diffuse; circumsolar diffuse; and surrounding terrain irradiance into the all-sky solar surface irradiance. The at-surface upwelling radiance and surface shortwave albedo estimates were in satisfactory agreement with ground observations at four stations in the Tibetan Plateau, with RMSE values of 56.5 W/m2 and 0.0422, 67.6 W/m2 and 0.0545, 98.6 W/m2 and 0.0992, and 78.0 98.6 W/m2 and 0.639. This comparison indicated an improved accuracy of at-surface upwelling radiance and surface albedo and significantly reduced data gaps. Valid observations increased substantially in comparison to the MCD43A2 data product, with the new method achieving an increase ranging from 40% to 200% at the four stations. Our study demonstrates that by integrating terrain, cloud properties, and radiative transfer modeling, the accuracy and completeness of retrieved surface albedo and radiance in complex terrains can be effectively improved.

Assessment of Suitable Habitat of the Demoiselle Crane (Anthropoides virgo) in the Wake of Climate Change: A Study of Its Wintering Refugees in Pakistan.

The inevitable impacts of climate change have reverberated across ecosystems and caused substantial global biodiversity loss. Climate-induced habitat loss has contributed to range shifts at both species and community levels. Given the importance of identifying suitable habitats for at-risk species, it is imperative to assess potential current and future distributions, and to understand influential environmental factors. Like many species, the Demoiselle crane is not immune to climatic pressures. Khyber Pakhtunkhwa and Balochistan provinces in Pakistan are known wintering grounds for this species. Given that Pakistan is among the top five countries facing devastating effects of climate change, this study sought to conduct species distribution modeling under climate change using data collected during 4 years of field surveys. We developed a Maximum Entropy distribution model to predict the current and projected future distribution of the species across the study area. Future habitat projections for 2050 and 2070 were carried out using two representative concentration pathways (RCP 4.5 and RCP 8.5) under three global circulation models, including HADGEM2-AO, BCC-CSM1-1, and CCSM4. The most influential factors shaping Demoiselle Crane habitat suitability included the temperature seasonality, annual mean temperature, terrain ruggedness index, and human population density, all of which contributed significantly to the suitability (81.3%). The model identified 35% of the study area as moderately suitable (134,068 km2) and highly suitable (27,911 km2) habitat for the species under current climatic conditions. Under changing climate scenarios, our model predicted a major loss of the species' current suitable habitat, with shrinkage and shift towards western-central areas along the Pakistan-Afghanistan boarder. The RCP 8.5, which is the extreme climate change scenario, portrays particularly severe consequences, with habitat losses reaching 65% in 2050 and 85% in 2070. This comprehensive study provides useful insights into the Demoiselle Crane habitat's current and future dynamics in Pakistan.

Space/ground-borne techniques and petrographic microscopic dissection for geologic mapping in Gabal Ras Abda area, Northeastern Desert, Egypt

BackgroundGabal Ras Abda area as a part of the Red Sea Mountain range, is characterized by inaccessible and rugged terrains. The exposed rock units are hardly followed in the field because of the rigid topography. Thus, the present work proposes and develops an integrated approach to map the exposed rock units and extract the geologic structures using satellite imagery data followed by both field and petrographic verification, saving time, efforts and cost.ResultsTo achieve the target, both the measured spectral signature curves with Landsat-8 and Sentinel-2A data were used to develop and create the most enhanced Band Ratios and Principal Components for lithological discrimination and mapping which were (((Band7 + Band 4)/(Band 7), (Band 2)/(Band 2 + Band 5) and (Band 5) in RGB) and ((Band 9 + Band 11 + Band 12)/(Band 1), (Band 4 − Band 2) and (Band 11/Band 6) + (Band 6) in RGB)) with Principal Component Bands ((PC1, PC2 and PC3 in RGB) and (PC3, PC2 and PC1 in RGB)), respectively. Also, georeferenced Google Earth Pro, panchromatic band of Landsat-8 and ALOS PALSAR Digital Elevation Model images were used to extract the structural lineaments. Geologic, petrographic and field structural studies were emphasized the remote sensing results, indicating that the main rock types cropped out in Ras Abda area from the oldest to the youngest are older granitoids (quartz-diorites, tonalites and granodiorites), Dokhan volcanics (andesites, rhyodacites, rhyolites and their related tuffs), younger gabbros, younger granites (monzogranites, syenogranites and alkali-feldspar granites), post-granite dykes and offshoots (acidic, microgranitic and basic types) and Phanerozoic sedimentary rocks. Also, the study emphasized that the E-W trend is the main structural trend controlling the investigated area followed by WNW-ESE and NE-SW directions.ConclusionsThe results of remote sensing achieved compliance with the geologic, petrographic and structural investigation through distinctly differentiating the different rocks and extracting the lineaments, indicating the accuracy of the remote sensing results and emphasizing their importance and effective role in producing a precise and highly accurate geologic map.Graphical abstract

Large-scale overlap and fine-scale avoidance: assessing interactions between coyotes, bobcats, and cougars at multiple scales

Abstract In multi-carnivore systems individuals must forage and reproduce while also competing with other carnivores and avoiding intraguild predation. These interactions may vary by strength and scales across different ecosystems. We used occupancy analyses and attraction–avoidance indices to assess large- and fine-scale interactions, respectively, between Cougars (Puma concolor), Bobcats (Lynx rufus), and Coyotes (Canis latrans) in northeast Oregon based on data from camera traps set during the summer and fall of 2016 and 2017. To determine the importance of habitat preferences and spatial overlap, we compared occupancy (probability of use) models that included habitat covariates with and without species co-occurrence terms. Detection was different for all species and was influenced by roads, game trails, and canopy cover. Terrain ruggedness was important for all species; Bobcats showed the strongest preference for ruggedness, Cougars showed a weak preference, and Coyotes significantly but weakly avoided rugged areas. The top probability of use model showed that Bobcats and Coyotes were 1.88 times more likely to spatially overlap on roads. We also found evidence that interactions among carnivores were scale-dependent. At larger scales, habitat preference was more important than interactions with other carnivores, whereas at finer scales, most carnivores avoided each other. At fine spatiotemporal scales, our attraction–avoidance analyses showed that Bobcats avoided Coyotes and Coyotes avoided both Bobcats and Cougars. These findings suggest that carnivores in our study system were adept at minimizing conflict through fine-scale avoidance in space and time, that the human footprint can influence carnivore interactions, and that studying carnivore interactions at multiple scales is important for understanding the effects of spatial overlap and potential competition.

US County-Level Variation in Availability and Prevalence of Black Physicians in 1906

Black physicians are substantially underrepresented in the US health care workforce, with detrimental effects on the health and health care experiences of Black individuals. These contemporary gaps can be traced to the early days of the medical profession using the first edition of the American Medical Directory (AMD). To identify state- and county-level patterns related to the training and availability of Black physicians relative to their White counterparts in the 1906 AMD. For this cross-sectional study, data for 41 828 physician entries in 18 US states in or adjacent to the South as well as the District of Columbia were extracted from the 1906 AMD and aggregated to 1570 counties. Data analysis was performed between September 2023 and January 2024. County-level exposure variables included population density, racial composition, and illiteracy rate among US-born White residents as well as an index of terrain ruggedness and the number of lynchings in the previous decade. Median values of physicians' distance from place of practice to place of medical training (by race of physician) were also used as an exposure variable. There were 4 county-level outcomes: (1) presence of any Black physician, (2) proportion of Black physicians per Black population, (3) proportion of White physicians per White population, and (4) community representativeness (reported as the community representativeness ratio). The cross-sectional analysis used generalized additive mixed models with state-level random effects. Across 1570 counties, Black physicians comprised 746 (1.8%) of the 41 828 physicians in the dataset. Black physicians tended to train further from their place of practice than their White counterparts. The proportion of Black physicians per 1000 Black residents was 0.08 compared with 1.62 for White physicians; these proportions varied substantially by state. At the county level, the presence of any Black physician was associated with percentage Black population (odds ratio [OR], 28.94 [95% CI, 9.77 to 85.76]; P ≤ .001), population density (OR, 2.63 [95% CI, 2.03 to 3.40]; P ≤ .001), and distance to the nearest Black medical school (OR, 0.62 [95% CI, 0.42 to 0.92]; P = .02). A variety of structural disadvantages are illustrated in this cross-sectional study of county-level sociodemographic and geographic characteristics associated with the prevalence of Black physicians in the earliest days of the profession. To demonstrate its broader utility for health disparities research, the dataset has been made publicly available with a visualization platform.

Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.