Search Results (161)

When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work. Full article

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation. Full article

Active travel modes, such as walking and cycling, are essential for fostering sustainable urban transportation. However, their adoption in environmentally challenging areas—characterised by steep slopes, extreme weather, and rugged terrain—presents significant obstacles. This study addresses these challenges by conducting a systematic literature review of studies published between 2000 and 2024 to identify strategies that promote active travel in such contexts. Using a structured five-step methodology, 62 relevant articles were selected and analysed to explore common challenges and propose tailored solutions. The findings highlight critical barriers, including topographical difficulties, harsh climatic conditions, and adverse weather, all of which hinder walking and cycling. To address these barriers, this study identifies a range of solutions, including infrastructure enhancements such as bike lifts, e-bike systems, shaded walkways, and heated pavements, as well as policy measures like financial incentives and disincentive regulations. Importantly, this study makes a deliberate effort to avoid overgeneralised solutions by emphasising the need for interventions that are context-sensitive and tailored to specific environmental challenges, urban scales, and local conditions. By providing options for actionable strategies, this research offers a comprehensive foundation for developing inclusive and sustainable policies that encourage active travel in diverse and environmentally constrained urban settings. Full article

The Daguangbao landslide (DGBL), triggered by the 2008 Wenchuan earthquake, is a rare instance of super-giant landslides globally. The post-earthquake evolution of the DGBL has garnered significant attention in recent years; however, its deformation patterns remain poorly characterized owing to the complex local topography. In this study, we present the first observations of the surface dynamics of DGBL by integrating satellite- and ground-based InSAR data complemented by kinematic interpretation using a LiDAR-derived Digital Surface Model (DSM). The results indicate that the maximum line-of-sight (LOS) displacement velocity obtained from satellite InSAR is approximately 80.9 mm/year between 1 January 2021, and 30 December 2023, with downslope displacement velocities ranging from −60.5 mm/year to 69.5 mm/year. Ground-based SAR (GB-SAR) enhances satellite observations by detecting localized apparent deformation at the rear edge of the landslide, with LOS displacement velocities reaching up to 1.5 mm/h. Our analysis suggests that steep and rugged terrain, combined with fragile and densely jointed lithology, are the primary factors contributing to the ongoing deformation of the landslide. The findings of this study demonstrate the effectiveness of combining satellite- and ground-based InSAR systems, highlighting their complementary role in interpreting complex landslide deformations. Full article

Qing-Chengzi (QCZ) is an important silver-gold mining area in the eastern part of the Northeast China Craton. The shallow minerals in this area are almost completely depleted, leading to a demand for exploration to find deeper, concealed deposits. However, due to the rugged terrain, few high-precision ground surveys have been executed in this area, resulting in an insufficient understanding of the unexposed ores. To address this issue, this study implemented a high-precision ground magnetic survey to identify faults and potential rocks in this area. To achieve these goals, remanence was analyzed to reduce its adverse effect on processing. Then, lineament enhancement with directional derivatives was conducted on the pre-processed magnetic anomalies to highlight structural features. Based on the results, eight major and twenty-one minor faults were identified, among which three major faults correspond well to the known faults. Most of the major faults run N–S, and the others run NW/NE. Furthermore, 3D inversion was conducted to locate potential rocks. Our inversion results indicate that there are six hidden rocks in the underground, extending from a depth of a few hundred meters to no more than three km. Two of the rocks correspond well to the already mined areas. This study provides support for subsequent exploration in the QCZ area. Full article

The Douro Demarcated Region is fundamental to local cultural and economic identity. Despite its importance, the region faces the challenge of abandoned vineyard plots, caused, among other factors, by the high costs of maintaining vineyards on hilly terrain. To solve this problem, the European Union (EU) offers subsidies to encourage active cultivation, with the aim of protecting the region’s cultural and environmental heritage. However, monitoring actively cultivated vineyards and those that have been abandoned presents considerable logistical challenges. With 43,843 vineyards spread over 250,000 hectares of rugged terrain, control of these plots is limited, which hampers the effectiveness of preservation and incentive initiatives. Currently, the EU only inspects 5 per cent of farmers annually, which results in insufficient coverage to ensure that subsidies are properly used and vineyards are actively maintained. To complement this limited monitoring, organisations such as the Instituto dos Vinhos do Douro e do Porto (IVDP) use aerial and satellite images, which are manually analysed to identify abandoned or active plots. To overcome these limitations, images can be analysed using deep learning methods, which have already shown great potential in agricultural applications. In this context, our research group has carried out some preliminary evaluations for the automatic detection of abandoned vineyards using deep learning models, which, despite showing promising results on the dataset used, proved to be limited when applied to images of the entire region. In this study, a new dataset was expanded to 137,000 images collected between 2018 and 2023, filling critical gaps in the previous datasets by including greater temporal and spatial diversity. Subsequently, a careful evaluation was carried out with various DL models. As a result, the ViT_b32 model demonstrated superior performance, achieving an average accuracy of 0.99 and an F1 score of 0.98, outperforming CNN-based models. In addition to the excellent results obtained, this dataset represents a significant contribution to advancing research in precision viticulture, providing a solid and relevant basis for future studies and driving the development of solutions applied to vineyard monitoring in the Douro Demarcated Region. These advances not only improve efficiency in detecting abandoned plots, but also contribute significantly to optimising the use of subsidies in the region. Full article

The use of minimalist shoes can lead to enhanced foot somatosensory activation and postural stability but can also increase the incidence of overuse injuries during high-impact or prolonged activities. Therefore, it appears useful to explore new strategies that employ minimalist shoes to effectively facilitate the somatosensory activation of the foot while minimizing acute and cumulative joint stress and risk of injury. To this purpose, this study introduces a novel exercise paradigm: walking for three minutes in ultra-minimalist shoes on artificial flat surfaces designed to mimic highly rugged natural terrains. The activity of foot muscles and lumbar multifidus, pain perception level, and stabilometric parameters were recorded and analyzed to characterize the novel exercise, comparing it to walking barefoot or in conventional shoes on the same rugged surface. Compared to being barefoot, ultra-minimalist shoes effectively filter nociceptive stimuli from the rugged surface, while compared to conventional shoes, they enhance the somatosensory input supporting static stability. Walking with ultra-minimalist and conventional shoes yielded higher gastrocnemius activity and lower tibialis anterior and multifidus activity compared to barefoot walking. This study highlights a practical and safe framework for enhancing foot somatosensory activation and postural stability. The new intervention is suitable for people of all ages, requires minimal time commitment, and can be performed in controlled environments such as homes, gyms, and healthcare facilities. Full article

Climate change and human activities were identified as the primary drivers of streamflow in arid alpine regions. However, limitations in observational data have resulted in a limited understanding of streamflow changes in these water sources, which hinders efforts to adapt to ongoing climate change and to formulate effective streamflow management policies. Here, we use the four main tributaries in the upper reach of the Shiyang River in China as a case study to investigate the long-term trends in streamflow within arid alpine water sources, quantifying the individual contributions of climate change and human activities to these changes. The findings revealed that temperatures and precipitation in arid alpine regions have risen over the past 40 years. Although the warming trend has been significant, it has slowed in recent years. Nevertheless, three-quarters of the rivers are experiencing a decline in streamflow. The land types within the watershed remain relatively stable, with land use and cover change (LUCC) primarily occurring in the Gulang River watershed. Climate change has significantly affected streamflow change in high and rugged terrains, with an influence exceeding 70%. For example, Jingta River showed an impact of 118.79%, Zamu River 84.00%, and Huangyang River 71.43%. Human-driven LUCC, such as the expansion of cultivated and urban land, have led to increased water consumption, resulting in reduced streamflow. This effect is particularly pronounced in the low-lying and gently undulating areas of the Gulang River, where LUCC account for 78.68% of the change in streamflow. As human activities intensify and temperatures continue to rise, further declines in streamflow are projected, highlighting the urgent need for effective water resource management. These insights highlight the urgent need for targeted mitigation and adaptation strategies to confront the water scarcity challenges faced by these vulnerable regions. Full article

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R² of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments. Full article

Land use is a remarkable human-induced change that has redesigned the Earth’s surface since the beginning of civilization. Due to the combination of rugged terrain and low-income levels in rural areas, people in watershed regions often resort to overexploiting forests, agricultural land, and grasslands beyond their capacity. As a result of these spatio-temporal changes in land use, various soil properties undergo changes. This study aims to determine the changes in some physical (texture, bulk weight, particle density, total porosity), hydro-physical (water holding capacity, permeability, field capacity, wilting point), physico-chemical (organic matter, pH, electrical conductivity), and erodibility (dispersion ratio, colloid–moisture equivalent ratio, erosion ratio, clay ratio, aggregate stability and K-factor of Universal Soil Loss Equation-USLE) properties of soil depending on land use in the Deviskel Watershed in the city of Artvin in Türkiye. For this purpose, disturbed (composite) and undisturbed (cylinder) soil samples were taken from a 0 to 20 cm depth at 108 different points in the determined areas (36 from forests, 36 from agricultural areas, and 36 from grassland areas). It was determined that 15 of the 19 soil properties examined showed statistical differences depending on the change in land use. All the examined soil properties, except for clay content, particle density, dispersion ratio, and aggregate stability, were found to be statistically significantly affected by the change in land use, and the reasons behind these changes were discussed. The particle density had the lowest coefficient of variation value (15.26%) while electrical conductivity had the highest coefficient of variation value (91.25%). According to erosion tendencies, all watershed soils were found to be susceptible to erosion. The average aggregate stability was 88.52% in forest soils, 84.84% in agricultural soils, and 85.48% in grassland soils. The average USLE-K factor was determined to be 0.22 for forests, while it was determined to be 0.17 and 0.18 for agriculture and grassland areas, respectively. According to the USLE-K factor, 68.37% of the watershed was dominated by moderately erodible soils, while 31.63% consisted of highly erodible soils. Based on the colloid–moisture equivalent ratio, erosion ratio, and clay ratio, which are statistically different erodibility features, the grassland soils of the research area were found to be more susceptible to erosion than forest and agricultural soils. In terms of aggregate stability, which indicates resistance to water erosion, forest areas had higher values, while agricultural lands were more prone to erosion. Full article

Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing factors and their mechanisms vary significantly across regions, particularly along the Chengdu–Lhasa (G318) route, which features large elevation changes, diverse climatic conditions, rugged terrain, and frequent geological disasters, making the influencing factors particularly complex. This study analyzes comment texts from 38 shared charging stations along the G318 route in the e-Charging APP, totaling 15,214 comments. A comprehensive approach is employed, including high-frequency word analysis, term frequency–inverse document frequency (TF-IDF) comparison, co-occurrence semantic network and co-word matrix feature correlation analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis. This multifaceted analysis explores core themes, user viewpoints, and sentiments in the comments, focusing on users’ perspectives on service quality, usage experience, and environmental impact of the charging stations. The findings indicate that charging speed, service attitude, environment, operational status of hardware and software, and pricing are key factors influencing user sentiment. Users have a high demand for the perfection of supporting facilities of shared charging stations, directly affecting user satisfaction and indirectly influencing the brand image and market competitiveness of enterprises. Full article

The present research’s main objective was to apply thorough exploration approaches that combine remote sensing data with geochemical sampling and analysis to predict and identify potential chromitite locations in a complex geological site, particularly in rugged mountainous terrain, and differentiate the ultramafic massif containing chromitite orebodies from other lithologies. The ultramafic massif forming the mantle section of the Kırdağ ophiolite, located within the Erzurum–Kars Ophiolite Zone and emerging in the east of Oltu district (Erzurum, NE Turkey), was selected as the study area. Optimum index factor (OIF), false-color composite (FCC), decorrelation stretch (DS), band rationing (BR), minimum noise fraction (MNF), and principal and independent component analyses (PCA-ICA) were performed to differentiate the lithological features and identify the chromitite host formations. The petrography, mineral chemistry, and whole-rock geochemical properties of the harzburgites, which are the host rocks of chromitites in the research area, were evaluated to verify and confirm the remote sensing results. In addition, detailed petrographic properties of the pyroxenite and chromitite samples are presented. The results support the existence of potential chromitite formations in the mantle section of the Kırdağ ophiolite. Our remote sensing results also demonstrate the successful detection of the spectral anomalies of this ultramafic massif. The mineral and whole-rock geochemical features provide clear evidence of petrological processes, such as partial melting and melt–peridotite interactions during the harzburgite formation. The chromian spinels’ Cr#, Mg#, Fe³⁺, Al₂O₃, and TiO₂ concentrations indicate that the harzburgite formed in a fore-arc environment. The Al₂O₃ content and Mg# of the pyroxenes and the whole-rock Al₂O₃/MgO ratio and V contents of the harzburgite are also compatible with these processes. Consequently, the combined approaches demonstrated clear advantages over conventional chromitite exploration techniques, decreasing the overall costs and supporting the occurrence of chromite production at the site. Full article

The Pisha sandstone area, situated in the upper and middle reaches of the Yellow River in China, is characterized by severe soil and water erosion, making it one of the most critical regions on the Loess Plateau. The rugged terrain and exposed bedrock complicate management efforts for this area, posing challenges for accurate forecasting using soil erosion models. Through an analysis of terrain, vegetation, and precipitation impacts on soil erosion, this study offers theoretical support for predicting soil erosion within the exposed Pisha sandstone area of the Loess Plateau. This has substantial implications for guiding water and soil conservation measures in this region. Focusing on China’s exposed sandstone area within the Geqiugou watershed, temporal and spatial changes in vegetation cover and land use from 1990 to 2020 were analyzed. The result shows that, from 1990 to 2020, the grassland area has exhibited a consistent downward trend, with successive reductions of 64.86% to 59.46%. The area of low vegetation cover witnessed a significant decline of 59.29% in 2020 compared to that in 1990. The moderate erosion area decreased from 84.52 to 57.17 km². The significant reduction in soil and water loss can be attributed to the expansion of forest and grassland areas, with the implementation of the Grain for Green project serving as a key policy driver for facilitating this expansion. This study provided a good example of combining rainfall with vegetation coverage to fast estimation soil erosion. A mathematical relationship between the vegetation rainfall coupling index (RV) and soil erosion was established with strong fitting effects, enabling estimation of the soil erosion volume under varying slope conditions within Pisha sandstone areas. The main focus of future soil and water conservation in the Pisha sandstone area should be on effectively managing the channel slope and minimizing exposed bedrock areas through a combination of slope cutting, the application of anticorrosive materials, and the implementation of artificial vegetation planting. Full article

Kelp forests are essential marine ecosystems increasingly compromised by human activities. Effective reforestation strategies are urgently needed, and the “green gravel” method is a viable tool already used in some European regions. This study aimed to assess the success of this method using the native Kelp species Laminaria ochroleuca on the Portuguese coastline. Cultures of green gravel were reared until the specimens reached a size of approximately 3 cm. The gravel was then deployed at selected sites in Peniche, Berlengas, and Cascais. Over an eight-month period, scientific scuba divers monitored the integration of Kelp, along with associated fish, invertebrate, and algae communities. Nutrient availability, temperature, water movement, substrate type, and Rugosity Index (RI) were also measured. The highest success rate was 12% in Consolação, with Elefante and Galos (Berlengas) reaching 7% and 4%, respectively. By the end of the monitoring period, Cascais had no remaining Kelp on green gravel. Present data suggest that higher success is dependent on less rugged and higher RI topography. Higher grazing pressure, rougher terrain, and unexpected sedimentation appear to be the main obstacles to deployment success. Solid knowledge (biologic and topographic) on the restoration site, starting restoration actions near already established Kelp forests, and significantly scaling up restoration efforts could substantially improve the success of the green gravel method in future reforestation campaigns. Full article

Urban development of clusters situated along rivers significantly affects the health of the river ecosystems, the quality of urban environments, and the overall well-being of local communities. Ecosystem service supply value (ESSV) measures the delivery of ecosystem goods and services within a specific timeframe in a particular area. Using the lower Yellow River urban agglomeration (Shandong section) as a case, we comprehensively applied land use structure and intensity change analysis, quantitative calculation of ESS, and geographical probe methods to unveil ESS and its mechanism of response to the spatio-temporal evolution of the intensity of land use in urban agglomeration along the river. The key results were as follows: (1) Over the past two decades, farmland and construction land areas have continued to decrease and increase, respectively, with the intensity of land use change being highest from 2005 to 2010. (2) ESS has continued to rise over the past 20 years, with the income in 2020 being 11.142 billion yuan, an increase of 31.13%. The “low-value areas” are mainly concentrated in Liaocheng City, Dezhou City, and Tai’an City, which are characterized by predominantly flat terrains where farmland constitutes the principal land use type. Conversely, “high-value areas” are largely in the counties bordering the Yellow River, including the upper estuary in the north and the rugged, southeastern terrains. (3) Areas with concentrated ESSV were primarily localized in the northern estuary area and along the Yellow River in a scattered point-like pattern. The spatial distribution of hotspots has become increasingly concentrated, transitioning from points to planes. Conversely, cold spots initially increased in number before subsequently decreasing. Waterbody was the most sensitive ESSV-determining factor. (4) The spatial heterogeneity of ESSV emerges as a consequence of the interaction of multiple factors, and among these interactions, those involving NDVI and POP contain the greatest explanatory power. Our findings are expected to offer a scientific foundation for optimizing land spatial patterns and enhancing ecological management in the lower Yellow River region. Full article