Search Results (47)

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Accurate prediction of geological hazard susceptibility forms the foundation of effective risk management, yet small-sample constraints often limit model generalization. In order to address this issue, this study applied an ensemble method based on predictive symmetry quantification, using Mount Tai, China, as a test case. Thirteen influencing factors were integrated using six machine learning algorithms—Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM)—trained on 34 hazard sites. Symmetry breaking in model outputs was quantified, and XGB and MLP, which showed the lowest correlation (0.59), were selected for dynamic weighted integration. Symmetry-adjusted weighting counteracts bias from individual models. For hyperparameter tuning, grid search was employed, while SHapley Additive exPlanations (SHAP) was used to quantify factor contributions. The performance of each model was evaluated using AUC and AP metrics. The key results show that all base models performed robustly (AUC > 0.8), with XGB showing high consistency (AUC = 0.927), and the performance of the symmetry-optimized ensemble (MLP + XGB) exceeded that of all the individual models (AUC = 0.964). The dominant drivers of Geohazards included elevation, slope, the topographic wetness index, and road adjacency, with high-susceptibility zones clustered in southeastern high-altitude terrain, central mountains, and road-intensive north-central sectors. The approach presented here provides an ensemble method based on predictive symmetry quantification that is effective under the constraints of small sample sizes. Full article

This study assesses whether the rise in water level—following three years of reservoir filling at the Teles Pires Hydroelectric Plant (135.6 km² water surface) in Southern Amazonia—has affected the floristic composition, structure, and dynamics of adjacent forests. We established 62 permanent plots (2000 m² each) across a topographic gradient from the reservoir margin and conducted annual tree inventories for individuals with DBH ≥ 10 cm from 2014 to 2017. A total of 6322 individuals were recorded, representing 322 species, 210 genera, and 61 families. Fabaceae was the most abundant family, and the ten species with the highest importance value index (IVI) before reservoir filling remained dominant afterward. The forests exhibited high species richness and were characterized by a few common and many rare species. Mortality rates were highest within 10 m of elevation from the maximum reservoir level, indicating possible hydrological impacts, although no abnormal dieback or sharp shifts in floristic structure were observed. These results suggest limited short-term effects on species composition, but subtle changes in vegetation dynamics underscore the importance of long-term monitoring. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

Electrospun fibers, possessing biomimetic characteristics similar to fibrous extracellular matrices, have attracted widespread attention as scaffold materials for skin tissue engineering. The topographical structure of electrospun fibers plays a critical role in determining cell behavior. However, the effects of fiber topography on human skin fibroblasts (HSFs) remain unclear. In this study, electrospinning technology was employed to investigate how parallel and crossed fiber architectures influence the spreading morphology, proliferation, and migration of HSFs. The results demonstrated that cells exhibited spindle-shaped elongation along single fibers; on closely spaced parallel fibers, cells formed cross-adhesions between adjacent fibers, with a fiber spacing of 30–60 μm serving as the threshold range for distinguishing individual cell behaviors. At fiber intersections, a characteristic spacing of 100 μm distinguished three distinct cellular responses: anchoring, turning, and bridging. The probability of a cell altering its preexisting migration path depended on its ability to extend laterally and reach adjacent fibers, which was constrained by the upper limit of the cell body’s minor axis. This study elucidated the unique role of the electrospun fiber topography in guiding cellular decision-making in complex microenvironments, provided important insights into topography-triggered cell migration, and highlighted the practical significance of material-guided strategies in tissue engineering. Full article

This research aims to characterize sediment dynamics in the Rupinaro catchment, a uniquely terraced and human-shaped basin in Italy’s Liguria region, employing geomorphometric methods to unravel sediment connectivity in a landscape vulnerable to shallow landslides. Within a scenario-based approach, we utilized high-resolution LiDAR-derived digital terrain models (DTMs) to calculate the Connectivity Index, comparing sediment dynamics between the original terraced landscape and a virtual natural scenario. To reconstruct a pristine slope morphology, we applied a topographic roughness-based skeletonization algorithm that simplifies terraces into linear features to simulate natural hillslope conditions and remove anthropogenic structures. The analysis was carried out considering diverse targets (e.g., hydrographic networks, road networks) and the effect of land use. The results reveal significant differences in sediment connectivity between the anthropogenic and natural morphologies, with implications for erosion and landslide susceptibility. The findings reveal that sediment connectivity is moderately higher in the scenario without terraces, indicating that terraces function as effective barriers to sediment transfer. This highlights their potential role in mitigating landslide susceptibility on steep slopes. Additionally, the results show that roads exert a stronger influence on the Connectivity Index, significantly altering flow paths. These modifications appear to contribute to increased landslide susceptibility in adjacent areas, as reflected by the higher observed landslide density within the study region. Full article

The dorsomedial caudate–putamen (dmCPu), a key input structure of the basal ganglia, plays a crucial role in goal-directed behaviors and the transition to habits. The functional specialization of the dmCPu along its anteroposterior axis suggests that distinct prefrontal cortex (PFC) subregions may differentially contribute to these processes. However, the precise topographical organization of PFC and adjacent areas projections to the anterior and posterior dmCPu remains poorly understood. We employed retrograde tracing using Fluoro-Gold to map the projections from PFC subregions and adjacent areas to the anterior and posterior dmCPu in male Sprague Dawley rats. Histological verification and immunohistochemical labeling were conducted to confirm injection sites and neuronal labeling. Quantitative analyses were performed to assess the effects of injection site placement (anterior vs. posterior dmCPu), laterality (ipsilateral vs. contralateral), and cortical subregion on projection density. The posterior dmCPu received significantly higher projection densities than the anterior dmCPu, with a pronounced ipsilateral dominance across all cortical subregions. Among the subregions examined, the cingulate cortex exhibited the highest number of labeled neurons projecting to the dmCPu, with distinct patterns of connectivity between anterior and posterior injection sites. Notably, motor and somatosensory cortical projections were more prominent in the posterior dmCPu, whereas cingulate projections demonstrated robust anteroposterior and lateralized differences. These findings provide a comprehensive map of the topographical organization of cortical inputs to the dmCPu, highlighting differential connectivity patterns that may underlie distinct functional roles in goal-directed and habitual behaviors. This work advances our understanding of corticostriatal circuits and their relevance to adaptive behaviors and neuropsychiatric disorders. Full article

Flood disasters are one of the major natural hazards threatening agricultural production. To reduce agricultural disaster losses, accurately identifying agricultural flood-affected areas is crucial. Taking Shengzhou City as a case study, we proposed a refined method for identifying agricultural flood-affected areas by integrating microwave and optical remote sensing data with deep learning techniques, GIS, and the pixel-based direct differencing method. Complementary advantages of microwave and optical remote sensing data can effectively solve the problem of difficulty in accurately detecting floods due to thick clouds before and after flood disasters. Deep learning technology can effectively identify farmland areas, and the pixel direct difference method can accurately analyze agricultural flood disasters. Analyzing three typical rainfall events along with the topographical and geomorphological characteristics of Shengzhou City, the results indicate that agricultural flood disaster areas exhibit significant spatial heterogeneity. The primary influencing factors include rainfall intensity, topography, and drainage infrastructure. The northern, eastern, and southwestern regions of Shengzhou City, particularly the peripheral areas adjacent to mountainous and hilly terrains, contain most of the flood-affected farmland. These areas, characterized by low-lying topography, are highly susceptible to flood disasters. Therefore, optimizing the drainage systems of farmland in low-lying areas near mountainous and hilly regions of Shengzhou City is essential to enhance flood resilience. Full article

This research seeks to develop and test a rapid mapping approach using unmanned aerial vehicles (UAVs) and terrestrial laser scanning to provide precise, high-resolution spatial data for urban areas right after disasters. This mapping aims to support efforts to protect the population and infrastructure while analyzing the situation in affected areas. It focuses on flood-prone regions lacking modern hydrological data and where regular monitoring is absent. This study was conducted in resort villages and adjacent catchments in Bulgaria’s southern Black Sea coast with leading maritime tourism features, after a flash flood on 5 September 2023 caused human casualties and severe material damage. The resulting field data with a spatial resolution of 3 to 5 cm/px were used to trace the effects of the flood on topographic surface changes and structural disturbances. Flood simulation using UAV data and a digital elevation model was performed. The appropriateness of contemporary land use forms and infrastructure location in catchments is discussed. The role of spatial data in the analysis of genetic factors in risk assessment is commented on. The results confirm the applicability of rapid mapping in informing the activities of responders in a period of increased vulnerability following a flood. The results were used by Bulgaria’s Ministry of Environment and Water to analyze the situation shortly after the disaster. Full article

Agricultural greenhouses (AGs) are an effective solution to address the growing demand for vegetables despite limited cropland, yet significant soil quality problems often accompany them, particularly in high-altitude regions. However, the effects of natural factors and production management on soil quality are not well understood in such fragile environments. This study analyzed soil quality differences between AGs and adjacent open cropland (OCs) in the Lhasa River Valley, Tibetan Plateau, based on 592 soil samples and 12 key soil physicochemical indicators. GeoDetector was used to identify the dominant factors and their interactions with these differences. The results showed that AG soils had significantly lower pH, with an average decrease of 20%, indicating acidification, while nutrient levels and total salinity were significantly higher compared to OC soils. Specifically, available phosphorus, available potassium, the soil fertility quality index, and total soluble salt increased by 281%, 102%, 38%, and 184%, respectively. Planting, topographic, and fertilizer factors were identified as the dominant factors contributing to these differences. Interaction analysis showed that the interaction of these factors increased the explanatory power by 20.2% to 41.32% compared to individual factors. The interaction between planting year and fertilizer type had the highest explanatory power for nutrient increases and pH decline, while fertilizer amount and slope aspect contributed to salinity accumulation. These findings provide valuable insights and practical guidance for optimizing AG management and ensuring sustainable agricultural development in high-altitude regions. Full article

Vertical creep along 15 ground ruptures within a 15 km long and 1.5 km wide zone has been occurring along the southeastern part of Metro Manila. Though the unusually high rates of vertical slip point to excessive groundwater withdrawal as the trigger, the evidence presented herein indicates that these may not be simple irregular subsidence fissures. Tectonic control of creep along these traces is suggested by the following: the occurrence of some of these ground ruptures along pre-existing scarps that coincide with topographic and lithologic boundaries, the left-stepping en echelon pattern of surface rupturing, and the distribution of the creeping zone within the dilational gap of the dextral strike-slip West Valley Fault (WVF). Furthermore, interpretation of an exposure across one of the creeping faults indicates reactivation by creep of a pre-existing tectonic fault zone. The paleoseismic evidence also suggests that the pre-creep slips are coseismic and dominantly strike-slip. Recognizing the occurrence of coseismic slip preceding aseismic creep is a primary consideration in assessing the potential of the WVF’s creeping segment and its adjacent segments in generating earthquakes. Tighter groundwater extraction regulations may be necessary to avoid exacerbating the effects of vertical ground deformation and the occurrence of induced seismicity. Full article

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. Full article

The microphase separation of high-molecular-weight block copolymers into nanostructured films is strongly dependent on the surface fields. Both, the chain mobility and the effective interaction parameters can lead to deviations from the bulk morphologies in the structures adjacent to the substrate. Resolving frustrated morphologies with domain period L₀ above 100 nm is an experimental challenge. Here, solvothermal annealing was used to assess the contribution of elevated temperatures of the vapor T_v and of the substrate T_s on the evolution of the microphase-separated structures in thin films symmetric of polystyrene-b-poly(2vinylpyridine) block copolymer (PS-PVP) with L₀ about 120 nm. Pronounced topographic mesh-like and stripe patterns develop on a time scale of min and are attributed to the perforated lamella (PL) and up-standing lamella phases. By setting T_v/T_s combinations it is possible to tune the sizes of the resulting PL patterns by almost 10%. Resolving chemical periodicity using selective metallization of the structures revealed multiplication of the topographic stripes, i.e., complex segregation of the component within the topographic pattern, presumably as a result of morphological phase transition from initial non-equilibrium spherical morphology. Reported results reveal approaches to tune the topographical and chemical periodicity of microphase separation of high-molecular-weight block copolymers under strong confinement, which is essential for exploiting these structures as functional templates. Full article

Plain lakes play a crucial role in the hydrological cycle of a watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river–lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction with the Yangtze River, revealing the spatiotemporal patterns of flood propagation within the lake and its impact on surrounding flood control measures. The aim is to provide insights for flood management in similar environments worldwide. This study employs a comprehensive approach combining hydrological statistical analysis and two-dimensional hydrodynamic modeling, based on extensive hydrological, topographic, and socio-economic data. The results indicate that the annual maximum outflow from Poyang Lake is primarily controlled by floods within the watershed, while the highest annual lake water level is predominantly influenced by floods from the Yangtze River. The peak discharge typically reaches the lake outlet within 48 h, with the peak water level taking slightly longer at 54 h. However, water storage in the lake can shorten the time that it takes for the peak discharge to arrive. When converging with floods from the Yangtze River, the peak water level may be delayed by up to 10 days, due to the top-supporting interaction. Furthermore, floods from the “Five Rivers” propagate differently within the lake, affecting various lake regions to differing degrees. Notably, floods from the Fu River cause the most significant rise in the lake’s water level under the same flow rate. The top-supporting effect from the Yangtze River also significantly impacts the water surface slope of Poyang Lake. When the Yangtze River flood discharge significantly exceeds that of the “Five Rivers” (i.e., when the top-supporting intensity value, f, exceeds four), the lake surface becomes as flat as a reservoir. During major floods in the watershed, the water level difference in the lake can increase dramatically, potentially creating a “dynamic storage capacity” of up to 840 million cubic meters. Full article

The energy state of endosteal implants is dependent on the material, manufacturing technique, cleaning procedure, sterilization method, and surgical manipulation. An implant surface carrying a positive charge renders hydrophilic properties, thereby facilitating the absorption of vital plasma proteins crucial for osteogenic interactions. Techniques to control the surface charge involve processes like oxidation, chemical and topographical adjustments as well as the application of nonthermal plasma (NTP) treatment. NTP at atmospheric pressure and at room temperature can induce chemical and/or physical reactions that enhance wettability through surface energy changes. NTP has thus been used to modify the oxide layer of endosteal implants that interface with adjacent tissue cells and proteins. Results have indicated that if applied prior to implantation, NTP strengthens the interaction with surrounding hard tissue structures during the critical phases of early healing, thereby promoting rapid bone formation. Also, during this time period, NTP has been found to result in enhanced biomechanical fixation. As such, the application of NTP may serve as a practical and reliable method to improve healing outcomes. This review aims to provide an in-depth exploration of the parameters to be considered in the application of NTP on endosteal implants. In addition, the short- and long-term effects of NTP on osseointegration are addressed, as well as recent advances in the utilization of NTP in the treatment of periodontal disease. Full article