Search Results (1,074)

Free water surface constructed wetlands can be effective systems for contaminant removal, but their performance is sensitive to interactions among flow dynamics, vegetation, and bed topography. This study presents a numerical investigation into how heterogeneous bed topographies influence hydraulic and contaminant transport behavior in a rectangular wetland. Topographies were generated using a correlated pseudo-random pattern generator, and flow and solute transport were simulated with a two-dimensional, depth-averaged model. Residence time distributions and contaminant removal efficiencies were analyzed as functions of the variance and correlation length of the bed elevation. Results indicate that increasing the variability of bed elevation leads to greater dispersion in residence times, reducing hydraulic efficiency. Moreover, as the variability of bed elevation increases, so does the spread in hydraulic performance among wetlands with the same statistical topographic parameters, indicating a growing sensitivity of flow behavior to the specific spatial configurations of bed features. Larger spatial correlation lengths were found to reduce the residence time variance, as shorter correlation lengths promoted complex flow structures with lateral dead zones and internal islands. Contaminant removal efficiency, evaluated under the assumption of uniform vegetation, was influenced by bed topography, with variations becoming more pronounced under conditions of lower vegetation density. The results underscore the significant impact of bed topography on hydraulic behavior and contaminant removal performance, highlighting the importance of careful topographic design to ensure high wetland efficiency. Full article

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Benthic habitats are highly complex and diverse ecosystems that are increasingly threatened by human-induced stressors and the impacts of climate change. Therefore, accurate classification and mapping of these marine habitats are essential for effective monitoring and management. In recent years, Unmanned Aerial Vehicles (UAVs) have been increasingly used to expand the spatial coverage of surveys and to produce high-resolution imagery. These images can be processed using photogrammetry-based techniques to generate high-resolution digital elevation models (DEMs) and orthomosaics. In this study, we demonstrate that integrating descriptors extracted from pre-trained Convolutional Neural Networks (CNNs) with geomorphometric attributes derived from DEMs significantly enhances the accuracy of automatic benthic habitat classification. To assess this integration, we analyzed orthomosaics and DEMs generated from UAV imagery across three shallow reef zones along the Red Sea coast of Saudi Arabia. Furthermore, we tested various combinations of feature layers from pre-trained CNNs—including ResNet-50, VGG16, and AlexNet—together with several geomorphometric variables to evaluate classification accuracy. The results showed that features extracted from the ResNet-50 FC1000 layer, when combined with twelve geomorphometric attributes based on curvature, slope, the Topographic Ruggedness Index (TRI), and DEM-derived heights, achieved the highest overall accuracies. Moreover, training a Support Vector Machine (SVM) classifier using both pre-trained ResNet-50 features and geomorphometric variables led to an improvement in overall accuracy of up to 5%, compared to using ResNet-50 features alone. The proposed integration effectively improves the automation and accuracy of benthic habitat mapping processes. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

Peatlands are vital for global carbon cycling, and their ecological functions are influenced by vegetation composition. Accurate vegetation mapping is crucial for peatland management and conservation, but traditional methods face limitations such as low spatial resolution and labor-intensive fieldwork. We used ultra-high-resolution UAV imagery captured across seasonal and topographic gradients and assessed the impact of phenology and topography on classification accuracy. Additionally, this study evaluated the performance of four deep learning models (ResNet, Swin Transformer, ConvNeXt, and EfficientNet) for mapping vegetation in a subtropical Sphagnum peatland. ConvNeXt achieved peak accuracy at 87% during non-growing seasons through its large-kernel feature extraction capability, while ResNet served as the optimal efficient alternative for growing-season applications. Non-growing seasons facilitated superior identification of Sphagnum and monocotyledons, whereas growing seasons enhanced dicotyledon distinction through clearer morphological features. Overall accuracy in low-lying humid areas was 12–15% lower than in elevated terrain due to severe spectral confusion among vegetation. SHapley Additive exPlanations (SHAP) of the ConvNeXt model identified key vegetation indices, the digital surface model, and select textural features as primary performance drivers. This study concludes that the combination of deep learning and UAV imagery presents a powerful tool for peatland vegetation mapping, highlighting the importance of considering phenological and topographical factors. Full article

Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns. This study proposes a framework based on a convolutional autoencoder (CAE) along with a traditional classifier for identifying individuals using EEG brainprints. The convolutional autoencoder extracts a compact and discriminative representation from the topographic data cubes that capture both spatial and temporal dynamics of neural oscillations. The latent tensor features extracted by the CAE are subsequently classified by a machine learning module utilizing Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) models. EEG data were collected under three conditions—resting state, music stimuli, and cognitive task—to investigate a diverse range of neural responses. Training and testing datasets were extracted from separate sessions to enable a true longitudinal analysis. The performance of the framework was evaluated using the Area Under the Curve (AUC) and accuracy (ACC) metrics. The effect of subject identifiability was also investigated. The proposed framework achieved a performance score up to a maximum AUC of 99.89% and ACC of 96.98%. These results demonstrate the effectiveness of the proposed automated subject-specific patterns in capturing stable EEG brainprints and support the potential of the proposed framework for reliable, session-independent EEG-based biometric identification. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

To explore the relationship between features of pocket parks and restorative effects, this paper conducted a systematic review and synthesized existing empirical literature. Following the PRISMA guidelines, six databases were searched using keywords related to pocket parks and restorative outcomes. A total of 19 articles were identified. Results indicate that: (1) Research shows distinct regional phases, shifting from Northern European dominance to Asian leadership (particularly China) post-2019, with notable gaps in South America and Africa. (2) Current studies predominantly rely on cross-sectional designs and subjective assessments. (3) While existing research has evolved from initial investigations into visual landscapes and infrastructure in relation to restorative effects, expanding to encompass soundscapes and topographical dimensions, critical dimensions including nocturnal environments and intelligent technologies remain underexplored within pocket park studies. (4) Evidence confirms plant diversity, natural aesthetics, open views, enclosed boundaries, and moderate soundscapes enhance restoration, whereas excessive hardscapes and dense recreational facilities reduce effectiveness. Full article

Fuel efficiency (FE) modeling under real-world conditions remains limited in Andean cities, where topographical and traffic conditions affect vehicle performance. Vehicles powered by spark-ignition engines are the most popular in Latin America, but few studies integrate dynamic conditions with geographic features. This study addresses this gap by developing an explanatory model to predict FE for light-duty vehicles (LDVs) in the Metropolitan District of Quito (DMQ), which is one of the most congested cities in Latin America. Data were collected from eight vehicles circulating under real conditions across 35 zones in the DMQ. Predictors such as vehicle speed (VSS), acceleration (A), speed per acceleration in its 95th percentile (VA[95]), road slope, and Vehicle-Specific Power (VSP) were included in the analysis. As a first attempt, linear models were tested, but the assumptions were not satisfied. Therefore, a Gamma regression model with a logarithmic link was selected. The final model achieved a Root Mean Square Error (RMSE) of 0.939, a Relative RMSE (RRMSE) of 0.155, a Mean Absolute Error (MAE) of 0.754, and an approximate coefficient of determination (${\mathrm{R}}^2$) of 0.956. This methodology combines continuous and categorical variables and offers a replicable framework for FE estimation in other urban contexts. Full article

Gully development is a significant geomorphological and environmental process that affects land degradation worldwide, with ephemeral gullies (EGs) and permanent gullies (PGs) being the two most common types. These two gully types are often spatially connected, and with such EG-PG connectivity can accelerate erosion. However, systematic research on this phenomenon remains limited, particularly at the regional scale. This study focuses on the spatial connectivity between EGs and PGs in the Songnen black soil region of northeast China. An unequal probability stratified sampling was used to establish 977 small watershed units, and a database of gullies and their connectivity was constructed based on sub-meter imagery. Among them, 55 representative units were randomly selected within geomorphic zones for field surveys and UAV validation to ensure data accuracy. Spatial patterns of gully connectivity were analyzed, and dominant controlling factors were identified using the Geodetector, which quantifies spatial stratified heterogeneity and evaluates the explanatory power of potential driving factors. The results are as follows: (1) Gully connectivity varies significantly across the region, with hotspot areas where more than 50% of permanent gullies are connected to ephemeral gullies, and cold spot clusters elsewhere. (2) Permanent gullies connected to ephemeral gullies differ significantly from unconnected ones in both length and width, with the former exhibiting a more elongated morphology. (3) Slope length and mean annual precipitation are the primary drivers of gully connectivity, both showing significant positive effects. Moreover, the interaction between mean annual precipitation and slope length shows the strongest explanatory power, indicating that precipitation, in combination with topographic features, plays a dominant role in shaping gully connectivity. By examining the spatial patterns of gully connectivity, this study contributes to a more refined understanding of gully morphological evolution and offers empirical insights for enhancing gully erosion models and optimizing regional soil and water conservation strategies. Full article

Intensified by climate change and anthropogenic activities, flood disasters necessitate rapid and accurate mapping for effective disaster management. This study develops an integrated framework leveraging synthetic aperture radar (SAR) and cloud computing to enhance flood monitoring, with a focus on a 2024 extreme rainfall event in Liaoning Province, China. Utilizing the Google Earth Engine (GEE) platform, we combine three complementary techniques: (1) Otsu automatic thresholding, for efficient extraction of surface water extent from Sentinel-1 GRD time series (154 scenes, January–October 2024), achieving processing times under 2 min with >85% open-water accuracy; (2) random forest (RF) classification, integrating multi-source features (SAR backscatter, terrain parameters from 30 m SRTM DEM, NDVI phenology) to distinguish permanent water bodies, flooded farmland, and urban areas, attaining an overall accuracy of 92.7%; and (3) Fuzzy C-Means (FCM) clustering, incorporating backscatter ratio and topographic constraints to resolve transitional “mixed-pixel” ambiguities in flood boundaries. The RF-FCM synergy effectively mapped submerged agricultural land and urban spill zones, while the Otsu-derived flood frequency highlighted high-risk corridors (recurrence > 10%) along the riverine zones and reservoir. This multi-algorithm approach provides a scalable, high-resolution (10 m) solution for near-real-time flood assessment, supporting emergency response and sustainable water resource management in affected basins. Full article

Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation of geological features. However, in areas with dense vegetation coverage, the information directly extracted from single-source optical imagery is limited, thereby constraining interpretation accuracy. Supplementary inputs such as synthetic aperture radar (SAR), topographic features, and texture information—collectively referred to as sensitive features and prior knowledge—can improve interpretation, but their effectiveness varies significantly across time and space. This variability often leads to inconsistent performance in general-purpose models, thus limiting their practical applicability. To address these challenges, we construct a geological element interpretation dataset for Northwest China by incorporating multi-source data, including Sentinel-1 SAR imagery, Sentinel-2 multispectral imagery, sensitive features (such as the digital elevation model (DEM), texture features based on the gray-level co-occurrence matrix (GLCM), geological maps (GMs), and the normalized difference vegetation index (NDVI)), as well as prior knowledge (such as base geological maps). Using five mainstream deep learning models, we systematically evaluate the performance improvement brought by various sensitive features and prior knowledge in remote sensing-based geological interpretation. To handle disparities in spatial resolution, temporal acquisition, and noise characteristics across sensors, we further develop a multi-source complement-driven network (MCDNet) that integrates an improved feature rectification module (IFRM) and an attention-enhanced fusion module (AFM) to achieve effective cross-modal alignment and noise suppression. Experimental results demonstrate that the integration of multi-source sensitive features and prior knowledge leads to a 2.32–6.69% improvement in mIoU for geological elements interpretation, with base geological maps and topographic features contributing most significantly to accuracy gains. Full article

The Lower Congo Basin (LCB) and the Niger Delta Basin (NDB), two end-member deep-water systems along the West African passive margin, exhibit contrasting sedimentary architectures despite shared geodynamic settings. The research comprehensively utilizes seismic reflection structure, root mean square amplitude slices, drilling lithology, changes in logging curves, and previous research achievements to elucidate the controlling mechanisms behind these differences. Key findings include: (1) Stark depositional contrast: Since the Eocene, the LCB developed retrogradational narrow-shelf systems dominated by erosional channels and terminal lobes, whereas the NDB formed progradational broad-shelf complexes with fan lobes and delta-fed turbidites. (2) Primary controls: Diapir-driven topographic features and basement uplift govern architectural variability, whereas shelf-slope break configuration and oceanic relief constitute subordinate controls. (3) Novel mechanism: First quantification of how diapir-induced seafloor relief redirects sediment pathways and amplifies facies heterogeneity. These insights establish a tectono-sedimentary framework for predicting deep-water reservoirs in diapir-affected passive margins, refine the conventional “source-to-sink” model by emphasizing salt-geomorphic features coupling as the primary driver. By analyzing the differences in lithofacies assemblages and sedimentary configurations among the above-mentioned different basins, this study can provide beneficial insights for the research on related deep-water turbidity current systems and also offer guidance for deep-water oil and gas exploration and development in the West African region and other similar areas. Full article