Search Results (94)

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

Classifying land cover in mountainous areas has always been challenging due to the high diversity of ecosystems and the complexity of the spectral–temporal–spatial relationships caused by the rugged terrain. This paper introduces multi-year synthesized phenology data to improve land cover classification in these regions. Using the Shennongjia Forestry District in Hubei Province, China, as a case study, we investigate how incorporating multi-year synthesized phenology data enhances the accuracy of land cover classification with single-temporal and multi-temporal remote sensing imagery, as well as how it aids in identifying different vegetation types in shaded areas of the mountains. The research results indicate that incorporating multi-year synthesized phenology data significantly improves the accuracy of land cover classification for single summer imagery, single autumn imagery, multi-temporal summer–autumn imagery, and mountain shadow areas. The Kappa coefficient (Kappa) increased by 1.57% to 9.93%, while overall accuracy (OA) improved by 1.4% to 8.75%. Notably, the improvement in classification accuracy was most pronounced for single summer imagery. Furthermore, the results demonstrate that, in the absence of terrain data, multi-year synthesized phenology data provide even greater enhancements in land cover classification accuracy using remote sensing imagery. Full article

Precise extraction of rural settlements in alpine regions is critical for geographic data production, rural development, and spatial optimization. However, existing deep learning models are hindered by insufficient datasets and suboptimal algorithm structures, resulting in blurred boundaries and inadequate extraction accuracy. Therefore, this study uses high-resolution unmanned aerial vehicle (UAV) remote sensing images to construct a specialized dataset for the extraction of rural settlements in alpine mountainous areas, while introducing an innovative shadow mitigation technique that integrates multiple spectral characteristics. This methodology effectively addresses the challenges posed by intense shadows in settlements and environmental occlusions common in mountainous terrain analysis. Based on the comparative experiments with existing deep learning models, the Swin Transformer was selected as the baseline model. Building upon this, the Feature Fusion Swin Transformer (FF-SwinT) model was constructed by optimizing the data processing, loss function, and multi-view feature fusion. Finally, we rigorously evaluated it through ablation studies, generalization tests and large-scale image application experiments. The results show that the FF-SwinT has improved in many indicators compared with the traditional Swin Transformer, and the recognition results have clear edges and strong integrity. These results suggest that the FF-SwinT establishes a novel framework for rural settlement extraction in alpine mountain regions, which is of great significance for regional spatial optimization and development policy formulation. Full article

The lower Yarlung Zangbo River Basin’s Great Bend region, characterized by extreme topography and intense orographic precipitation processes, presents significant challenges for accurate precipitation estimation using reanalysis products. Therefore, this study evaluates four widely used products (ERA5-Land, MSWEP, CMA, and TPMFD) against station observations (2014–2022) in this critical area. Performance was rigorously assessed using correlation analysis, error metrics (RMSE, MAE, RBIAS), and spatial regression. The region exhibits strong seasonality, with 62.1% of annual rainfall occurring during the monsoon (June-October). Results indicate TPMFD performed best overall, capturing spatiotemporal patterns effectively (correlation coefficients 0.6–0.8, low RBIAS). Conversely, ERA5-Land significantly overestimated precipitation, particularly in rugged northeast areas, suggesting poor representation of orographic effects. MSWEP and CMA underestimated rainfall with variable temporal consistency. Topographic analysis confirmed slope, aspect, and longitude strongly control precipitation distribution, aligning with classical orographic mechanisms (e.g., windward enhancement, lee-side rain shadows) and monsoonal moisture transport. Spatial regression revealed terrain features explain 15.4% of flood-season variation. TPMFD most accurately captured these terrain-precipitation relationships. Consequently, findings underscore the necessity for terrain-sensitive calibration and data fusion strategies in mountainous regions to improve precipitation products and hydrological modeling under orographic influence. Full article

To address existing issues in water extraction from SAR images based on deep learning, such as confusion between mountain shadows and water bodies and difficulty in extracting complex boundary details for continuous water bodies, the LGFUNet model is proposed. The LGFUNet model consists of three parts: the encoder–decoder, the DECASPP module, and the LGFF module. In the encoder–decoder, the Swin-Transformer module is used instead of convolution kernels for feature extraction, enhancing the learning of global information and improving the model’s ability to capture the spatial features of continuous water bodies. The DECASPP module is employed to extract and select multiscale features, focusing on complex water body boundary details. Additionally, a series of LGFF modules are inserted between the encoder and decoder to reduce the semantic gap between the encoder and decoder feature maps and the spatial information loss caused by the encoder’s downsampling process, improving the model’s ability to learn detailed information. Sentinel-1 SAR data from the Qinghai–Tibet Plateau region are selected, and the water extraction performance of the proposed LGFUNet model is compared with that of existing methods such as U-Net, Swin-UNet, and SCUNet++. The results show that the LGFUNet model achieves the best performance, respectively. Full article

Urban regulations in Amman, Jordan, enforce uniform building setbacks irrespective of topography, exacerbating shading effects and compromising daylight access in residential areas—a critical factor for occupant health and psychological well-being. This study evaluates the interplay between standardized setbacks, slope variations (0–30%), and shadow patterns in Amman’s dense, mountainous urban fabric. Focusing on the Al Jubayhah district, a mixed-methods approach was used, combining field surveys, 3D modeling (Revit), and seasonal shadow simulations (March, September, December) to quantify daylight deprivation. The results reveal severe shading in winter (78.3% site coverage in December) and identify slope-dependent setbacks as a key determinant: for instance, a 15 m building on a 30% slope requires a 26.4 m rear setback to mitigate shadows, compared to 13.8 m on flat terrain. Over 39% of basements in the study area remain permanently shaded due to retaining walls, correlating with poor living conditions. The findings challenge Amman’s one-size-fits-all regulatory framework (Building Code No. 67, 1979), and we propose adaptive guidelines, including slope-adjusted setbacks, restricted basement usage, and optimized street orientation. This research underscores the urgency of context-sensitive urban policies in mountainous cities to balance developmental density with daylight equity, offering a replicable methodology for similar Mediterranean climates. Full article

Global warming and intensified human activities increase flood disasters, causing annual casualties and economic losses. Mountain shadows are a major source of interference in floodwater extraction from SAR imagery, severely impacting the accuracy of water body detection. This study proposes an innovative approach based on the Inverted Exponential Shadow Removal Model (IESRM). This model can adaptively and dynamically adjust the slope threshold according to the terrain characteristics. It is easy to use, eliminating the need for manual parameter setting. The experimental results demonstrate the following: (1) Water body detection tests across diverse terrains (mountains, plains, and foothill plains) show robust results even in complex foothill regions, with an overall accuracy of 94.51% and a Kappa coefficient of 0.86. (2) A comparative analysis with the shadow formation mechanism method and the HAND (Height Above Nearest Drainage) method revealed that the inverted exponential function model achieved the highest accuracy, with an overall accuracy of 96.46% and a Kappa coefficient of 0.89. The IESRM provides an innovative solution for removing mountain shadows, enhancing SAR imagery-based flood monitoring in complex terrains. It offers timely and accurate data support for flood disaster management agencies. Full article

This study develops an optimized approach for small-scale forest area extraction in mountainous regions by integrating Landsat multispectral and ALOS PALSAR-2 radar data through threshold-based classification methods. The threshold fusion method proposed in this study achieves innovations in three key aspects: First, by integrating Landsat NDVI with PALSAR-2 polarization characteristics, it effectively addresses omission errors caused by cloud interference and terrain shadows. Second, the adoption of a decision-level (rather than feature-level) fusion strategy significantly reduces computational complexity. Finally, the incorporation of terrain correction (slope > 20° and aspect 60–120°) enhances classification accuracy, providing a reliable technical solution for small-scale forest monitoring. The results indicate that (1) the combination of Landsat multispectral remote sensing data and PALSAR-2 radar remote sensing data achieved the highest classification accuracy, with an overall forest classification accuracy of 97.62% in 2015 and 96.97% in 2022. The overall classification accuracy of Landsat multispectral remote sensing data alone was 93%, and that of PALSAR radar data alone was 85%, which is significantly lower than the results obtained using the combined data for forest classification. (2) Between 2015 and 2023, the forest area of Helan Mountain experienced certain fluctuations, primarily influenced by ecological and natural factors as well as variations in the accuracy of remote sensing data. In conclusion, the method proposed in this study enables more precise estimation of the forest area in the Helan Mountain region of Ningxia. This not only meets the management needs for forest resources in Helan Mountain but also provides valuable reference for forest area extraction in mountainous regions of Northwest China. Full article

Shadow in remote sensing images can obscure important details of land features, making shadow detection crucial for enhancing the accuracy of subsequent analyses and applications. Current shadow detection methods primarily rely on the spectral information of images, which can often result in shadow misdetection due to the phenomenon of spectral confusion of different objects. To mitigate this issue, we propose a method that combines topography and spectra (CTS). Firstly, we introduce a new DEM-based shadow coarse detection method to obtain the DEM rough shadow mask, which uses a relationship between the magnitude of terrain height angle and solar elevation angle to determine shadow properties. Then, we use the MC3 (modified C3 component) index-based shadow fine detection method to obtain an MC3 mean map, which includes image enhancement with a stretching process and multi-scale superpixel segmentation. We then derive the Shadow pixel Proportion Map (SPM) by counting the DEM rough shadow mask in terms of superpixels. The Joint Shadow probability Map (JSM) is obtained by combining the SPM and the MC3 mean map with specific weights. Finally, a multi-level Otsu threshold method is applied to the JSM to generate the shadow mask. We compare the proposed CTS method against several state-of-the-art algorithms through both qualitative assessments and quantitative metrics. The results show that the CTS method demonstrates superior accuracy and consistency in detecting true shadows, achieving an average overall accuracy of 95.81% on mountainous remote sensing images. Full article

The accurate extraction of mountainous cropland from remote sensing images remains challenging due to its fragmented plots, irregular shapes, and the terrain-induced shadows. To address this, we propose a deep learning framework, SE-ResUNet, that integrates Squeeze-and-Excitation (SE) modules into ResUNet to enhance feature representation. Leveraging Sentinel-1/2 imagery and DEM data, we fuse vegetation indices (NDVI/EVI), terrain features (Slope/TRI), and SAR polarization characteristics into 3-channel inputs, optimizing the network’s discriminative capacity. Comparative experiments on network architectures, feature combinations, and terrain conditions demonstrated the superiority of our approach. The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. This study provides a scalable solution for mountainous cropland mapping, supporting precision agriculture and sustainable land management. Full article

Flooding is one of the most frequent natural disasters at present, and can pose a serious threat to transmission towers. In response to the accuracy and timeliness requirements of flood emergency monitoring, a local region growth algorithm combining polarization and texture information is proposed for Synthetic Aperture Radar (SAR) image water recognition. Morphological methods and external geographic information are used to optimize the results, allowing for rapid extraction of the flood range. The method is validated using Gaofen-3 (GF-3) Fine Strip Imaging Mode II (FSII) SAR images covering Fangshan District in Beijing, China. The experimental results indicate that this method can obtain more effective water information compared to traditional threshold segmentation methods, and can also reduce the effects of noise and mountain shadows. It has good applicability and timeliness with respect to large-scale flood emergency disaster monitoring, and can help to rapidly and accurately obtain detailed information of flood-affected areas, thus providing reference for emergency rescue and disaster relief services. Full article

In response to the issue that existing soil moisture monitoring methods are significantly affected by surface roughness and the complex environment around water bodies, leading to a need for improvement in the accuracy of soil moisture inversion, a soil moisture detection algorithm based on a DCU-Net (Deformable Conv Unit-Net) water body extraction model is proposed, using the Ningxia region as the study area. The algorithm introduces the DCU (Deformable Conv Unit) module, which addresses the problem of extracting small water bodies at large scales with low resolution; reduces the probability of misjudgment during water body extraction caused by shadows from mountains, buildings, and other objects; and enhances the robustness and adaptability of the water body extraction algorithm. The method first creates a water body extraction dataset based on multi-year remote sensing images from Ningxia Province and trains the proposed DCU-Net model; then, it selects remote sensing images from certain areas for water body extraction; finally, it conducts regression analysis between the water body areas of Ningxia Province at different times and the corresponding measured soil moisture data to establish the intrinsic relationship between water body areas and soil moisture in the study area, achieving real-time regional soil moisture monitoring. The water body extraction performance of DCU-Net is verified based on extraction accuracy, with U-Net selected as the baseline network. The experimental results show that DCU-Net leads to improvements of 2.98%, 1.37%, 0.36%, and 1.49% in terms of IoU, Precision, Recall, and $F_1$, respectively. The algorithm is more sensitive to water body feature information, can more accurately identify water bodies, and extracts water body contours more accurately. Additionally, a soil moisture inversion method based on a cubic polynomial is constructed. These results indicate that DCU-Net can precisely extract water body contours and accurately invert regional soil moisture, thereby providing support for the monitoring of large-scale soil moisture. Full article

Automatic glacier extraction from remote sensing images is the most important approach for large scale glacier monitoring. Commonly used band calculation indices to enhance glacier information are not effective in identifying shadowed glaciers and debris-covered glaciers. In this study, we used the Kolmogorov–Smirnov test as the theoretical basis and determined the most suitable band calculation indices to distinguish different land cover classes by comparing inter-sample separability and reasonable threshold range ratios of different indices. We then constructed a glacier classification decision tree. This approach resulted in the development of a method to automatically extract glacier areas at given spatial and temporal scales. In comparison with the commonly used indices, this method demonstrates an improvement in Cohen’s kappa coefficient by more than 3.8%. Notably, the accuracy for shadowed glaciers and debris-covered glaciers, which are prone to misclassification, is substantially enhanced by 108.0% and 6.3%, respectively. By testing the method in the Qilian Mountains, the positive prediction value of glacier extraction was calculated to be 91.8%, the true positive rate was 94.0%, and Cohen’s kappa coefficient was 0.924, making it well suited for glacier extraction. This method can be used for monitoring glacier changes in global mountainous regions, and provide support for climate change research, water resource management, and disaster early warning systems. Full article

Mountainous roads are severely affected by environmental factors such as insufficient lighting and shadows from tree branches, which complicates the detection of drivers’ facial features and the determination of fatigue states. An improved method for recognizing driver fatigue states on mountainous roads using the YOLOv5 neural network is proposed. Initially, modules from Deformable Convolutional Networks (DCNs) are integrated into the feature extraction stage of the YOLOv5 framework to improve the model’s flexibility in recognizing facial characteristics and handling postural changes. Subsequently, a Triplet Attention (TA) mechanism is embedded within the YOLOv5 network to bolster image noise suppression and improve the network’s robustness in recognition. Finally, the Wing loss function is introduced into the YOLOv5 model to heighten the sensitivity to micro-features and enhance the network’s capability to capture details. Experimental results demonstrate that the modified YOLOv5 neural network achieves an average accuracy rate of 85% in recognizing driver fatigue states. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article