Freeze–thaw is the major factor in gravitational erosion creep of slopes in cold regions, but gravitational erosion is influenced remarkably by vegetation cover. In order to investigate the ...phenomenon of the gravitational erosion process of vegetation slopes under freeze–thaw in seasonally frozen soil regions, on-site freeze–thaw tests of different vegetation slopes were conducted in the frozen soil testing ground of Kitami Institute of Technology (KIT, Hokkaido, Japan). Slope vegetation is divided into an external-soil spray seeding section and a turfed section according to its method of formation. Through field tests of four cycles, the slope temperature, frost depth, amount of frost-heave, and amount of movement were observed dynamically in real-time, initially revealing the regularity of the gravitational erosion process of the vegetation slope, which provided a scientific basis for slope stability control in cold regions. The experimental results show that the gravitational erosion of the slope is a process of irreversible gradual evolution, that the extent of erosion has a periodical fluctuation in time, that the target point of the slope surface moves in a jagged trajectory down the slope year after year, and that the maximum values of the amount of movement of the external-soil spray seeding section and the turfed section are −13.1 and –6.1 cm, respectively, after four freeze–thaw cycles. The space distribution of the slope surface has inhomogeneity. The difference in temperature and water content of each part of the slope surface is the main reason why the freezing front of the slope is not parallel to the slope surface. The amount of frost-heave of the slope toe was greater than that of the slope crest, which caused upward displacement along the slope during early freezing. The movement of the slope is closely related to the development of vegetation, and the heat insulation and reinforcement of vegetation cover effectively restrain the displacement of the slope's shallow soil. The results of this study have a certain significance in the prevention and treatment of shallow slope sliding in cold regions.
•The actual trajectory of movement of the slope surface is captured.•The different distribution of water content and temperature causes the freezing front not to be parallel to slope surface.•Slope behaviour is closely related to the development of the root system.
This paper presents an experimental study on the anisotropic shear strength behavior of soil–geogrid interfaces. A new type of interface shear test device was developed, and a series of soil–geogrid ...interface shear tests were conducted for three different biaxial geogrids and three different triaxial geogrids under the shear directions of 0°, 45° and 90°. Clean fine sand, coarse sand, and gravel were selected as the testing materials to investigate the influence of particle size. The experimental results for the interface shear strength behavior, and the influences of shear direction and particle size are presented and discussed. The results indicate that the interface shear strength under the same normal stress varies with shear direction for all the biaxial and triaxial geogrids investigated, which shows anisotropic shear strength behavior of soil–geogrid interfaces. The soil–biaxial geogrid interfaces show stronger anisotropy than that of the soil–triaxial geogrid interfaces under different shear directions. Particle size has a great influence on the anisotropy shear strength behavior of soil–geogrid interfaces.
Groundwater flow has a negative impact on the artificial freezing project, especially when the salt content of the groundwater is high, it will change the thermophysical properties, which isn’t ...conducive to the formation of the frozen wall. In this paper, the changes in temperature and seepage fields during the formation of frozen wall under the flow of saline groundwater were studied. By established the seepage-freezing hydrothermal coupling calculation model, and combined with a small seepage-freezing model test, which based on the theory of heat transfer and seepage in porous media. Results show that the frozen wall is in an uneven state under the action of horizontal seepage. The frozen wall perpendicular to the direction of groundwater flow is formed slowly, while the frozen wall parallel to the direction of water flow is formed quickly. The salt content affects the development of the frozen wall. As the salt content increases, the thickness of the frozen wall decreases. The maximum percentage of reduction of the upstream and downstream frozen wall thickness can reach 80.4% and 59.5%, respectively. With the gradual expansion of the frozen wall around two freezing pipes, the flow around and the change of the flow velocity occur. The maximum value of the flow rate is about 3–4 times of the initial flow rate, and then the flow rate decreases rapidly until the frozen wall encloses. When the seepage velocity increases to 5.5 m/d and the salt content reaches 15‰ and above, the time for the closure of the frozen wall is significantly delayed or even impossible. The conclusions obtained in this study can provide a theoretical reference for the design and construction of the similar engineering.
Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, ...the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complete and continuous road network from HRI. RALC-Net utilizes a dual-encoder structure to improve the feature extraction capability of the network, whose two different branches take different feature information as input data. Specifically, we construct the residual attention module using the residual connection that can integrate spatial context information and the attention mechanism, highlighting local semantics to extract local feature information of roads. The residual attention module combines the characteristics of both the residual connection and the attention mechanism to retain complete road edge information, highlight essential semantics, and enhance the generalization capability of the network model. In addition, the multi-scale dilated convolution module is used to extract multi-scale spatial receptive fields to improve the model’s performance further. We perform experiments to verify the performance of each component of RALC-Net through the ablation study. By combining low-level features with high-level semantics, we extract road information and make comparisons with other state-of-the-art models. The experimental results show that the proposed RALC-Net has excellent feature representation ability and robust generalizability, and can extract complete road information from a complex environment.
Tree species classification is crucial for forest resource investigation and management. Remote sensing images can provide monitoring information on the spatial distribution of tree species and ...multi-feature fusion can improve the classification accuracy of tree species. However, different features will play their own unique role. Therefore, considering various related factors about the growth of tree species such as spectrum information, texture structure, vegetation phenology, and topography environment, we fused multi-feature and multi-temporal Sentinel-2 data, which combines spectral features with three other types of features. We combined different feature-combinations with the random forest method to classify Changbai Mountain tree species. Results indicate that topographic features participate in tree species classification with higher accuracy and more efficiency than phenological features and texture features, and the elevation factor possesses the highest importance through the Mean Decrease in Gini (MDG) method. Finally, we estimated the area of the target tree species and analyzed the spatial distribution characteristics by overlay analysis of the Classification 3 result and topographic features (elevation, slope, and aspect). Our findings emphasize that topographic factors have a great influence on the distribution of forest resources and provide the basis for forest resource investigation.
Alongside geometric information accepted widely, airborne laser bathymetry (ALB) typically records the radiometric properties of sensed targets and assists with strip registration, ground ...(sediment)-type classification, and geometric modeling. Continuous intensity stripping frequently occurs in the ALB-recorded intensity images due to automatic gain control (AGC), an established circuit to compensate for echo power variations caused by distance changes. This situation is exacerbated by the delayed gain control response and inadequate adaptability to target. To address this issue, an adaptive local joint-weighted method for continuous intensity stripping correction is designed. In this method, an index-sharing mechanism is established between intensity, point cloud, emission angle, and return number for rapid localization and intervention of intensity. Unordered intensities are divided into scan line units based on the signal emission period, and subsequent stripped intensity range identification is achieved by assessing the intensity difference between adjacent scan lines. Neighboring scan lines and neighborhood intensities are weighted to jointly reset the stripped intensity values. In the new method, a bidirectional shifting strategy is implemented to attenuate the degradation of the intensity correction accuracy due to the gradual accumulation of correction defects. From the two measurement missions using the ALB, it is concluded that the stripping intensity is mainly distributed in the region of maximum water depth detectable by the sensor and areas with mixed high and low returns. Compared with the original intensity, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the corrected intensity decreased by 35% and 40%, respectively, and the variation coefficient of the stripped intensity and the neighboring intensity decreased from 0.4 to 0.18.
High-resolution remote sensing images have the advantage of timeliness, and they can display feature information in more detail. Deep learning embodies its unique characteristics in land cover ...classification, target recognition, and other fields, which can automatically learn the in-depth feature information of images and make accurate classification decisions. However, when deep learning models extract high-dimensional abstract feature information, they often ignore and lose part of the underlying features essential for classification accuracy. This article proposes a dual-channel fully convolutional network (D-FCN), whose two channels, respectively, take image data and low-level features such as color, texture, and shape as the different input data to combine the underlying features with high-dimensional abstract features. To reduce the complexity of the model, we add a large number of skip connections between the model and make full use of the advantages of weight sharing and local connections to connect spatial context information. We used multifeature information as the model input and compared and analyzed the impact of different features on the land cover classification accuracy, and finally obtained the most suitable combination of multifeature information. In addition, we provide a small-scale land cover classification dataset with labels to verify the applicability and transferability of the D-FCN, and use the optimal combination of multifeature information to conduct comparative experiments on the small-scale dataset. The experimental results show that D-FCN has outstanding applicability and transferability. Compared with other state-of-the-art models, D-FCN has a more challenging performance and greatly reduces model complexity.
•ALB interference intensity is caused by invalid waveforms, oversaturated waveforms, and multiple echo intensities resulting from repetitive waveforms.•The waveform classification refinement and ...index sharing facilitate precise identification of interference intensity and integrated processing of waveform, terrain, and intensity data.•About 20.3% of total water-area waveforms are surface-reflected.•Compared to LAS files, the proposed method’s extracted intensity shows a maximum deviation reduction of 883 DN, STD of 350 DN, and MSE of 310 DN.
Alongside widely accepted geometric information, airborne laser bathymetry (ALB) typically captures the temporal profile sample (waveforms) of both the emitted laser pulses and their echoes. These waveforms provide radiometric properties (backscattering intensity) of sensed targets and assist with precise strip registration, classification of fine ground cover (sediment), and advanced geometric modelling. However, effective intensity data extraction is essential because the intensity provided by the LAS file is mixed with invalid signals, multiple echo intensity, and intermittent intensity variations caused by improper interference of automatic gain control, making it unusable for direct analysis. To address this issue, a flexible index sharing mechanism between waveform, coordinate, and intensity data is constructed for interference intensity tracking. A novel waveform classification-based approach is proposed to efficiently extract ALB intensity by dividing waveform data into six categories using morphological differences and topographic data of waveforms. To ensure accurate analysis, duplicate and invalid waveforms are eliminated, leaving only genuine intensity readings. Additionally, a triple spline fit is employed to restore oversaturated waveform segments that were previously suppressed due to exceeding the device's maximum measurable limit. To address the problem of mixing multiple return intensities, waveforms are decomposed using various decomposition models. This approach ensures that different waveform categories retain their respective varying return intensities. The approach is then tested on an ALB dataset collected using Optech Aquarius around Yuanzhi Island in the South China Sea. The results demonstrate a considerable advancement in data quality when compared to LAS file intensity products with a reduction in maximum deviation of 883 digital number, DN, standard deviation of 350 DN, and mean absolute error of 310 DN.
PDF
