Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Detecting Long-Term Deformation of a Loess Landslide from the Phase and Amplitude of Satellite SAR Images: A Retrospective Analysis for the Closure of a Tunnel Event

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.

Hydrological and mechanical properties of loess landslides under different vegetation restoration types

Loess landslide is a common geological disaster in northern Shaanxi, which seriously affects people's life and property safety and social and economic development. The research on vegetation restoration types and hydrological and mechanical properties of loess landslides can provide basic data support for landslide stability prediction, and further provide reference for landslide prevention and treatment. In the present study, the loess landslide point of Zhang Zi Gou in Gan Quan County, Yan’an City was taken as the research object. On the basis of the existing natural condition data, the basic physical and mechanical properties and hydrological characteristics were obtained by collecting field landslide soil samples for indoor experimental analysis. The indoor analysis shows that the landslide is mainly distributed in dry land, medium coverage and low coverage grassland, indicating that the surface vegetation coverage can affect the stability of landslide. The worse the vegetation coverage, the more landslides occur. The void ratio and porosity of landslide soil decrease with the increase of dry density. The cohesion of natural soil is obviously higher than that of saturated soil, and the internal friction angle of natural soil is slightly lower than that of saturated soil. In general, due to the influence of water content, the shear strength of natural soil samples is higher than that of saturated soil samples. Therefore, in order to improve the accuracy of prediction and early warning system, it is necessary to consider the response of hydrological and mechanical properties of loess to vegetation restoration. The results provide basic data support for the establishment of loess landslide stability prediction system and provide reference for geological disaster management. Bangladesh J. Bot. 50(3): 795-801, 2021 (September) Special

The Promotion Mechanism of Frozen Stagnant Water on the Sliding in the Loess Landslide Zone of Heifangtai

Freeze-thaw cycles can significantly change the hydrologic and thermal state of slopes in cold regions and affect their stability. Landslides occur continuously in the slip area of seasonally frozen soil area during the freezing period. The freeze-thaw action and the difference in the characteristics of the underlying surface of the slope are important factors inducing landslides. Taking Heifangtai slope in Gansu Province as an example, the freezing-thawing characteristics of the slope surfaces under different underlying surface conditions were analyzed by field monitoring. A thermohydromechanical coupling model was established to reconstruct the frozen stagnant water process of the Heifangtai landslide zone, and the impact of freeze-thaw action on the loess landslide zone was studied. The results show that differences in the underlying surface led to different freezing-thawing characteristics between the unsaturated area and the groundwater overflow zone. During the freezing period, the soil freezing depth was greater, and the freezing duration was longer in the unsaturated area. The frozen stagnant water effect of the Heifangtai loess landslide zone is obvious. The maximum difference in the groundwater level between February and August could reach nearly 1 m. Meanwhile, the frozen stagnant water process of the Heifangtai landslide zone has a slip-promoting action on the slope. The factor of safety declined during the freezing period and increased during the thawing period. It reached a minimum of 1.42 in February.

Applicability of Susceptibility Model for Rock and Loess Earthquake Landslides in the Eastern Tibetan Plateau

It is crucial to explore a suitable landslide susceptibility model with an excellent prediction capability for rapid evaluation and disaster relief in seismic regions with different lithological features. In this study, we selected two typical seismic events, the Jiuzhaigou and Minxian earthquakes, which occurred in the Alpine karst and loess regions, respectively. Eight influencing factors and five models were chosen to calculate the susceptibility of landslide, including the information (I) model, certainty factor (CF) model, logistic regression (LR) model, I + LR coupling model, and CF + LR coupling model. Then, the accuracy and the landslide susceptibility distribution of these models were assessed by the area under curve (AUC) and distribution criteria. Finally, the model with high accuracy and good applicability for the rock landslide or loess landslide regions was optimized. Our results showed that the accuracy of the coupling model is higher than that of the single models. Except for the LR model, the landslide susceptibility distribution for the above-mentioned models is consistent with universal cognition. The coupling models are generally better than their single models. Among them, the I + LR model can obtain the best comprehensive results for assessing the distribution and accuracy of both rock and loess landslide susceptibility, which is helpful for disaster relief and policy-making, and it can also provide useful scientific data for post-seismic reconstruction and restoration.