Spatial and temporal analyses of post-seismic landslide changes near the epicentre of the Wenchuan earthquake

Geomorphology ◽  
2017 ◽  
Vol 276 ◽  
pp. 8-15 ◽  
Author(s):  
Wentao Yang ◽  
Wenwen Qi ◽  
Ming Wang ◽  
Jianjun Zhang ◽  
Yan Zhang
Keyword(s):  
Wenchuan Earthquake ◽  
The Wenchuan Earthquake ◽  
Seismic Landslide ◽  
Spatial And Temporal Analyses

scholarly journals Discovering Vegetation Recovery and Landslide Activities in the Wenchuan Earthquake Area with Landsat Imagery

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5243
Author(s):  
Cheng Zhong ◽  
Chang Li ◽  
Peng Gao ◽  
Hui Li
Keyword(s):  
Wenchuan Earthquake ◽  
Landsat Imagery ◽  
Vegetation Recovery ◽  
Landsat Images ◽  
Ecosystem Recovery ◽  
Landslide Stability ◽  
The Wenchuan Earthquake ◽  
Mixed Pixels ◽  
Seismic Landslide ◽  
Earthquake Area

Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide vegetation recovery rate (VRR) with Landsat images and a (nearly) complete landslide inventory. Thirty thousand landslides that were larger than nine pixels were chosen for VRR analysis, to reduce the influence of mixed pixels and support detailed investigation within landslides. The study indicates that about 60% of landslide vegetation gets close to the pre-earthquake level in ten years and is expected to recover to the pre-earthquake level within 20 years. The vegetation recovery is significantly influenced by topographic factors, especially elevation and slope, while it is barely related to the distance to epicenter, fault ruptures, and rivers. This study checked and improved the knowledge of vegetation recovery and landslide stability in the area, based on a detailed investigation.

scholarly journals Evaluation of Seismic Landslide Susceptibility by Integrating Statistical Learning Model and Newmark Model—A Case Study of the Wenchuan Earthquake

2021 ◽  
Author(s):  
Zelang Miao ◽  
Minghui Pu ◽  
Yueguang He ◽  
Ke Li ◽  
Renfeng Peng ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Statistical Learning ◽  
Landslide Susceptibility ◽  
Learning Model ◽  
Difficult Problem ◽  
Landslide Inventory ◽  
Target Area ◽  
Loss Assessment ◽  
The Wenchuan Earthquake ◽  
Seismic Landslide

Whether it can quickly and effectively predict the susceptibility of regional earthquake landslides to achieve rapid rescue, loss assessment and post-disaster reconstruction has always been a difficult problem. However, the traditional high-precision evaluation of seismic landslide susceptibility often relies heavily on the complete or incomplete landslide inventory, which is poor in timeliness and cannot effectively evaluate the target area before or shortly after the earthquake. In most cases, the Newmark model relies on experts’ experience to select model parameters, therefore the evaluation result of this method is unstable and it lacks strong generalization ability. A fused model is proposed to classify the positive and negative training samples of the study area through the evaluation results of the Newmark model under the slope units, and it applies a variety of statistical learning models to evaluate the landslide susceptibility of the Wenchuan earthquake based on the classification results of the Newmark model. The results show that the evaluation of the statistical learning model fused with the Newmark model has higher accuracy. This method can overcome the inherent shortcomings of a single Newmark model to obtain better evaluation results without relying on obtaining the complete landslide inventory. Meanwhile, the model can be applied to quickly obtain the evaluation results of regional landslide susceptibility before or shortly after the earthquake, thereby effectively reducing human and economic losses caused by earthquake landslides.

Catastrophic debris flows triggered by the 20 August 2019 rainfall, a decade since the Wenchuan earthquake, China

Landslides ◽  
2021 ◽  
Author(s):  
Fan Yang ◽  
Xuanmei Fan ◽  
Srikrishnan Siva Subramanian ◽  
Xiangyang Dou ◽  
Junlin Xiong ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Debris Flows ◽  
The Wenchuan Earthquake

scholarly journals Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Remote Sensing Images ◽  
Machine Learning Methods ◽  
The Wenchuan Earthquake ◽  
Causative Factors ◽  
Multiple Structures ◽  
Sample Set ◽  
Larger Sample ◽  
Structure Layer ◽  
Earthquake Area

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.

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