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 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.

Initiation processes for run-off generated debris flows in the Wenchuan earthquake area of China

Geomorphology ◽  
2016 ◽  
Vol 253 ◽  
pp. 468-477 ◽  
Author(s):  
W. Hu ◽  
X.J. Dong ◽  
Q. Xu ◽  
G.H. Wang ◽  
T.W.J. van Asch ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Debris Flows ◽  
The Wenchuan Earthquake ◽  
Run Off ◽  
Earthquake Area

Assessment of spatio-temporal variations in vegetation recovery after the Wenchuan earthquake using Landsat data

2013 ◽  
Vol 70 (2) ◽  
pp. 1309-1326 ◽  
Author(s):  
Quanjun Jiao ◽  
Bing Zhang ◽  
Liangyun Liu ◽  
Zhenwang Li ◽  
Yuemin Yue ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Temporal Variations ◽  
Vegetation Recovery ◽  
Landsat Data ◽  
The Wenchuan Earthquake ◽  
Spatio Temporal

Discussion on the Reconstruction of the Wenchuan Earthquake-Damaged Ecosystems

2012 ◽  
Vol 209-211 ◽  
pp. 1668-1673
Author(s):  
Xiao Fei Jing ◽  
Yu Long Chen ◽  
Hong Chen ◽  
Zhi Yong Cai
Keyword(s):  
Wenchuan Earthquake ◽  
Cost Effective ◽  
Recovery Process ◽  
Future Research ◽  
Natural Ecosystems ◽  
Ecosystem Recovery ◽  
The Wenchuan Earthquake ◽  
Richter Scale ◽  
Research Questions ◽  
Made In

The Wenchuan Earthquake of 8.0 magnitude at Richter scale hit large region of Sichuan, China on May 12, 2008. It was a terrible disaster that caused great casualties and serious damages, and largely destroyed natural mountainous ecosystems in the earthquake-hit areas. Since the restoration of those damaged mountainous ecosystems plays an important role in ecological security in this region, it is argued that the reconstruction of those earthquake-damaged ecosystems should be given greater attention before and during the ecological recovery process. Yet, to date, little effort has been made to review and summarize the major unresolved questions relating to the ecosystem recovery after the earthquake. Focusing on the priorities for future research in this subject, based on the study and comparison of literatures we identified and examined four important research questions that need to be addressed, even two years after the earthquake. All these questions need to be resolved before significant progress can be made in habilitating the damaged natural ecosystems, and perhaps more importantly, the results provide very useful information for cost-effective restoration decision making and policy planning for the earthquake-hit areas.

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