Impacts of Wenchuan Earthquake-Induced Landslides on Tree Organ Nutrients

2012 ◽  
Author(s):  
Song Cheng ◽  
Hui Yu ◽  
Gang Yang ◽  
Ren Yang ◽  
Yuxian Liu ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Tree Organ

Runoff and Soil Loss of a Typical Subtropical Forest Stricken by Wenchuan Earthquake

2013 ◽  
Vol 19 (5) ◽  
pp. 766-773
Author(s):  
Jinniu WANG ◽  
Geng SUN ◽  
Fusun SHI ◽  
Jiceng XU ◽  
Yan WU ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Soil Loss ◽  
Subtropical Forest ◽  
Runoff And Soil Loss

scholarly journals When Housing and Communities Were Delivered: A Case Study of Post-Wenchuan Earthquake Rural Reconstruction and Recovery

2021 ◽  
Vol 13 (14) ◽  
pp. 7629
Author(s):  
Haorui Wu
Keyword(s):  
Built Environment ◽  
Wenchuan Earthquake ◽  
Place Making ◽  
Sustainable Rural Development ◽  
Residential Communities ◽  
Social Settings ◽  
Disaster Survivors ◽  
Two Phases

This study contributes to an in-depth examination of how Wenchuan earthquake disaster survivors utilize intensive built environment reconstruction outcomes (housing and infrastructural systems) to facilitate their long-term social and economic recovery and sustainable rural development. Post-disaster recovery administered via top-down disaster management systems usually consists of two phases: a short-term, government-led reconstruction (STGLR) of the built environment and a long-term, survivor-led recovery (LTSLR) of human and social settings. However, current studies have been inadequate in examining how rural disaster survivors have adapted to their new government-provided housing or how communities conducted their long-term recovery efforts. This qualitative case study invited sixty rural disaster survivors to examine their place-making activities utilizing government-delivered, urban-style residential communities to support their long-term recovery. This study discovered that rural residents’ recovery activities successfully perpetuated their original rural lives and rebuilt social connections and networks both individually and collectively. However, they were only able to manage their agriculture-based livelihood recovery temporarily. This research suggests that engaging rural inhabitants’ place-making expertise and providing opportunities to improve their housing and communities would advance the long-term grassroots recovery of lives and livelihoods, achieving sustainable development.

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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