How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

• Published in: Land (Basel) 2023-03, Vol.12 (3), p.681
• Main Authors: Huang, Yu, Zhang, Jianqiang, Zhang, Lili, Ming, Zaiyang, He, Haiqing, Chen, Rong, Ge, Yonggang, Liu, Rongkun
• Format: Article
• Language: English
• Subjects: Accuracy; Adam optimizer; Aerial surveys; Algorithms; Artificial neural networks; China; Deep learning; Disaster recovery; Disaster relief; Earth resources technology satellites; Earthquakes; fully convolutional neural network; Geology; Image reconstruction; Investigations; Landsat; Landsat satellites; landslide detection; Landslides; Landslides & mudslides; Luding earthquake; Machine learning; Maximum likelihood method; Neural networks; Remote sensing; remote sensing images of different resolutions; Satellite imagery; Satellites; Seismic activity; Seismology; Spatial discrimination; Spatial resolution; Unmanned aerial vehicles; Visual fields
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land12030681

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m2 could not be detected from a Landsat 8 satellite image.