RAPID EXTRACTION OF LANDSLIDE AND SPATIAL DISTRIBUTION ANALYSIS AFTER JIUZHAIGOU Ms7.0 EARTHQUAKE BASED ON UAV IMAGES

Abstract

ABSTRACT:Jiuzhaigou earthquake led to the collapse of the mountains and formed lots of landslides in Jiuzhaigou scenic spot and surrounding roads which caused road blockage and serious ecological damage. Due to the urgency of the rescue, the authors carried unmanned aerial vehicle (UAV) and entered the disaster area as early as August 9 to obtain the aerial images near the epicenter. On the basis of summarizing the earthquake landslides characteristics in aerial images, by using the object-oriented analysis method, landslides image objects were obtained by multi-scale segmentation, and the feature rule set of each level was automatically built by SEaTH (Separability and Thresholds) algorithm to realize the rapid landslide extraction. Compared with visual interpretation, object-oriented automatic landslides extraction method achieved an accuracy of 94.3%. The spatial distribution of the earthquake landslide had a significant positive correlation with slope and relief and had a negative correlation with the roughness, but no obvious correlation with the aspect. The relationship between the landslide and the aspect was not found and the probable reason may be that the distance between the study area and the seismogenic fault was too far away. This work provided technical support for the earthquake field emergency, earthquake landslide prediction and disaster loss assessment.

Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

“…Object-oriented classification technology segments homogeneous images and collects adjacent pixels as analysis objects, and uses high-resolution and multiple-spectral data to conduct high-precision classification [21]. Many scholars used the object-oriented classification method to extract landslides automatically, and the accuracy is higher than the traditional statistical analysis and machine learning methods [5,[22][23][24][25][26][27][28][29][30]. In order to improve the accuracy of automatic extraction, some scholars combined traditional statistical analysis and machine learning methods to optimize the object-oriented method [31][32][33][34][35][36].…”

Extraction of Scattered Small-Scale Landslides Distribution by Object-Based Classification Using Optical High-Resolution Satellite Images

“…In addition, some effects of objectbased classification on landslide extraction using high resolution satellite images have been investigated (Helno et al, 2016;Hölbling et al, 2015;Lu et al, 2011;Martha et al, 2011;Martha and Kerl, 2010). Recently, object-based classification has been applied to landslide observation images by UAV (Jiao et al, 2018;Karantanellis et al, 2019;Rau et al, 2011). While many studies have investigated large-scale landslides using object-based classification, this method has seldom been applied for extracting the distribution of small-scale landslides that frequently occur in mountainous regions with complex terrain.…”