Abstract
The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.
Highlights
This paper introduces a confusion matrix to calculate user’s accuracy (UA), commission error (CE), omission error (OE), producer’s accuracy (PA), overall accuracy (OA), and the Kappa coefficient to evaluate the accuracy of this method (Table 1)
The avalanches, inducing earthquake-triggered landslides was accompanied by the generation of rock factors are mainly the deformation of area, the ground and the loosening of to which moved from adue hightoaltitude to the valley leavingsurface a grayish-white tone similar mountain deposits caused by seismic motion; inertial force of thetoslope body changes, the landslide body on the unmanned aerial vehicle (UAV)
Previous statistical studies have shown that earthquake-induced geological disasters mostly occur in the range of 20~50◦ [43]
Summary
Liu et al [29] added three new layers (i.e., digital surface model, slope gradient, and slope aspect) to limit the distribution of landslides based on the original three RGB bands, so as to better distinguish the impact of roads and villages on landslides These methods have achieved results that are certain and promoted the development of accurate landslide-identification research. This study uses high-resolution UAV remote-sensing imagery and proposes a support vector machine/UAV imagery method for automatic landslide identification that incorporates pre-seismic image-recognition results for masking This method selects roads, villages, and landslides as the feature vectors of the samples; constructs a support vector machine automatic-recognition method that mixes imagery features such as spectrum, color tone, and texture, etc.; extracts distribution information through imagery comparison before and after earthquake; eliminates the extraction errors caused by the interpretation results of roads and villages; and realizes the accurate identification of landslides. High-resolution digital elevation model (DEM) data and fault data are introduced to carry out statistical research on the spatial distribution characteristics of earthquake-triggered landslides, which provides important support for susceptibility and risk assessment
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