The extraction and analysis of Luding earthquake-induced landslide based on high-resolution optical satellite images

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Abstract

On September 5, 2022, a Ms 6.8 earthquake occurred in Luding County, Ganzi Prefecture, Sichuan Province, inducing numerous landslides. This study collected the pre- and post-earthquake images from the GF-2 and GF-6 satellites, as well as the DEM data of Luding. Then, using the object-oriented method, the stepwise optimization multi-scale segmentation method, and the nearest neighbor classification method, this study extracted the landslide information according to the spectrum, thematic index, geometric texture, and topographic features of the objects in the experimental area. The overall identification accuracy of pre- and post-earthquake landslides was 92.3% and 95.4%, respectively. The comprehensive analysis of the distribution of pre- and post-earthquake landslide landslides shows that 23.91 km² of new landslides were induced by the earthquake. This study summarized the distribution characteristics of post-earthquake landslides through the spatial statistical analysis of seven topographic factors. The results are as follows: ① The post-earthquake landslides were mainly affected by the Xianshuihe fault zone, and they show a banded distribution along rivers and a lamellar, dense distribution along the hillsides and valleys near the fault zone; ② Compared with the historical landslides, the new landslides have a relatively stable elevation range and a large slope range. Moreover, there is a significantly negative correlation between the area of the post-earthquake landslides and the surface roughness.

Keywords optical remote sensing object-oriented landslide information extraction M_s 6.8 earthquake in Luding County

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Issue Date: 20 March 2023

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	Yu ZHANG
	Dongping MING
	Wenyi ZHAO
	Lu XU
	Zhi ZHAO
	Ran LIU

Cite this article:

Yu ZHANG,Dongping MING,Wenyi ZHAO, et al. The extraction and analysis of Luding earthquake-induced landslide based on high-resolution optical satellite images[J]. Remote Sensing for Natural Resources, 2023, 35(1): 161-170.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022434 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/161

Fig.1 Location of the study area

Fig.2 Flowchart of research methods

Fig.3 Variance and ROC variation curve at different scales

Fig.4 Post-processing results of spectral difference segmentation with different parameters

Fig.5 Landslide extraction results of first research area

Fig.6 Landslide extraction results of second research area

Fig.7 Dual-temporal landslide extraction result

Fig.8 Post-earthquake landslide distribution statistics with terrain factors

Fig.9 Landslide distribution distance from seismic fault zones

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