Abstract:
Objectives On 16 June 2024, Longyan City in Fujian Province, Eastern China experienced exceptionally heavy rainfalls, setting a 24 h record of 377.3 mm. The extreme rainfalls triggered numerous landslides, causing widespread damage to residential homes and disrupting transportation in several areas, which attracted significant public attention. Timely acquisition of landslide inventories, along with a detailed understanding of their spatial distribution and controlling factors, is crucial for informing post-disaster emergency response and recovery efforts.
Methods Satellite optical remote sensing imagery and digital elevation model in the affected region were used in conjunction with the ResU-Net model to rapidly and accurately identify the landslides triggered by the extreme rainfalls. A spatial analysis of the landslide distribution was conducted by integrating factors such as topography, geomorphology, and human activities. Additionally, an optimal parameters-based geographical detector model was employed to quantitatively analyze the primary controlling factors behind the landslides and the interaction effects between dual controlling factors.
Results The extreme rainfall event triggered at least 3 951 landslides, covering a total area of approximately 21.30 km². Most landslides were small in scale, with Shanghang and Wuping counties being the most severely affected, showing a clustered spatial distribution. The spatial analysis revealed that 44% of the landslides occurred at elevations between 200-300 m, with landslide frequency increasing as the distance to roads and rivers decreased. Elevation, distance to roads, and distance to rivers were identified as the primary controlling factors for the landslides. Interaction effects between controlling factors were found to enhance landslide occurrence, with the interaction between elevation and land cover being particularly significant.
Conclusions This study provides a comprehensive inventory of landslides triggered by the extreme rainfall event in Longyan City, and identifies the primary controlling factors and spatial distribution patterns. The findings provide essential data for post-disaster emergency response, reconstruction planning, and risk assessment of potential secondary disasters.
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