Method on Early Identification of Low-Frequency Debris Flow Gullies along the Highways in the Chuanxi Plateau

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1183
• Main Authors: Hu, Guisheng, Huang, Hong, Tian, Shufeng, Rahman, Mahfuzur, Shen, Haowen, Yang, Zhiquan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Analysis; Arteries; colluvium deposits in a hollow region; Debris flow; Detritus; early identification; Earthquakes; Field investigations; Freeways; Geomorphology; Gullies; Highway construction; Highways; Identification systems; low-frequency debris flow; Machine learning; Methods; Mountain regions; Mountainous areas; Onsite; Remote sensing; Roads & highways; Roundness; roundness value; Safety factors; Stone; Topography; Vegetation; Watersheds; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051183

Low-frequency debris flows are characterized by strong concealment, high potential danger, and difficulty achieving an early warning; hence early identification of low-frequency debris flow gullies is crucial to mitigation. Here, an identification system for low-frequency debris flow gullies along the traffic arteries in the Chuanxi Plateau is proposed based on the identification and stability calculation of colluvium deposits in a hollow region (CDH) and the quantitative roundness analysis for the stones in a deposit fan. At first, for the watershed without a deposit fan, the CDH is identified and analyzed using the geomorphologic change point detection method combined with high-precision remote sensing images and field investigation. The watershed can be identified as a low-frequency debris flow gully with the safety factors (Fs) of all CDHs greater than 1. Then, the roundness of stones in the deposit fan is quantitatively analyzed. The watershed can also be identified as a low-frequency debris flow gully with the average roundness of the stones ranging from 0.30 to 0.41. Lastly, the identification system was tested and verified based on another ten watersheds along three traffic arteries. It shows that the method proposed in this paper has good applicability and high accuracy. Here we try to achieve the accurate early identification of low-frequency debris flow gullies by combining remote sensing interpretation and field investigation, which can provide theoretical support for predicting and mitigating debris flows in mountainous areas.