Large-scale mapping of solifluction terraces in the southeastern Tibetan Plateau using high-resolution satellite images and deep learning

Abstract

Solifluction terraces are a topographic and sedimentary signature for understanding the mechanism of solifluction occurrence and the past climate (e.g., paleoclimate), and have also impacts on hillslope stability. Traditional field surveys and aerial remote sensing have been primarily employed to map solifluction terraces, but they have challenges in large-scale mapping, which results in little knowledge of spatial distribution of solifluction terraces in many areas of the world. In this study, we made a first attempt at large-scale mapping of solifluction terraces based on high-resolution satellite remote sensing images and deep learning in the Luhuo and Xinduqiao areas, located in the southeastern Tibetan Plateau. Enriched training samples were firstly generated through manually delineated 1057 solifluction terrace and non-solifluction terrace polygons in Luhuo and data augmentation. The DeepLabv3+ model (Chen et al., 2018) was then trained by these diverse and abundant samples, and employed to Gaofen-2 satellite images for extraction of solifluction terraces in both the Luhuo and Xinduqiao areas. The performance estimation showed that the F1-score, precision, and recall reached 91.15 %, 84.56 %, and 98.85 % in the Luhuo area, and 84.30 %, 84.90 %, and 83.71 % in the Xinduqiao area, respectively. The extracted results of solifluction terraces were also validated by field investigations in the Xinduqiao area. Moreover, we found that the solifluction terraces were mainly distributed around the slope angle of 21° and concentrated in the altitude range of 3000–3900 m, but the slope orientations were heterogeneous in both areas. Our results demonstrated that the combination of high-resolution satellite images and deep learning had a significant potential for mapping solifluction terraces at regional and global scales.