Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas

Abstract

The Himalayan range is one of the most tectonically active mountain ranges on Earth. The Jilong Valley is a deeply-dissected canyon in the Chinese Himalayas in Tibet, and in this valley, landslide hazard and risk have increased significantly in response to active crustal movements, intense rainfall and ever-increasing human interference. Landslide susceptibility assessment in the valley is fundamental for risk mitigation and to inform land use and planning but remains a challenge owing to the inaccessible high altitude and incomplete landslide inventory. In addition, because of sustained glacial erosion, weathering, and denudation, there is considerable uncertainty in the use of remote sensing interpretation of landslides in high-altitude terrain. In this paper, therefore, a systematic approach for landslide susceptibility assessment is proposed, combining the interpretations from remotely sensed dataset and both heuristic and statistical susceptibility models to overcome the problem of the limited spatial coverage of landslide data and uncertainty in landslide interpretation. The main steps include: 1) landslide field investigation and interpretation and the compilation of a preliminary landslide inventory, including certain and probable landslides, 2) knowledge-driven identification of landslide-prone areas based on the heuristic model, 3) classification and quantification of the uncertainty of probable landslides through heuristic landslide-prone areas mapping and the production of a revised landslide inventory, and 4) data-driven susceptibility assessment using a statistical model. In the statistical model, the landslide samples have multivalent dependent variables between 0 and 1. Therefore, a multinomial statistical classifier and multiclass Receiver Operation Characteristic curves are needed for model calibration and validation. The statistical susceptibility mapping showed good performance in the study area, with an average AUC of 0.867, which is a significant improvement over the heuristic model. The methodology presented can be used for landslide susceptibility mapping in data-scarce environments, not only in the Himalayan region but also in other mountainous areas where there is a lack of data on past landslide occurrences.