Spatial Analyses and Susceptibility Modeling of Thermokarst Lakes in Permafrost Landscapes along the Qinghai–Tibet Engineering Corridor

• Published in: Remote Sensing
• Main Authors: Guoan Yin, Jing Luo, Fujun Niu, Fujun Zhou, Xianglian Meng, Zhanju Lin, Minghao Liu
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: geomorphology; environment; remote sensing; machine learning; susceptibility map; Science; Q; Ice; permafrost; Thermokarst
• Online Access: https://doi.org/10.3390/rs13101974; https://doaj.org/article/4a160dee4e76402d8e170c6df0a5ae1e

Thermokarst lakes (TLs) caused by the thaw of massive ground ice in ice-rich permafrost landscapes are increasing and have strong impacts on the hydro–ecological environment and human infrastructure on the Qinghai–Tibet Plateau (QTP), however, its spatial distribution characteristics and environmental controls have not been underrepresented at the local scale. Here, we analyzed the spatial distribution of small TLs along the Qinghai–Tibet Engineering Corridor (QTEC) based on high-resolution (up to 2.0 m) satellite images. The TLs gathered in the plains and upland plateau and covered 8.3% of the QTEC land. We deployed a random-frost method to investigate the suitable environmental conditions for TLs. Climate including summer rainfall and the air temperature was the most important factor controlling the TL distribution, followed by topography and soil characteristics that affected the ground ice content. TL susceptibility was mapped based on the combinations of climate, soil, and topography grid data. On average, around 20% of the QTEC area was in a high to very-high-susceptibility zone that is likely to develop TLs in response to climate change. This study improved the understanding of controlling factors for TL development but also provided insights into the conditions of massive ground ice and was helpful to assess the impacts of climate change on ecosystem processes and engineering design.