气候变化领域集成服务门户(CCPortal): Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai-Tibet Plateau Using Machine Learning Methods

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DOI	10.3390/rs15133331
	Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai-Tibet Plateau Using Machine Learning Methods
	Wang, Rui; Guo, Lanlan; Yang, Yuting; Zheng, Hao; Liu, Lianyou; Jia, Hong; Diao, Baijian; Liu, Jifu
发表日期	2023
EISSN	2072-4292
卷号	15 期号:13
英文摘要	s The rapidly warming climate on the Qinghai-Tibet Plateau (QTP) leads to permafrost degradation, and the thawing of ice-rich permafrost induces land subsidence to facilitate the development of thermokarst lakes. Thermokarst lakes exacerbate the instability of permafrost, which significantly alters regional geomorphology and hydrology, affecting biogeochemical cycles. However, the spatial distribution and future changes in thermokarst lakes have rarely been assessed at large scales. In this study, we combined various conditioning factors and an inventory of thermokarst lakes to assess the spatial distribution of susceptibility maps using machine-learning algorithms. The results showed that the extremely randomized trees (EXT) performed the best in the susceptibility modeling process, followed by random forest (RF) and logistic regression (LR). According to the assessment based on EXT, the high- and very high-susceptibility area of the present (2000-2016) susceptibility map was 196,222 km(2), covering 19.67% of the permafrost region of the QTP. In the future (the 2070s), the area of the susceptibility map was predicted to shrink significantly under various representative concentration pathway scenarios (RCPs). The susceptibility map area would be reduced to 37.06% of the present area in RCP 8.5. This paper also performed correlation and importance analysis on the conditioning factors and thermokarst lakes, which indicated that thermokarst lakes tended to form in areas with flat topography and high soil moisture. The uncertainty of the susceptibility map was further assessed by the coefficient of variation (CV). Our results demonstrate a way to study the spatial distribution of thermokarst lakes at the QTP scale and provide a scientific basis for understanding thermokarst processes in response to climate change.
关键词	thermokarst lake machine learning susceptibility map permafrost degradation Qinghai-Tibet Plateau
英文关键词	NORTHERN-HEMISPHERE; ACTIVE LAYER; BEILUHE; MAP; TERRAIN; REGIME; BASIN; RIVER; THAW; SOIL
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001028168600001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282841
作者单位	Beijing Normal University; Beijing Normal University; Beijing Normal University; Lanzhou Jiaotong University
推荐引用方式 GB/T 7714	Wang, Rui,Guo, Lanlan,Yang, Yuting,et al. Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai-Tibet Plateau Using Machine Learning Methods[J],2023,15(13).
APA	Wang, Rui.,Guo, Lanlan.,Yang, Yuting.,Zheng, Hao.,Liu, Lianyou.,...&Liu, Jifu.(2023).Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai-Tibet Plateau Using Machine Learning Methods.REMOTE SENSING,15(13).
MLA	Wang, Rui,et al."Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai-Tibet Plateau Using Machine Learning Methods".REMOTE SENSING 15.13(2023).