Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods
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 re...
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ftmdpi:oai:mdpi.com:/2072-4292/15/13/3331/ 2023-08-20T04:07:07+02:00 Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods Rui Wang Lanlan Guo Yuting Yang Hao Zheng Lianyou Liu Hong Jia Baijian Diao Jifu Liu agris 2023-06-29 application/pdf https://doi.org/10.3390/rs15133331 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs15133331 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 13; Pages: 3331 thermokarst lake machine learning susceptibility map permafrost degradation Qinghai–Tibet Plateau Text 2023 ftmdpi https://doi.org/10.3390/rs15133331 2023-08-01T10:40:50Z 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 km2, 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. Text Ice permafrost Thermokarst MDPI Open Access Publishing Remote Sensing 15 13 3331 |
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MDPI Open Access Publishing |
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ftmdpi |
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English |
topic |
thermokarst lake machine learning susceptibility map permafrost degradation Qinghai–Tibet Plateau |
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thermokarst lake machine learning susceptibility map permafrost degradation Qinghai–Tibet Plateau Rui Wang Lanlan Guo Yuting Yang Hao Zheng Lianyou Liu Hong Jia Baijian Diao Jifu Liu Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
topic_facet |
thermokarst lake machine learning susceptibility map permafrost degradation Qinghai–Tibet Plateau |
description |
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 km2, 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. |
format |
Text |
author |
Rui Wang Lanlan Guo Yuting Yang Hao Zheng Lianyou Liu Hong Jia Baijian Diao Jifu Liu |
author_facet |
Rui Wang Lanlan Guo Yuting Yang Hao Zheng Lianyou Liu Hong Jia Baijian Diao Jifu Liu |
author_sort |
Rui Wang |
title |
Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
title_short |
Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
title_full |
Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
title_fullStr |
Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
title_full_unstemmed |
Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods |
title_sort |
thermokarst lake susceptibility assessment induced by permafrost degradation in the qinghai–tibet plateau using machine learning methods |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15133331 |
op_coverage |
agris |
genre |
Ice permafrost Thermokarst |
genre_facet |
Ice permafrost Thermokarst |
op_source |
Remote Sensing; Volume 15; Issue 13; Pages: 3331 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs15133331 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15133331 |
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Remote Sensing |
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15 |
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13 |
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3331 |
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1774718573355204608 |