Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra

Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Aobo Liu, Yating Chen, Xiao Cheng
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2023
Subjects:
Online Access:https://doi.org/10.3390/rs15184396
id ftmdpi:oai:mdpi.com:/2072-4292/15/18/4396/
record_format openpolar
spelling ftmdpi:oai:mdpi.com:/2072-4292/15/18/4396/ 2023-10-09T21:49:06+02:00 Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra Aobo Liu Yating Chen Xiao Cheng agris 2023-09-07 application/pdf https://doi.org/10.3390/rs15184396 eng eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs15184396 https://creativecommons.org/licenses/by/4.0/ Remote Sensing Volume 15 Issue 18 Pages: 4396 thermokarst lakes lake drainage events remote sensing permafrost Arctic region Text 2023 ftmdpi https://doi.org/10.3390/rs15184396 2023-09-10T23:54:47Z Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change. Text Arctic Climate change permafrost Thermokarst Tundra MDPI Open Access Publishing Arctic Remote Sensing 15 18 4396
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic thermokarst lakes
lake drainage events
remote sensing
permafrost
Arctic region
spellingShingle thermokarst lakes
lake drainage events
remote sensing
permafrost
Arctic region
Aobo Liu
Yating Chen
Xiao Cheng
Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
topic_facet thermokarst lakes
lake drainage events
remote sensing
permafrost
Arctic region
description Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change.
format Text
author Aobo Liu
Yating Chen
Xiao Cheng
author_facet Aobo Liu
Yating Chen
Xiao Cheng
author_sort Aobo Liu
title Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
title_short Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
title_full Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
title_fullStr Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
title_full_unstemmed Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
title_sort monitoring thermokarst lake drainage dynamics in northeast siberian coastal tundra
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url https://doi.org/10.3390/rs15184396
op_coverage agris
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
permafrost
Thermokarst
Tundra
genre_facet Arctic
Climate change
permafrost
Thermokarst
Tundra
op_source Remote Sensing
Volume 15
Issue 18
Pages: 4396
op_relation Environmental Remote Sensing
https://dx.doi.org/10.3390/rs15184396
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs15184396
container_title Remote Sensing
container_volume 15
container_issue 18
container_start_page 4396
_version_ 1779312137682812928