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Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion
As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drai...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (18), p.4561 |
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description | As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change. |
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This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs15184561</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air temperature ; Algorithms ; Arctic greening ; Basins ; Carbon ; Change detection ; Climate ; Climate change ; Drainage ; Drainage basins ; drained lake basins ; Earth resources technology satellites ; Environmental assessment ; Greening ; Heterogeneity ; Hydrology ; Lake basins ; Lakes ; Land area ; Landsat ; Landsat satellites ; Machine learning ; Marginal seas ; Permafrost ; Quantitative analysis ; Regions ; Remote sensing ; Rivers ; Satellite imagery ; Sediments ; Soil erosion ; Soil temperature ; Spatial heterogeneity ; Surface water ; Sustainable living ; Taiga & tundra ; Topography ; Trend analysis ; Trends ; Tundra ; Vegetation ; Vegetation changes ; Vegetation growth ; Yamal–Gydan tundra ecoregion</subject><ispartof>Remote sensing (Basel, Switzerland), 2023-09, Vol.15 (18), p.4561</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-26149be9ec95a725bc38b2bafd1338777de45edebb43403d795bbdb0b40a66c13</cites><orcidid>0000-0001-8912-8729 ; 0000-0001-6910-6565</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2869610836/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2869610836?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Liu, Aobo</creatorcontrib><creatorcontrib>Chen, Yating</creatorcontrib><creatorcontrib>Cheng, Xiao</creatorcontrib><title>Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion</title><title>Remote sensing (Basel, Switzerland)</title><description>As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change.</description><subject>Air temperature</subject><subject>Algorithms</subject><subject>Arctic greening</subject><subject>Basins</subject><subject>Carbon</subject><subject>Change detection</subject><subject>Climate</subject><subject>Climate change</subject><subject>Drainage</subject><subject>Drainage basins</subject><subject>drained lake basins</subject><subject>Earth resources technology satellites</subject><subject>Environmental assessment</subject><subject>Greening</subject><subject>Heterogeneity</subject><subject>Hydrology</subject><subject>Lake basins</subject><subject>Lakes</subject><subject>Land area</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Machine learning</subject><subject>Marginal seas</subject><subject>Permafrost</subject><subject>Quantitative analysis</subject><subject>Regions</subject><subject>Remote sensing</subject><subject>Rivers</subject><subject>Satellite imagery</subject><subject>Sediments</subject><subject>Soil erosion</subject><subject>Soil temperature</subject><subject>Spatial heterogeneity</subject><subject>Surface water</subject><subject>Sustainable living</subject><subject>Taiga & tundra</subject><subject>Topography</subject><subject>Trend analysis</subject><subject>Trends</subject><subject>Tundra</subject><subject>Vegetation</subject><subject>Vegetation changes</subject><subject>Vegetation growth</subject><subject>Yamal–Gydan tundra 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of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion</title><author>Liu, Aobo ; Chen, Yating ; Cheng, Xiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-26149be9ec95a725bc38b2bafd1338777de45edebb43403d795bbdb0b40a66c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Air temperature</topic><topic>Algorithms</topic><topic>Arctic greening</topic><topic>Basins</topic><topic>Carbon</topic><topic>Change detection</topic><topic>Climate</topic><topic>Climate change</topic><topic>Drainage</topic><topic>Drainage basins</topic><topic>drained lake basins</topic><topic>Earth resources technology satellites</topic><topic>Environmental assessment</topic><topic>Greening</topic><topic>Heterogeneity</topic><topic>Hydrology</topic><topic>Lake basins</topic><topic>Lakes</topic><topic>Land area</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Machine learning</topic><topic>Marginal seas</topic><topic>Permafrost</topic><topic>Quantitative analysis</topic><topic>Regions</topic><topic>Remote sensing</topic><topic>Rivers</topic><topic>Satellite imagery</topic><topic>Sediments</topic><topic>Soil erosion</topic><topic>Soil temperature</topic><topic>Spatial heterogeneity</topic><topic>Surface water</topic><topic>Sustainable living</topic><topic>Taiga & tundra</topic><topic>Topography</topic><topic>Trend analysis</topic><topic>Trends</topic><topic>Tundra</topic><topic>Vegetation</topic><topic>Vegetation changes</topic><topic>Vegetation growth</topic><topic>Yamal–Gydan tundra ecoregion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Aobo</creatorcontrib><creatorcontrib>Chen, Yating</creatorcontrib><creatorcontrib>Cheng, Xiao</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology 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Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Aobo</au><au>Chen, Yating</au><au>Cheng, Xiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2023-09-01</date><risdate>2023</risdate><volume>15</volume><issue>18</issue><spage>4561</spage><pages>4561-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs15184561</doi><orcidid>https://orcid.org/0000-0001-8912-8729</orcidid><orcidid>https://orcid.org/0000-0001-6910-6565</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air temperature Algorithms Arctic greening Basins Carbon Change detection Climate Climate change Drainage Drainage basins drained lake basins Earth resources technology satellites Environmental assessment Greening Heterogeneity Hydrology Lake basins Lakes Land area Landsat Landsat satellites Machine learning Marginal seas Permafrost Quantitative analysis Regions Remote sensing Rivers Satellite imagery Sediments Soil erosion Soil temperature Spatial heterogeneity Surface water Sustainable living Taiga & tundra Topography Trend analysis Trends Tundra Vegetation Vegetation changes Vegetation growth Yamal–Gydan tundra ecoregion |
title | Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion |
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