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Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence
Permafrost is prevalent over the Qinghai–Tibet Plateau (QTP), but mapping its distribution is challenging due to the limited availability of ground‐truth data sets and strong spatial heterogeneity in the region. Based on a recently developed inventory of permafrost presence or absence from 1475 in s...
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Published in: | Permafrost and periglacial processes 2019-07, Vol.30 (3), p.178-194 |
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description | Permafrost is prevalent over the Qinghai–Tibet Plateau (QTP), but mapping its distribution is challenging due to the limited availability of ground‐truth data sets and strong spatial heterogeneity in the region. Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multiple controlling variables, including near‐surface air temperature downscaled from re‐analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to available existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5–65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%. |
doi_str_mv | 10.1002/ppp.2006 |
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Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multiple controlling variables, including near‐surface air temperature downscaled from re‐analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to available existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5–65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.</description><identifier>ISSN: 1045-6740</identifier><identifier>EISSN: 1099-1530</identifier><identifier>DOI: 10.1002/ppp.2006</identifier><language>eng</language><publisher>Chichester: Wiley Subscription Services, Inc</publisher><subject>Air temperature ; Distribution ; Glacial lakes ; Glaciers ; Heterogeneity ; In situ measurement ; Lakes ; Mapping ; Mathematical models ; mountians ; Patchiness ; Permafrost ; Permafrost distribution ; permafrost zonation index ; Plant cover ; Qinghai‐Tibet Plateau ; Remote sensing ; Snow cover ; Spatial data ; Spatial heterogeneity ; Spatial variability ; Spatial variations ; Statistical analysis ; Statistical methods ; Statistical models ; Surface-air temperature relationships ; Vegetation cover ; Zonation</subject><ispartof>Permafrost and periglacial processes, 2019-07, Vol.30 (3), p.178-194</ispartof><rights>2019 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3166-49a9fd6b4e677a60d8b272b1e04bc7096fa09c0bb77dd3fb0f1c237d19ced33d3</citedby><cites>FETCH-LOGICAL-a3166-49a9fd6b4e677a60d8b272b1e04bc7096fa09c0bb77dd3fb0f1c237d19ced33d3</cites><orcidid>0000-0003-2473-2276 ; 0000-0002-7965-0975</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Cao, Bin</creatorcontrib><creatorcontrib>Zhang, Tingjun</creatorcontrib><creatorcontrib>Wu, Qingbai</creatorcontrib><creatorcontrib>Sheng, Yu</creatorcontrib><creatorcontrib>Zhao, Lin</creatorcontrib><creatorcontrib>Zou, Defu</creatorcontrib><title>Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence</title><title>Permafrost and periglacial processes</title><description>Permafrost is prevalent over the Qinghai–Tibet Plateau (QTP), but mapping its distribution is challenging due to the limited availability of ground‐truth data sets and strong spatial heterogeneity in the region. Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multiple controlling variables, including near‐surface air temperature downscaled from re‐analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to available existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5–65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.</description><subject>Air temperature</subject><subject>Distribution</subject><subject>Glacial lakes</subject><subject>Glaciers</subject><subject>Heterogeneity</subject><subject>In situ measurement</subject><subject>Lakes</subject><subject>Mapping</subject><subject>Mathematical models</subject><subject>mountians</subject><subject>Patchiness</subject><subject>Permafrost</subject><subject>Permafrost distribution</subject><subject>permafrost zonation index</subject><subject>Plant cover</subject><subject>Qinghai‐Tibet Plateau</subject><subject>Remote sensing</subject><subject>Snow cover</subject><subject>Spatial data</subject><subject>Spatial heterogeneity</subject><subject>Spatial variability</subject><subject>Spatial variations</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical models</subject><subject>Surface-air temperature relationships</subject><subject>Vegetation cover</subject><subject>Zonation</subject><issn>1045-6740</issn><issn>1099-1530</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kM9KAzEQh4MoWKvgIwS8eNk62WyT7lGK_6DgCvUcks3Epmx312RbrSffwTf0Sdxar57mx_DxG-Yj5JzBiAGkV23bjlIAcUAGDPI8YWMOh7ucjRMhMzgmJzEuAWDCWTYgWGBYaRea2NGPptadb2rqa4vvdKVbqmtLY9dvY-fLSJsNBtotkD75-mWh_ffn19wb7GhR6Q71mhod0dK-wnmsLMWNt1iXeEqOnK4inv3NIXm-vZlP75PZ493D9HqWaM6ESLJc584Kk6GQUguwE5PK1DCEzJQScuE05CUYI6W13BlwrEy5tCwv0XJu-ZBc7Hvb0LyuMXZq2axD3Z9Uad80kSBA9NTlnir7t2NAp9rgVzpsFQO1k6h6iWonsUeTPfrmK9z-y6miKH75H_90dQg</recordid><startdate>201907</startdate><enddate>201907</enddate><creator>Cao, Bin</creator><creator>Zhang, Tingjun</creator><creator>Wu, Qingbai</creator><creator>Sheng, Yu</creator><creator>Zhao, Lin</creator><creator>Zou, Defu</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0003-2473-2276</orcidid><orcidid>https://orcid.org/0000-0002-7965-0975</orcidid></search><sort><creationdate>201907</creationdate><title>Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence</title><author>Cao, Bin ; Zhang, Tingjun ; Wu, Qingbai ; Sheng, Yu ; Zhao, Lin ; Zou, Defu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3166-49a9fd6b4e677a60d8b272b1e04bc7096fa09c0bb77dd3fb0f1c237d19ced33d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Air temperature</topic><topic>Distribution</topic><topic>Glacial lakes</topic><topic>Glaciers</topic><topic>Heterogeneity</topic><topic>In situ measurement</topic><topic>Lakes</topic><topic>Mapping</topic><topic>Mathematical models</topic><topic>mountians</topic><topic>Patchiness</topic><topic>Permafrost</topic><topic>Permafrost distribution</topic><topic>permafrost zonation index</topic><topic>Plant cover</topic><topic>Qinghai‐Tibet Plateau</topic><topic>Remote sensing</topic><topic>Snow cover</topic><topic>Spatial data</topic><topic>Spatial heterogeneity</topic><topic>Spatial variability</topic><topic>Spatial variations</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical models</topic><topic>Surface-air temperature relationships</topic><topic>Vegetation cover</topic><topic>Zonation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cao, Bin</creatorcontrib><creatorcontrib>Zhang, Tingjun</creatorcontrib><creatorcontrib>Wu, Qingbai</creatorcontrib><creatorcontrib>Sheng, Yu</creatorcontrib><creatorcontrib>Zhao, Lin</creatorcontrib><creatorcontrib>Zou, Defu</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Permafrost and periglacial processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cao, Bin</au><au>Zhang, Tingjun</au><au>Wu, Qingbai</au><au>Sheng, Yu</au><au>Zhao, Lin</au><au>Zou, Defu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence</atitle><jtitle>Permafrost and periglacial processes</jtitle><date>2019-07</date><risdate>2019</risdate><volume>30</volume><issue>3</issue><spage>178</spage><epage>194</epage><pages>178-194</pages><issn>1045-6740</issn><eissn>1099-1530</eissn><abstract>Permafrost is prevalent over the Qinghai–Tibet Plateau (QTP), but mapping its distribution is challenging due to the limited availability of ground‐truth data sets and strong spatial heterogeneity in the region. Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multiple controlling variables, including near‐surface air temperature downscaled from re‐analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to available existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5–65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.</abstract><cop>Chichester</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/ppp.2006</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0003-2473-2276</orcidid><orcidid>https://orcid.org/0000-0002-7965-0975</orcidid></addata></record> |
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subjects | Air temperature Distribution Glacial lakes Glaciers Heterogeneity In situ measurement Lakes Mapping Mathematical models mountians Patchiness Permafrost Permafrost distribution permafrost zonation index Plant cover Qinghai‐Tibet Plateau Remote sensing Snow cover Spatial data Spatial heterogeneity Spatial variability Spatial variations Statistical analysis Statistical methods Statistical models Surface-air temperature relationships Vegetation cover Zonation |
title | Permafrost zonation index map and statistics over the Qinghai–Tibet Plateau based on field evidence |
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