Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework
Grasslands are globally important for providing essential ecosystem services and maintaining ecological security. Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plat...
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Veröffentlicht in: | Sustainability 2022-05, Vol.14 (9), p.4912 |
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description | Grasslands are globally important for providing essential ecosystem services and maintaining ecological security. Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plateau (QTP) is considered high, but its spatial and temporal variations in response to human activities and climate change are not well understood. In this study, a comprehensive grassland vulnerability index (GVI), which includes natural factors (VNF), environmental disturbances (VED), and socioeconomic impacts (VSI), was developed by using the analytic hierarchy process (AHP), principal component analysis (PCA), and environmental vulnerability distance index (EVDI). Our results showed that the spatial distribution of GVI had obvious heterogeneity, decreasing from northwest to southeast; the regions with serious and extreme vulnerability were mainly concentrated in the north-western alpine steppe and desert steppe. From 2000 to 2018, GVI decreased from 0.61 in 2000 to 0.60 in 2010 and then to 0.59 in 2018, demonstrating a healthy tendency. The normalized difference vegetation index (NDVI), land desertification, and population were the factors that had the most significant impact on VNF, VED, and VSI, respectively. The global Moran’s I index of grassland vulnerability was greater than 0, with a significant positive spatial correlation. The number of High-High and Low-Low units decreased, indicating that the High-High and Low-Low cluster regions tended to be discrete. Moreover, our results suggest that understanding the variations in grassland vulnerability on the QTP is important for regional sustainable development in the context of intensified climate change and human disturbances. |
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Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plateau (QTP) is considered high, but its spatial and temporal variations in response to human activities and climate change are not well understood. In this study, a comprehensive grassland vulnerability index (GVI), which includes natural factors (VNF), environmental disturbances (VED), and socioeconomic impacts (VSI), was developed by using the analytic hierarchy process (AHP), principal component analysis (PCA), and environmental vulnerability distance index (EVDI). Our results showed that the spatial distribution of GVI had obvious heterogeneity, decreasing from northwest to southeast; the regions with serious and extreme vulnerability were mainly concentrated in the north-western alpine steppe and desert steppe. From 2000 to 2018, GVI decreased from 0.61 in 2000 to 0.60 in 2010 and then to 0.59 in 2018, demonstrating a healthy tendency. The normalized difference vegetation index (NDVI), land desertification, and population were the factors that had the most significant impact on VNF, VED, and VSI, respectively. The global Moran’s I index of grassland vulnerability was greater than 0, with a significant positive spatial correlation. The number of High-High and Low-Low units decreased, indicating that the High-High and Low-Low cluster regions tended to be discrete. Moreover, our results suggest that understanding the variations in grassland vulnerability on the QTP is important for regional sustainable development in the context of intensified climate change and human disturbances.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14094912</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Biodiversity ; Climate change ; Desertification ; Disturbances ; Ecosystem services ; Ecosystems ; Environmental conditions ; Environmental economics ; GDP ; Grassland management ; Grasslands ; Gross Domestic Product ; Heterogeneity ; Human impact ; Human influences ; Livestock ; Management decisions ; Normalized difference vegetative index ; Population ; Precipitation ; Principal components analysis ; Regional development ; Regional planning ; Regions ; Security ; Soil erosion ; Spatial distribution ; Steppes ; Sustainable development ; Temporal variations</subject><ispartof>Sustainability, 2022-05, Vol.14 (9), p.4912</ispartof><rights>2022 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><citedby>FETCH-LOGICAL-c295t-753436a383b1fe7695de2e68cf0729425e985f508bb26b610bbe79e50935ed7e3</citedby><cites>FETCH-LOGICAL-c295t-753436a383b1fe7695de2e68cf0729425e985f508bb26b610bbe79e50935ed7e3</cites><orcidid>0000-0003-2485-9007 ; 0000-0002-2231-2844</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Zhao, Zhengyuan</creatorcontrib><creatorcontrib>Zhang, Yunlong</creatorcontrib><creatorcontrib>Sun, Siqi</creatorcontrib><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Lü, Yihe</creatorcontrib><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Wu, Xing</creatorcontrib><title>Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework</title><title>Sustainability</title><description>Grasslands are globally important for providing essential ecosystem services and maintaining ecological security. Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plateau (QTP) is considered high, but its spatial and temporal variations in response to human activities and climate change are not well understood. In this study, a comprehensive grassland vulnerability index (GVI), which includes natural factors (VNF), environmental disturbances (VED), and socioeconomic impacts (VSI), was developed by using the analytic hierarchy process (AHP), principal component analysis (PCA), and environmental vulnerability distance index (EVDI). Our results showed that the spatial distribution of GVI had obvious heterogeneity, decreasing from northwest to southeast; the regions with serious and extreme vulnerability were mainly concentrated in the north-western alpine steppe and desert steppe. From 2000 to 2018, GVI decreased from 0.61 in 2000 to 0.60 in 2010 and then to 0.59 in 2018, demonstrating a healthy tendency. The normalized difference vegetation index (NDVI), land desertification, and population were the factors that had the most significant impact on VNF, VED, and VSI, respectively. The global Moran’s I index of grassland vulnerability was greater than 0, with a significant positive spatial correlation. The number of High-High and Low-Low units decreased, indicating that the High-High and Low-Low cluster regions tended to be discrete. Moreover, our results suggest that understanding the variations in grassland vulnerability on the QTP is important for regional sustainable development in the context of intensified climate change and human disturbances.</description><subject>Biodiversity</subject><subject>Climate change</subject><subject>Desertification</subject><subject>Disturbances</subject><subject>Ecosystem services</subject><subject>Ecosystems</subject><subject>Environmental conditions</subject><subject>Environmental economics</subject><subject>GDP</subject><subject>Grassland management</subject><subject>Grasslands</subject><subject>Gross Domestic Product</subject><subject>Heterogeneity</subject><subject>Human impact</subject><subject>Human influences</subject><subject>Livestock</subject><subject>Management decisions</subject><subject>Normalized difference vegetative index</subject><subject>Population</subject><subject>Precipitation</subject><subject>Principal components analysis</subject><subject>Regional development</subject><subject>Regional planning</subject><subject>Regions</subject><subject>Security</subject><subject>Soil erosion</subject><subject>Spatial distribution</subject><subject>Steppes</subject><subject>Sustainable development</subject><subject>Temporal variations</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkF1LwzAUhoMoOOZu_AUB74RqPpq0udShUxioOHdbkvXUZbZJTVJl_96NCXpu3sPLwznwIHROyRXnilzHgeZE5YqyIzRipKAZJYIc_9tP0STGDdkN51RROULptdfJ-gRd74Nu8VIHuy9cxNbhWdAxttrVeDm0DoI2trVpi73DaQ34xbr3tbbZwhpI-LnVCfSAb3WEeo9oPPVdH2ANLtovwPdBd_Dtw8cZOml0G2Hym2P0dn-3mD5k86fZ4_Rmnq2YEikrBM-51LzkhjZQSCVqYCDLVUMKpnImQJWiEaQ0hkkjKTEGCgWCKC6gLoCP0cXhbh_85wAxVRs_BLd7WTEpOaWSiXJHXR6oVfAxBmiqPthOh21FSbUXW_2J5T9dNGuG</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Zhao, Zhengyuan</creator><creator>Zhang, Yunlong</creator><creator>Sun, Siqi</creator><creator>Li, Ting</creator><creator>Lü, Yihe</creator><creator>Jiang, Wei</creator><creator>Wu, Xing</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-2485-9007</orcidid><orcidid>https://orcid.org/0000-0002-2231-2844</orcidid></search><sort><creationdate>20220501</creationdate><title>Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework</title><author>Zhao, Zhengyuan ; Zhang, Yunlong ; Sun, Siqi ; Li, Ting ; Lü, Yihe ; Jiang, Wei ; Wu, Xing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-753436a383b1fe7695de2e68cf0729425e985f508bb26b610bbe79e50935ed7e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Biodiversity</topic><topic>Climate change</topic><topic>Desertification</topic><topic>Disturbances</topic><topic>Ecosystem services</topic><topic>Ecosystems</topic><topic>Environmental conditions</topic><topic>Environmental economics</topic><topic>GDP</topic><topic>Grassland management</topic><topic>Grasslands</topic><topic>Gross Domestic Product</topic><topic>Heterogeneity</topic><topic>Human impact</topic><topic>Human influences</topic><topic>Livestock</topic><topic>Management decisions</topic><topic>Normalized difference vegetative index</topic><topic>Population</topic><topic>Precipitation</topic><topic>Principal components analysis</topic><topic>Regional development</topic><topic>Regional planning</topic><topic>Regions</topic><topic>Security</topic><topic>Soil erosion</topic><topic>Spatial distribution</topic><topic>Steppes</topic><topic>Sustainable development</topic><topic>Temporal variations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Zhengyuan</creatorcontrib><creatorcontrib>Zhang, Yunlong</creatorcontrib><creatorcontrib>Sun, Siqi</creatorcontrib><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Lü, Yihe</creatorcontrib><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Wu, Xing</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Zhengyuan</au><au>Zhang, Yunlong</au><au>Sun, Siqi</au><au>Li, Ting</au><au>Lü, Yihe</au><au>Jiang, Wei</au><au>Wu, Xing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework</atitle><jtitle>Sustainability</jtitle><date>2022-05-01</date><risdate>2022</risdate><volume>14</volume><issue>9</issue><spage>4912</spage><pages>4912-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Grasslands are globally important for providing essential ecosystem services and maintaining ecological security. Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plateau (QTP) is considered high, but its spatial and temporal variations in response to human activities and climate change are not well understood. In this study, a comprehensive grassland vulnerability index (GVI), which includes natural factors (VNF), environmental disturbances (VED), and socioeconomic impacts (VSI), was developed by using the analytic hierarchy process (AHP), principal component analysis (PCA), and environmental vulnerability distance index (EVDI). Our results showed that the spatial distribution of GVI had obvious heterogeneity, decreasing from northwest to southeast; the regions with serious and extreme vulnerability were mainly concentrated in the north-western alpine steppe and desert steppe. From 2000 to 2018, GVI decreased from 0.61 in 2000 to 0.60 in 2010 and then to 0.59 in 2018, demonstrating a healthy tendency. The normalized difference vegetation index (NDVI), land desertification, and population were the factors that had the most significant impact on VNF, VED, and VSI, respectively. The global Moran’s I index of grassland vulnerability was greater than 0, with a significant positive spatial correlation. The number of High-High and Low-Low units decreased, indicating that the High-High and Low-Low cluster regions tended to be discrete. Moreover, our results suggest that understanding the variations in grassland vulnerability on the QTP is important for regional sustainable development in the context of intensified climate change and human disturbances.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su14094912</doi><orcidid>https://orcid.org/0000-0003-2485-9007</orcidid><orcidid>https://orcid.org/0000-0002-2231-2844</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biodiversity Climate change Desertification Disturbances Ecosystem services Ecosystems Environmental conditions Environmental economics GDP Grassland management Grasslands Gross Domestic Product Heterogeneity Human impact Human influences Livestock Management decisions Normalized difference vegetative index Population Precipitation Principal components analysis Regional development Regional planning Regions Security Soil erosion Spatial distribution Steppes Sustainable development Temporal variations |
title | Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework |
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