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Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin

BACKGROUND: Climate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its...

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Autores principales: Han, Feifei, Yan, Junjie, Ling, Hong-bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106391/
https://www.ncbi.nlm.nih.gov/pubmed/33996282
http://dx.doi.org/10.7717/peerj.11334
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author Han, Feifei
Yan, Junjie
Ling, Hong-bo
author_facet Han, Feifei
Yan, Junjie
Ling, Hong-bo
author_sort Han, Feifei
collection PubMed
description BACKGROUND: Climate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its vegetation cover is more sensitive to climate change. However, previous studies rarely studied the changes in the vegetation coverage of the Irtysh River and its sensitivity to climate factors from a spatiotemporal perspective. METHODS: We adopted a vegetation sensitivity index based on remote sensing datasets of high temporal resolution to study the sensitivity of vegetation to climatic factors in the Irtysh River basin, then reveal the driving mechanism of vegetation cover change. RESULTS: The results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. Sensitivity of vegetation increases with the increase of coverage. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale.
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spelling pubmed-81063912021-05-13 Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin Han, Feifei Yan, Junjie Ling, Hong-bo PeerJ Ecology BACKGROUND: Climate change is an important factor driving vegetation changes in arid areas. Identifying the sensitivity of vegetation to climate variability is crucial for developing sustainable ecosystem management strategies. The Irtysh River is located in the westerly partition of China, and its vegetation cover is more sensitive to climate change. However, previous studies rarely studied the changes in the vegetation coverage of the Irtysh River and its sensitivity to climate factors from a spatiotemporal perspective. METHODS: We adopted a vegetation sensitivity index based on remote sensing datasets of high temporal resolution to study the sensitivity of vegetation to climatic factors in the Irtysh River basin, then reveal the driving mechanism of vegetation cover change. RESULTS: The results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. Sensitivity of vegetation increases with the increase of coverage. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale. PeerJ Inc. 2021-05-05 /pmc/articles/PMC8106391/ /pubmed/33996282 http://dx.doi.org/10.7717/peerj.11334 Text en ©2021 Han et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Ecology
Han, Feifei
Yan, Junjie
Ling, Hong-bo
Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title_full Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title_fullStr Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title_full_unstemmed Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title_short Variance of vegetation coverage and its sensitivity to climatic factors in the Irtysh River basin
title_sort variance of vegetation coverage and its sensitivity to climatic factors in the irtysh river basin
topic Ecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106391/
https://www.ncbi.nlm.nih.gov/pubmed/33996282
http://dx.doi.org/10.7717/peerj.11334
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