Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world
In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 1...
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Remote Sensing
2023
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Online Access: | http://hdl.handle.net/11093/5202 https://doi.org/10.3390/rs15194706 https://www.mdpi.com/2072-4292/15/19/4706 |
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ftunivvigo:oai:www.investigo.biblioteca.uvigo.es:11093/5202 2023-10-29T02:40:39+01:00 Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world Stojanovic, Milica Sorí Gómez, Rogert Guerova, Guergana Vázquez Domínguez, Marta Nieto Muñiz, Raquel Olalla Gimeno Presa, Luis 2023-09-26 http://hdl.handle.net/11093/5202 https://doi.org/10.3390/rs15194706 https://www.mdpi.com/2072-4292/15/19/4706 eng eng Remote Sensing Física aplicada EphysLab info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122314OB-I00/ES info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/RYC2021-034044-I/ES Remote Sensing, 15(19): 4706 (2023) 20724292 http://hdl.handle.net/11093/5202 doi:10.3390/rs15194706 https://www.mdpi.com/2072-4292/15/19/4706 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ openAccess 2502.03 Bioclimatología article 2023 ftunivvigo https://doi.org/10.3390/rs15194706 2023-10-03T23:21:37Z In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia. Agencia Estatal de Investigación | Ref. PID2021-122314OB-I00 Xunta de Galicia | Ref. ED431C 2021/44 Xunta de Galicia | Ref. ED481B-2021/134 Xunta de Galicia | Ref. ED481D 2022/020 Agencia Estatal de Investigación | Ref. RYC2021-034044-I Article in Journal/Newspaper taiga University of Vigo: Investigo (Repositorio Institucional de la Universidade de Vigo) Remote Sensing 15 19 4706 |
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Open Polar |
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University of Vigo: Investigo (Repositorio Institucional de la Universidade de Vigo) |
op_collection_id |
ftunivvigo |
language |
English |
topic |
2502.03 Bioclimatología |
spellingShingle |
2502.03 Bioclimatología Stojanovic, Milica Sorí Gómez, Rogert Guerova, Guergana Vázquez Domínguez, Marta Nieto Muñiz, Raquel Olalla Gimeno Presa, Luis Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
topic_facet |
2502.03 Bioclimatología |
description |
In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia. Agencia Estatal de Investigación | Ref. PID2021-122314OB-I00 Xunta de Galicia | Ref. ED431C 2021/44 Xunta de Galicia | Ref. ED481B-2021/134 Xunta de Galicia | Ref. ED481D 2022/020 Agencia Estatal de Investigación | Ref. RYC2021-034044-I |
format |
Article in Journal/Newspaper |
author |
Stojanovic, Milica Sorí Gómez, Rogert Guerova, Guergana Vázquez Domínguez, Marta Nieto Muñiz, Raquel Olalla Gimeno Presa, Luis |
author_facet |
Stojanovic, Milica Sorí Gómez, Rogert Guerova, Guergana Vázquez Domínguez, Marta Nieto Muñiz, Raquel Olalla Gimeno Presa, Luis |
author_sort |
Stojanovic, Milica |
title |
Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
title_short |
Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
title_full |
Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
title_fullStr |
Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
title_full_unstemmed |
Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
title_sort |
vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world |
publisher |
Remote Sensing |
publishDate |
2023 |
url |
http://hdl.handle.net/11093/5202 https://doi.org/10.3390/rs15194706 https://www.mdpi.com/2072-4292/15/19/4706 |
genre |
taiga |
genre_facet |
taiga |
op_relation |
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122314OB-I00/ES info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/RYC2021-034044-I/ES Remote Sensing, 15(19): 4706 (2023) 20724292 http://hdl.handle.net/11093/5202 doi:10.3390/rs15194706 https://www.mdpi.com/2072-4292/15/19/4706 |
op_rights |
Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ openAccess |
op_doi |
https://doi.org/10.3390/rs15194706 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
19 |
container_start_page |
4706 |
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1781068981611790336 |