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Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021
Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these progra...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643839/ https://www.ncbi.nlm.nih.gov/pubmed/36388493 http://dx.doi.org/10.3389/fpls.2022.1045290 |
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author | Wang, Huihui Zhan, Jinyan Wang, Chao Liu, Wei Yang, Zheng Liu, Huizi Bai, Chunyue |
author_facet | Wang, Huihui Zhan, Jinyan Wang, Chao Liu, Wei Yang, Zheng Liu, Huizi Bai, Chunyue |
author_sort | Wang, Huihui |
collection | PubMed |
description | Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×10(4) km(2), and browning was 5.32×10(4) km(2), representing a quarter and 2.86% of the natural vegetation area, respectively. The results of the browning driver analysis show that areas with high altitude, reduced annual precipitation, high intensity of human activity, average annual maximum and average annual minimum precipitation of approximately 500 mm are most susceptible to browning on the QTP. For the seven different vegetation types, their top 6 most important browning drivers and the ranking of drivers differed. DEM and precipitation changes are important drivers of browning for seven vegetation types. These results reflect the latest spatial and temporal dynamics of vegetation on the QTP and highlight the common and characteristic browning drivers of vegetation ecosystems. They provide support for understanding the response of different vegetation to natural and human impacts and for further implementation of site-specific restoration measures. |
format | Online Article Text |
id | pubmed-9643839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96438392022-11-15 Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 Wang, Huihui Zhan, Jinyan Wang, Chao Liu, Wei Yang, Zheng Liu, Huizi Bai, Chunyue Front Plant Sci Plant Science Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×10(4) km(2), and browning was 5.32×10(4) km(2), representing a quarter and 2.86% of the natural vegetation area, respectively. The results of the browning driver analysis show that areas with high altitude, reduced annual precipitation, high intensity of human activity, average annual maximum and average annual minimum precipitation of approximately 500 mm are most susceptible to browning on the QTP. For the seven different vegetation types, their top 6 most important browning drivers and the ranking of drivers differed. DEM and precipitation changes are important drivers of browning for seven vegetation types. These results reflect the latest spatial and temporal dynamics of vegetation on the QTP and highlight the common and characteristic browning drivers of vegetation ecosystems. They provide support for understanding the response of different vegetation to natural and human impacts and for further implementation of site-specific restoration measures. Frontiers Media S.A. 2022-10-26 /pmc/articles/PMC9643839/ /pubmed/36388493 http://dx.doi.org/10.3389/fpls.2022.1045290 Text en Copyright © 2022 Wang, Zhan, Wang, Liu, Yang, Liu and Bai https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Wang, Huihui Zhan, Jinyan Wang, Chao Liu, Wei Yang, Zheng Liu, Huizi Bai, Chunyue Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title | Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title_full | Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title_fullStr | Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title_full_unstemmed | Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title_short | Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021 |
title_sort | greening or browning? the macro variation and drivers of different vegetation types on the qinghai-tibetan plateau from 2000 to 2021 |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643839/ https://www.ncbi.nlm.nih.gov/pubmed/36388493 http://dx.doi.org/10.3389/fpls.2022.1045290 |
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