Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities

Reliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activi...

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Autores principales: Feng Tian, Lei-Zhen Liu, Jian-Hua Yang, Jian-Jun Wu
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Publicado: Elsevier 2021
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spelling oai:doaj.org-article:f67fba7193844953aeb905b26263b84f2021-12-01T04:47:04ZVegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities1470-160X10.1016/j.ecolind.2021.107479https://doaj.org/article/f67fba7193844953aeb905b26263b84f2021-06-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21001448https://doaj.org/toc/1470-160XReliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activities. However, whether there has been a significant change in the intensity of vegetation dynamics in the YRB during the 21st century and how much anthropogenic disturbances contribute to the change are still poorly documented so far; thus, this work may hold a far-reaching significance for maintaining the eco-balance of northern China and even all of China. In this study, based on the annually and seasonally averaged normalized deviation vegetation index (NDVI), a comprehensive analysis was conducted to detect the trends of vegetation changes derived by NDVI at six different time scales during the period 2000–2018 using Theil-Sen statistics, and the Mann-Kendall (M-K) method was employed to test the significance levels of vegetation greening and browning. Subsequently, based on Moderate Resolution Imaging Spectroradiometer land cover data, in situ climatic and meteorological data and socioeconomic and anthropogenic factors were included to explore the driving forces for vegetation change through the GeoDetector method. Our results show that the YRB experienced both significant vegetation greening and browning with great spatial heterogeneity. More than 94% of the regions in the YRB showed vegetation greening trends at average rates of 0.0040 yr−1 in the annually averaged NDVI (NDVIAN) and 0.0053 yr−1 in the growing season averaged NDVI (NDVIGS) during the 21st century; the trend was significant in more than 71% (p < 0.05) of the total YRB territory. This phenomenon was mainly detected in the middle reaches of the YRB, where effective land use activities and afforestation played vital roles. There was great seasonal disparity for greening; spring NDVI (NDVISP) and summer NDVI (NDVISU) exhibited the largest greening rates. However, only small parts of the YRB (0.9%, p < 0.05) showed browning trends at average rates of 0.0015 yr−1 in NDVIAN and 0.0021 yr−1 in NDVIGS, mainly located in the Weihe River basin and the lower reaches of the YRB, where the sharp increase in urban and built-up areas contributed substantially to the browning trends. Overall, warming, increasing population and afforestation jointly dominated the significant vegetation greening trends in the YRB.Feng TianLei-Zhen LiuJian-Hua YangJian-Jun WuElsevierarticleVegetation greeningAttribution detectionGeoDetectorM-K testYellow River BasinEcologyQH540-549.5ENEcological Indicators, Vol 125, Iss , Pp 107479- (2021)
institution DOAJ
collection DOAJ
language EN
topic Vegetation greening
Attribution detection
GeoDetector
M-K test
Yellow River Basin
Ecology
QH540-549.5
spellingShingle Vegetation greening
Attribution detection
GeoDetector
M-K test
Yellow River Basin
Ecology
QH540-549.5
Feng Tian
Lei-Zhen Liu
Jian-Hua Yang
Jian-Jun Wu
Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
description Reliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activities. However, whether there has been a significant change in the intensity of vegetation dynamics in the YRB during the 21st century and how much anthropogenic disturbances contribute to the change are still poorly documented so far; thus, this work may hold a far-reaching significance for maintaining the eco-balance of northern China and even all of China. In this study, based on the annually and seasonally averaged normalized deviation vegetation index (NDVI), a comprehensive analysis was conducted to detect the trends of vegetation changes derived by NDVI at six different time scales during the period 2000–2018 using Theil-Sen statistics, and the Mann-Kendall (M-K) method was employed to test the significance levels of vegetation greening and browning. Subsequently, based on Moderate Resolution Imaging Spectroradiometer land cover data, in situ climatic and meteorological data and socioeconomic and anthropogenic factors were included to explore the driving forces for vegetation change through the GeoDetector method. Our results show that the YRB experienced both significant vegetation greening and browning with great spatial heterogeneity. More than 94% of the regions in the YRB showed vegetation greening trends at average rates of 0.0040 yr−1 in the annually averaged NDVI (NDVIAN) and 0.0053 yr−1 in the growing season averaged NDVI (NDVIGS) during the 21st century; the trend was significant in more than 71% (p < 0.05) of the total YRB territory. This phenomenon was mainly detected in the middle reaches of the YRB, where effective land use activities and afforestation played vital roles. There was great seasonal disparity for greening; spring NDVI (NDVISP) and summer NDVI (NDVISU) exhibited the largest greening rates. However, only small parts of the YRB (0.9%, p < 0.05) showed browning trends at average rates of 0.0015 yr−1 in NDVIAN and 0.0021 yr−1 in NDVIGS, mainly located in the Weihe River basin and the lower reaches of the YRB, where the sharp increase in urban and built-up areas contributed substantially to the browning trends. Overall, warming, increasing population and afforestation jointly dominated the significant vegetation greening trends in the YRB.
format article
author Feng Tian
Lei-Zhen Liu
Jian-Hua Yang
Jian-Jun Wu
author_facet Feng Tian
Lei-Zhen Liu
Jian-Hua Yang
Jian-Jun Wu
author_sort Feng Tian
title Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
title_short Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
title_full Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
title_fullStr Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
title_full_unstemmed Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities
title_sort vegetation greening in more than 94% of the yellow river basin (yrb) region in china during the 21st century caused jointly by warming and anthropogenic activities
publisher Elsevier
publishDate 2021
url https://doaj.org/article/f67fba7193844953aeb905b26263b84f
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AT jianhuayang vegetationgreeninginmorethan94oftheyellowriverbasinyrbregioninchinaduringthe21stcenturycausedjointlybywarmingandanthropogenicactivities
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