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2000-2022年中国植被恢复力时空特征及演变趋势

王晶晶 朱烨 刘懿 张欣雨

农业工程学报2025,Vol.41Issue(3):136-143,8.
农业工程学报2025,Vol.41Issue(3):136-143,8.DOI:10.11975/j.issn.1002-6819.202406105

2000-2022年中国植被恢复力时空特征及演变趋势

Spatiotemporal characteristics and variation tendency of vegetation resilience over China during 2000-2022

王晶晶 1朱烨 1刘懿 2张欣雨3

作者信息

  • 1. 南京信息工程大学水文与水资源工程学院,南京 210044
  • 2. 水灾害防御全国重点实验室,南京 210098||河海大学水文水资源学院,南京 210098
  • 3. 河海大学水文水资源学院,南京 210098
  • 折叠

摘要

Abstract

Vegetation is an essential component of the terrestrial ecosystem,which plays a crucial role in facilitating material and energy exchanges among the soil,water,and atmosphere.In the context of global warming,climate extremes such as drought and heatwave events become more frequent.These extreme events threaten vegetation growth,leading to vegetation degradation and causing serious impacts on the structure and function of the terrestrial ecosystem.With the weakening of vegetation resistance to extreme dry and hot events,it has also been challenging for it to recover to its original state.To better cope with the risks of ecosystem degradation in a warming environment,it is necessary to evaluate the spatiotemporal patterns of vegetation resilience and its variation tendency.In this study,we used the Global Orbiting Carbon Observatory-2 based Solar Induced chlorophyll Fluorescence(GOSIF)satellite gridded data at a spatial resolution of 0.05°and eight day-interval during 2000-2022 to analyze the trend and spatial patterns of vegetation resilience over China.Based on the concept of the critical slowing down,the lag-one autocorrelation(AC1)and variance were employed as two indicators of the early-warning signals for the critical transitions of vegetation state.On this basis,the Kendall test was used to examine the trend of vegetation resilience,and the k-means clustering method based on the elbow method was employed to classify the different types of vegetation resilience.The results showed that spatially the vegetation resilience over China generally presents a decreasing pattern from south to north.Spatially,the lowest vegetation resilience was found in the Yellow River Basin,while vegetation resilience was generally high in Southwest China such as Sichuan and Yunnan provinces,and eastern parts of North China.Except for the northern parts of the Heilongjiang province and Inner Mongolia,the trends indicated by the AC1 and variance series were generally similar,where the vegetation resilience in most regions over China presented a decreasing pattern during 2000-2022.Compared with the period of 2000-2010,grid cells with enhanced vegetation resilience decreased by 31.04%,and grid cells with weakened vegetation resilience increased by 24.28%during 2011-2022,mainly distributed in the Yellow River Basin,upper and middle reaches of the Yangtze River Basin,Pearl River Basin.A tipping point of the vegetation resilience over China on average occurred in 2013.According to the k-means clustering method,the variation patterns of vegetation resilience were classified into seven groups.The tipping points of vegetation resilience in southwestern China,southeastern coastal areas,and the Pearl River Basin occurred earlier than the national average,while the tipping points in mountain areas in southern China,the Yellow River Basin,central Inner Mongolia and northern Xinjiang were the same as the national average.In contrast,the tipping points of vegetation resilience at the junction of Inner Mongolia and northern Heilongjiang were later than the national average.The results have implications for understanding the temporal characteristics and spatial heterogeneity of vegetation resilience over China,which are also promising to provide some references for the vegetation ecological restoration and protection strategies in China.

关键词

植被恢复力/日光诱导叶绿素荧光/时空特征/演变趋势

Key words

vegetation resilience/SIF/spatiotemporal characteristics/variation tendency

分类

大气科学

引用本文复制引用

王晶晶,朱烨,刘懿,张欣雨..2000-2022年中国植被恢复力时空特征及演变趋势[J].农业工程学报,2025,41(3):136-143,8.

基金项目

国家自然科学基金项目(U2243203) (U2243203)

国家重点研发计划项目(2023YFC3209800) (2023YFC3209800)

江苏省优秀青年基金项目(BK20220145) (BK20220145)

农业工程学报

OA北大核心

1002-6819

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