Spatiotemporal Variations of Global Terrestrial Typical Vegetation EVI and Their Responses to Climate Change from 2000 to 2021

冻土带 灌木丛 环境科学 气候变化 植被(病理学) 气候学 全球变暖 草原 全球变化 降水 生态系统 陆地生态系统 自然地理学 生态学 地理 气象学 医学 病理 生物 地质学
作者
Chenhao Li,Yifan Song,Tianling Qin,Denghua Yan,Xin Zhang,Lin Zhu,Batsuren Dorjsuren,Hira Khalid
出处
期刊:Remote Sensing [MDPI AG]
卷期号:15 (17): 4245-4245 被引量:3
标识
DOI:10.3390/rs15174245
摘要

With the increasing impact of climate change on ecosystems, it is crucial to analyze how changes in precipitation and temperature affect global ecosystems. Therefore, this study aims to investigate the spatiotemporal variation characteristics of the Enhanced Vegetation Index (EVI) in the global forest, grassland, shrubland, and tundra (FGST) from 2000 to 2021. We utilized partial correlation analysis and grey relation analysis to assess the responses of different vegetation types to precipitation, temperature, and extreme water and heat indicators. The result shows that, despite a “warmer and drier” trend in FGST (excluding tundra), global climate change has not adversely affected the ongoing vegetation growth. It presents a favorable implication for global carbon dioxide assimilation. Different vegetation types displayed different sensitivities to changes in precipitation and temperature. Shrubland proved to be the most sensitive, followed by grassland, forest, and tundra. As the impacts of global climate change intensify, it becomes crucial to direct our attention toward dynamics of vegetation types demonstrating heightened sensitivity to fluctuations in precipitation and temperature. Our study indicates that, except for forests, extreme precipitation indicators have a stronger impact on EVI than extreme temperature indicators. Forests and tundra have demonstrated heightened susceptibility to the intensity of extreme climatic events, while grasslands and shrublands have been more sensitive to the duration of such events. Understanding these responses can offer valuable insights for developing targeted strategies for adaptation and preservation. Our study enhances comprehension of the feedback relationship between global climate change and vegetation, offering scientific evidence for global climate change evaluation.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
山人完成签到 ,获得积分10
刚刚
刚刚
碧蓝曼冬发布了新的文献求助10
刚刚
领导范儿应助正直的雨双采纳,获得10
1秒前
1秒前
AI完成签到,获得积分10
1秒前
1秒前
半点发布了新的文献求助10
1秒前
斯文败类应助科研采纳,获得10
2秒前
2秒前
Cary完成签到,获得积分10
2秒前
3秒前
superkang发布了新的文献求助10
3秒前
失眠幻灵完成签到 ,获得积分10
3秒前
虹虹发布了新的文献求助10
3秒前
Fairy完成签到,获得积分10
4秒前
4秒前
4秒前
专注依云完成签到,获得积分20
4秒前
小二郎应助纯真厉采纳,获得10
4秒前
Jasper应助江遇采纳,获得10
4秒前
宫小小心发布了新的文献求助10
5秒前
小蘑菇应助小李爱查文献采纳,获得10
5秒前
脑洞疼应助phoebe采纳,获得10
5秒前
5秒前
AWE完成签到,获得积分10
5秒前
Jaron0080发布了新的文献求助10
6秒前
6秒前
华仔应助踏雾采纳,获得10
7秒前
量子星尘发布了新的文献求助10
8秒前
8秒前
9秒前
清修发布了新的文献求助10
9秒前
9秒前
蕾蕾蕾发布了新的文献求助20
9秒前
CipherSage应助王蕊采纳,获得10
9秒前
10秒前
别来春半发布了新的文献求助10
10秒前
10秒前
星光熠熠发布了新的文献求助10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Exploring Nostalgia 500
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
Advanced Memory Technology: Functional Materials and Devices 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5667488
求助须知:如何正确求助?哪些是违规求助? 4886195
关于积分的说明 15120469
捐赠科研通 4826311
什么是DOI,文献DOI怎么找? 2583920
邀请新用户注册赠送积分活动 1537973
关于科研通互助平台的介绍 1496095