归一化差异植被指数
环境科学
高原(数学)
植被(病理学)
全球变暖
大气科学
绿化
气候学
解耦(概率)
气候变化
驱动因素
自然地理学
生态学
地理
地质学
数学
医学
数学分析
考古
病理
控制工程
中国
工程类
生物
作者
Binni Xu,Jingji Li,Xiangjun Pei,Hai-Long Yang
标识
DOI:10.1016/j.jenvman.2023.119131
摘要
Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001–2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.
科研通智能强力驱动
Strongly Powered by AbleSci AI