Spatio-temporal response of vegetation coverage at multiple time scales to extreme climate in the Qinling mountains in Northwest China

Monitoring spatio-temporal variations in vegetation coverage and linking them to climatic drivers is crucial for guiding environmental management and understanding climate change. In this study, Pearson's correlation, MODIS NDVI time series, precipitation and temperature data, and extreme climate indices were used to investigate the response of vegetation to extreme climate at the monthly, seasonal, and yearly scales in the Qinling Mountains (QMs) in China from 2001 to 2020. The results indicate that vegetation coverage increased over time at a rate of 2.9×10 −3 per year. The QMs exhibited good vegetation coverage (average NDVI = 0.64), with over 64% of the area featuring NDVI values between 0.60 and 0.80. The Mean center was located in Ningshan County on the southern slope of the QMs. The spatial pattern of the annual average NDVI on the northern and southern slopes of the QMs was consistent with the seasonal average variation, with high values in the middle and low values at the edges. As transitional climate regions, the QMs exert a significant impact on vegetation. Spring maximum continuous 5-day monthly precipitation (Rx5day) and spring precipitation were the two most significant positive controlling factors affecting vegetation. Specifically, aside from water bodies, grasslands exhibited the largest response to these two factors. Good vegetation conditions in the QMs are of great significance for regulating climate and conserving water sources. Furthermore, they are important for controlling the response of vegetation to climatic conditions and, in a deeper sense, are of great significance for vegetation restoration, ecological protection, and carbon neutrality.

Authors

Institutions

Publication Details

Journal
Research in Cold and Arid Regions
Published
2024-11-23
DOI
https://doi.org/10.1016/j.rcar.2024.11.005
Citations
6
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
2.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatio-temporal response of vegetation coverage at multiple time scales to extreme climate in the Qinling mountains in Northwest China

Xin Peng, Qing Meng, Shanhong Zhang
6 citations
Research in Cold and Arid Regions
Remote Sensing in Agriculture
2.75
article

Spatio-temporal response of vegetation coverage at multiple time scales to extreme climate in the Qinling mountains in Northwest China

Xin Peng, Qing Meng, Shanhong Zhang
article en
6 citations

Abstract

Monitoring spatio-temporal variations in vegetation coverage and linking them to climatic drivers is crucial for guiding environmental management and understanding climate change. In this study, Pearson's correlation, MODIS NDVI time series, precipitation and temperature data, and extreme climate indices were used to investigate the response of vegetation to extreme climate at the monthly, seasonal, and yearly scales in the Qinling Mountains (QMs) in China from 2001 to 2020. The results indicate that vegetation coverage increased over time at a rate of 2.9×10 −3 per year. The QMs exhibited good vegetation coverage (average NDVI = 0.64), with over 64% of the area featuring NDVI values between 0.60 and 0.80. The Mean center was located in Ningshan County on the southern slope of the QMs. The spatial pattern of the annual average NDVI on the northern and southern slopes of the QMs was consistent with the seasonal average variation, with high values in the middle and low values at the edges. As transitional climate regions, the QMs exert a significant impact on vegetation. Spring maximum continuous 5-day monthly precipitation (Rx5day) and spring precipitation were the two most significant positive controlling factors affecting vegetation. Specifically, aside from water bodies, grasslands exhibited the largest response to these two factors. Good vegetation conditions in the QMs are of great significance for regulating climate and conserving water sources. Furthermore, they are important for controlling the response of vegetation to climatic conditions and, in a deeper sense, are of great significance for vegetation restoration, ecological protection, and carbon neutrality.

Research in Cold and Arid RegionsVol. 16(6)
Shangluo University (CN)
Climate action
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
2.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.