Spatiotemporal Variations and Driving Mechanisms of Vegetation Phenology Across Different Vegetation Types in Yan Mountain from 2000 to 2022

Yan Mountain, located in the northern part of the Beijing–Tianjin–Hebei economic zone in northern China, serves as a crucial ecological barrier for this densely populated and industrialized region. However, vegetation phenology and its responses to climate change in this region remain unclear. This study utilized MODIS EVI data to extract key phenological metrics and identify dominant vegetation types in Yan Mountain from 2000 to 2022. We analyzed spatiotemporal dynamics and driving mechanisms of vegetation phenology by examining relationships among phenological parameters and their responses to climatic variables. Results revealed significant spatial heterogeneity and interannual variation in vegetation phenology. The start of the growing season (SOS) and peak of the growing season (POS) advanced significantly at rates of 0.54 and 0.19 days/year, respectively. Meanwhile, the end of the growing season (EOS) was delayed by 0.38 days/year, and the growing season length (GSL) was extended by 0.97 days/year. These shifts were more pronounced in grasslands and shrublands than in forests. Significant physiological correlations existed among phenological parameters. Earlier green-up probably advanced POS and significantly delayed EOS. SOS advancement exerted a greater influence on GSL extension than EOS delay, with variations across vegetation types. Mean annual temperature and precipitation jointly regulated phenological shifts; precipitation primarily drove SOS advancement and GSL extension, while temperature mainly advanced POS and delayed EOS. This study elucidates spatiotemporal changes in vegetation phenology and its climate change response patterns in Yan Mountain, providing a crucial reference for ecological management and climate change adaptation strategies.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2025-09-02
DOI
https://doi.org/10.3390/rs17173051
Citations
4
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
5.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporal Variations and Driving Mechanisms of Vegetation Phenology Across Different Vegetation Types in Yan Mountain from 2000 to 2022

Yü Liu, Junbang Wang, Wenxing Luo, Leiming Zhang et al.
4 citations
Remote Sensing
Remote Sensing and Land Use
5.20
article

Spatiotemporal Variations and Driving Mechanisms of Vegetation Phenology Across Different Vegetation Types in Yan Mountain from 2000 to 2022

Yü Liu, Junbang Wang, Wenxing Luo, Leiming Zhang, Rong Huang
article en
4 citations

Abstract

Yan Mountain, located in the northern part of the Beijing–Tianjin–Hebei economic zone in northern China, serves as a crucial ecological barrier for this densely populated and industrialized region. However, vegetation phenology and its responses to climate change in this region remain unclear. This study utilized MODIS EVI data to extract key phenological metrics and identify dominant vegetation types in Yan Mountain from 2000 to 2022. We analyzed spatiotemporal dynamics and driving mechanisms of vegetation phenology by examining relationships among phenological parameters and their responses to climatic variables. Results revealed significant spatial heterogeneity and interannual variation in vegetation phenology. The start of the growing season (SOS) and peak of the growing season (POS) advanced significantly at rates of 0.54 and 0.19 days/year, respectively. Meanwhile, the end of the growing season (EOS) was delayed by 0.38 days/year, and the growing season length (GSL) was extended by 0.97 days/year. These shifts were more pronounced in grasslands and shrublands than in forests. Significant physiological correlations existed among phenological parameters. Earlier green-up probably advanced POS and significantly delayed EOS. SOS advancement exerted a greater influence on GSL extension than EOS delay, with variations across vegetation types. Mean annual temperature and precipitation jointly regulated phenological shifts; precipitation primarily drove SOS advancement and GSL extension, while temperature mainly advanced POS and delayed EOS. This study elucidates spatiotemporal changes in vegetation phenology and its climate change response patterns in Yan Mountain, providing a crucial reference for ecological management and climate change adaptation strategies.

Remote SensingVol. 17(17)
Chinese Academy of Sciences (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 4%
Remote Sensing and Land Use
5.20
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.