Spatiotemporal Variation of Vegetation Greenness and Its Response to Extreme Climate Events: A Case Study of the Central Yunnan Urban Agglomeration, China

ABSTRACT In recent years, the frequency of extreme climate events has risen significantly, exerting profound effects on both terrestrial ecosystems and human well‐being. The Central Yunnan Urban Agglomeration, an ecologically sensitive region, is especially vulnerable to these changes. This study, conducted using the Google Earth Engine (GEE) platform, utilizes trend analysis, Hurst exponent evaluation, correlation analysis, and random forest modeling to explore the spatiotemporal dynamics of vegetation greenness in the region and its response to extreme climate events. The main results are as follows: (1) From 2000 to 2022, vegetation greenness showed significant improvement with an annual growth rate of 0.003 ( R 2 = 0.69). Over half of the study area exhibited increasing trends across temporal scales, and a general upward trend over the past two decades, despite spring values being consistently lower than other seasons and low‐value areas concentrating in the eastern region and urban peripheries. (2) Vegetation stability was relatively low, with one‐third of areas showing high interannual variability and two‐thirds exhibiting high seasonal variability. (3) The annual mean Hurst index values were all below 0.5, with over 60% displaying anti‐persistent increasing trends, suggesting that while the overall trend may continue to rise, future patterns could differ from the past. (4) Vegetation greenness was primarily influenced by extreme temperatures, which had a far greater impact than precipitation events. The key factors identified for vegetation changes were consecutive dry days (CDD), extreme precipitation (R95p), and temperature extremes (TN90p, TX10p, TX90p), each accounting for over 30% of the changes. In conclusion, this study reveals the complex ecological dynamic of concurrent “vegetation growth and vulnerability,” providing a scientific basis for formulating ecosystem conservation and restoration policies in the region.

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Publication Details

Journal
Land Degradation and Development
Published
2025-12-04
DOI
https://doi.org/10.1002/ldr.70297
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Spatiotemporal Variation of Vegetation Greenness and Its Response to Extreme Climate Events: A Case Study of the Central Yunnan Urban Agglomeration, China

Haibo Yang, Guoping Chen, Junsan Zhao, Zhongzhi Zhang
Land Degradation and Development
Remote Sensing in Agriculture
article

Spatiotemporal Variation of Vegetation Greenness and Its Response to Extreme Climate Events: A Case Study of the Central Yunnan Urban Agglomeration, China

Haibo Yang, Guoping Chen, Junsan Zhao, Zhongzhi Zhang
article en

Abstract

ABSTRACT In recent years, the frequency of extreme climate events has risen significantly, exerting profound effects on both terrestrial ecosystems and human well‐being. The Central Yunnan Urban Agglomeration, an ecologically sensitive region, is especially vulnerable to these changes. This study, conducted using the Google Earth Engine (GEE) platform, utilizes trend analysis, Hurst exponent evaluation, correlation analysis, and random forest modeling to explore the spatiotemporal dynamics of vegetation greenness in the region and its response to extreme climate events. The main results are as follows: (1) From 2000 to 2022, vegetation greenness showed significant improvement with an annual growth rate of 0.003 ( R 2 = 0.69). Over half of the study area exhibited increasing trends across temporal scales, and a general upward trend over the past two decades, despite spring values being consistently lower than other seasons and low‐value areas concentrating in the eastern region and urban peripheries. (2) Vegetation stability was relatively low, with one‐third of areas showing high interannual variability and two‐thirds exhibiting high seasonal variability. (3) The annual mean Hurst index values were all below 0.5, with over 60% displaying anti‐persistent increasing trends, suggesting that while the overall trend may continue to rise, future patterns could differ from the past. (4) Vegetation greenness was primarily influenced by extreme temperatures, which had a far greater impact than precipitation events. The key factors identified for vegetation changes were consecutive dry days (CDD), extreme precipitation (R95p), and temperature extremes (TN90p, TX10p, TX90p), each accounting for over 30% of the changes. In conclusion, this study reveals the complex ecological dynamic of concurrent “vegetation growth and vulnerability,” providing a scientific basis for formulating ecosystem conservation and restoration policies in the region.

Land Degradation and Development
Kunming University of Science and Technology (CN), Innovation Team (China) (HK)
Openalex Percentile: Top 63%
Remote Sensing in Agriculture
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