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<p>Climate change significantly influences vegetation growth, necessitating an in-depth understanding of the climate-driven dynamics of vegetation to formulate ecological and environmental policies. This study addresses the limitations of traditional correlation analysis methods by utilizing a combined approach of Residual Trend Analysis (RESTREND) and Time Series Segmentation Residual Trend Analysis (TSS-RESTREND), known as CTSS-RESTREND. By using this method, we examined the influence of near-surface air temperature, precipitation, humidity, and wind speed on vegetation growth in Guangdong Province from 2000 to 2020, using Normalized Difference Vegetation Index (NDVI) data and climatic variables. Using MOD13Q1 NDVI data and ERA5 downscaled climate reanalysis data, this research utilizes the CTSS-RESTREND algorithm to quantify the climate effects on vegetation. The analysis reveals that precipitation and humidity are the primary positive drivers of vegetation growth, temperature has a slightly higher positive than negative impact on vegetation, while wind speed generally has a negative impact on vegetation, but its effect is relatively slight. During the growing season, the growth of vegetation becomes more sensitive to the three climatic factors: temperature, precipitation, and humidity. This study provides a more accurate and detailed understanding of the spatiotemporal changes and climate driving factors affecting vegetation in Guangdong Province.</p>

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Analysis of vegetation response to four climate factors based on the CTSS-RESTREND method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月21日

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  • B. Hao,
  • S. Ding,
  • Z. Wu,
  • Z. Ruan,
  • L. Mai,
  • Y. Kuang

摘要

Climate change significantly influences vegetation growth, necessitating an in-depth understanding of the climate-driven dynamics of vegetation to formulate ecological and environmental policies. This study addresses the limitations of traditional correlation analysis methods by utilizing a combined approach of Residual Trend Analysis (RESTREND) and Time Series Segmentation Residual Trend Analysis (TSS-RESTREND), known as CTSS-RESTREND. By using this method, we examined the influence of near-surface air temperature, precipitation, humidity, and wind speed on vegetation growth in Guangdong Province from 2000 to 2020, using Normalized Difference Vegetation Index (NDVI) data and climatic variables. Using MOD13Q1 NDVI data and ERA5 downscaled climate reanalysis data, this research utilizes the CTSS-RESTREND algorithm to quantify the climate effects on vegetation. The analysis reveals that precipitation and humidity are the primary positive drivers of vegetation growth, temperature has a slightly higher positive than negative impact on vegetation, while wind speed generally has a negative impact on vegetation, but its effect is relatively slight. During the growing season, the growth of vegetation becomes more sensitive to the three climatic factors: temperature, precipitation, and humidity. This study provides a more accurate and detailed understanding of the spatiotemporal changes and climate driving factors affecting vegetation in Guangdong Province.