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Chinese Journal of Applied Ecology ›› 2024, Vol. 35 ›› Issue (11): 2992-3004.doi: 10.13287/j.1001-9332.202410.022

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Global vegetation response to extreme climate from 2001 to 2020

JIAO Penghua1, NIU Jianzhi1,2,3,4*, MIAO Yubo1,5, LI Junyi1, WANG Di1   

  1. 1School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China;
    2State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China;
    3Key Laboratory of State Forestry Administration on Soil and Water Conservation and Desertification Combating, Beijing 100083, China;
    4Engineering Research Center of Forestry Ecological Engineering of Ministry of Education, Beijing Forestry University, Beijing 100083, China;
    5Academy of Forestry Inventory and Planning, National Forestry and Grassland Administration, Beijing 100013, China
  • Received:2024-02-05 Revised:2024-07-21 Online:2024-11-18 Published:2025-05-18

Abstract: Exploring the spatiotemporal variations and response characteristics of global vegetation and extreme climate is of great significance for addressing global climate change and improving ecosystem stability. Based on ERA5 climate data from the European Centre for Medium-Range Weather Forecasts and MODIS normalized difference vegetation index (NDVI) data, we used Sen’s trend analysis, correlation analysis, and random forest regression model to explore the responses of NDVI of five vegetation types (boreal and temperate forest, tropical forest, other woody vegetation, grassland, and cropland) to 23 extreme climate indices from 2001 to 2020. The results showed that global NDVI showed an overall increasing trend from 2001 to 2020. The areas with the most significant growth trend was boreal and temperate forest, and the least significant growth trend occurred in cropland. In terms of extreme climate index, except for a few extreme high temperature and low temperature indices, the other indices showed an increasing trend. Across different vegetation areas, the extreme climate index that had the greatest influence on NDVI was different. The results of correlation analysis showed that the indices with the greatest impact on NDVI in the boreal and temperate forest, tropical forest, other woody vegetation, grassland, and cropland were cold days, ice days, annual total precipitation, annual total precipitation, and annual total precipitation, respectively. The results of random forest analysis showed that the indices with the greatest impact on NDVI in each vegetation zone were cold days, warm night days, frost days, warm days, and the cold spell duration index, respectively. The reason for the different results between the two methods was that correlation analysis only reflected linear relationships between variables, while the random forest regression model could capture more complex nonlinear relationships. Our results showed that the response of global vegetation to extreme climate had significant regional differences and complexities, which may result from interactions between different climate factors.

Key words: extreme climate, global vegetation, normalized difference vegetation index, random forest, correlation analysis