2001—2020年全球植被对极端气候的响应

doi:10.13287/j.1001-9332.202410.022

应用生态学报 ›› 2024, Vol. 35 ›› Issue (11): 2992-3004.doi: 10.13287/j.1001-9332.202410.022

2001—2020年全球植被对极端气候的响应

焦鹏华¹, 牛健植^1,2,3,4*, 苗禹博^1,5, 李君宜¹, 王迪¹

¹北京林业大学水土保持学院, 北京 100083;
²林木资源高效生产全国重点实验室, 北京 100083;
³水土保持与荒漠化防治国家林业局重点实验室, 北京 100083;
⁴北京林业大学林业生态工程教育部工程研究中心, 北京 100083;
⁵国家林业和草原局林草调查规划院, 北京 100013

收稿日期:2024-02-05 修回日期:2024-07-21 出版日期:2024-11-18 发布日期:2025-05-18
通讯作者: *E-mail: nexk@bjfu.edu.cn
作者简介:焦鹏华, 女, 1998年生, 硕士研究生。主要从事植被与极端气候研究。E-mail: jph309u7@163.com
基金资助:
国家重点研发计划项目(2022YFF1300804)

Global vegetation response to extreme climate from 2001 to 2020

JIAO Penghua¹, NIU Jianzhi^1,2,3,4*, MIAO Yubo^1,5, LI Junyi¹, WANG Di¹

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

摘要/Abstract

摘要： 探究全球植被与极端气候的时空变化和响应特征,对于应对全球气候变化和提高生态系统的稳定性具有重要意义。本研究基于欧洲中期天气预报中心的ERA5气候数据和MODIS归一化植被指数(NDVI)数据,运用Sen’s趋势分析、相关性分析和随机森林回归模型等方法,探究2001—2020年全球5种植被类型(北方森林和温带森林、热带森林、其他木本植被、草原、农田)NDVI对23个极端气候指数的响应特征。结果表明: 研究期间,全球NDVI总体呈增长趋势,增长趋势最显著的植被类型为北方森林和温带森林,最不显著的是农田。极端气候指数方面,除少部分极端高温和低温指数呈降低趋势外,其余指数均呈增长趋势。在不同植被区,对NDVI影响最大的极端气候指数不同。相关性分析表明,在北方森林和温带森林、热带森林、其他木本植被、草原和农田区域,对NDVI影响最大的指数分别为冷昼日数、结冰天数、年降水总量、年降水总量、年降水总量;随机森林结果显示,对各植被区NDVI影响最大的指数分别为冷昼日数、热夜日数、霜冻天数、暖昼日数和寒冷时间持续指数。两种方法结果不同的原因在于相关性分析只反映变量间的线性关系,而随机森林回归模型可以捕捉更复杂的非线性关系。以上研究结果体现了全球植被对极端气候的响应具有显著的区域差异性和复杂性,这种差异性和复杂性可能是不同气候因子交互作用的结果。

关键词: 极端气候, 全球植被, 归一化植被指数, 随机森林, 相关分析

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

焦鹏华, 牛健植, 苗禹博, 李君宜, 王迪. 2001—2020年全球植被对极端气候的响应[J]. 应用生态学报, 2024, 35(11): 2992-3004.

JIAO Penghua, NIU Jianzhi, MIAO Yubo, LI Junyi, WANG Di. Global vegetation response to extreme climate from 2001 to 2020[J]. Chinese Journal of Applied Ecology, 2024, 35(11): 2992-3004.

参考文献

[1] 朴世龙, 张新平, 陈安平, 等. 极端气候事件对陆地生态系统碳循环的影响. 中国科学: 地球科学, 2019, 49(9): 1321-1334
[2] 于水, 张晓龙, 刘志娟, 等. 1961—2020年松花江流域极端气候指数的时空变化特征. 应用生态学报, 2023, 34(4): 1091-1101
[3] 王鑫, 王明田, 冯勇, 等. 2001—2020年川西北高原归一化植被指数演变特征及其对极端气候的响应. 应用生态学报, 2022, 33(7): 1957-1965
[4] 刘海红, 殷淑燕, 许丽婷, 等. 山东省极端气候和人类活动对不同植被类型NDVI的影响. 生态学报, 2023, 43(21): 8780-8792
[5] Heino M, Kinnunen P, Anderson W, et al. Increased probability of hot and dry weather extremes during the growing season threatens global crop yields. Scientific Reports, 2023, 13: 3583
[6] 安德帅, 徐丹丹, 濮毅涵, 等. 2000—2019年武夷山亚高山草甸对气候因子的响应及其时滞效应. 应用生态学报, 2021, 32(12): 4195-4202
[7] Lamchin M, Lee WK, Jeon SW, et al. Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Science of the Total Environment, 2018, 618: 1089-1095
[8] Chu HS, Venevsky S, Wu C, et al. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 2019, 650: 2051-2062
[9] Yin G, Hu ZY, Chen X, et al. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land, 2016, 8: 375-388
[10] Tucker CJ, Slayback DA, Pinzon JE, et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology, 2001, 45: 184-190
[11] 葛非凡, 毛克彪, 蒋跃林, 等. 华东地区夏季极端高温特征及其对植被的影响. 中国农业气象, 2017, 38(1): 42-51
[12] 项子源, 王钧, 王伟民. 亚热带城市高温对城市生态系统碳通量的抑制作用研究. 生态环境学报, 2020, 29(9): 1810-1821
[13] 刘炜, 焦树林. 喀斯特流域极端气候变化特征及对NDVI的影响. 水土保持学报, 2022, 36(5): 220-232
[14] 韩丹丹. 黄土高原植被变化及其对极端气候的响应. 硕士论文. 北京: 中国科学院大学, 2020
[15] Gouveia CM, Trigo RM, Beguería S, et al. Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators. Global and Planetary Change, 2017, 151: 15-27
[16] 赵安周, 张安兵, 赵延旭, 等. 基于MODISNDVI数据的陕甘宁地区植被覆盖时空变化及其对极端气候的响应. 水土保持研究, 2018, 25(3): 224-231
[17] 李茂华, 都金康, 李皖彤, 等. 1982—2015年全球植被变化及其与温度和降水的关系. 地理科学, 2020, 40(5): 823-832
[18] de Jong R, Schaepman ME, Furrer R, et al. Spatial relationship between climatologies and changes in global vegetation activity. Global Change Biology, 2013, 19: 1953-1964
[19] Ding YX, Li Z, Peng SZ. Global analysis of time-lag and -accumulation effects of climate on vegetation growth. International Journal of Applied Earth Observation and Geoinformation, 2020, 92: 102179
[20] He L, Guo JB, Yang WB, et al. Multifaceted responses of vegetation to average and extreme climate change over global drylands. Science of the Total Environment, 2023, 858: 159942
[21] Friedl M, Sulla-Menashe D. MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V061. Merritt Island, FL, USA: NASA EOSDIS Land Processes Distributed Active Archive Center, 2022
[22] 孟宪贵, 郭俊建, 韩永清. ERA5再分析数据适用性初步评估. 海洋气象学报, 2018, 38(1): 91-99
[23] Wang S, Fu BJ, Wei FL, et al. Drylands contribute disproportionately to observed global productivity increases. Science Bulletin, 2023, 68: 224-232
[24] 李阳阳, 董国涛, 薛华柱, 等. 1982—2020年黄河流域多时间尺度气象干旱对植被影响. 水土保持学报, 2024, 38(1): 187-196
[25] 葛建坤, 雷国相, 陈皓锐, 等. 基于SHAP重要性排序和机器学习算法的灌区渠道调度流量预测. 农业工程学报, 2023, 39(13): 113-122
[26] Bao ZX, Zhang JY, Wang GQ, et al. The sensitivity of vegetation cover to climate change in multiple climatic zones using machine learning algorithms. Ecological Indicators, 2021, 124: 107443
[27] Wang YY, Chen X, Gao M, et al. The use of random forest to identify climate and human interference on vege-tation coverage changes in southwest China. Ecological Indicators, 2022, 144: 109463
[28] 吴欣宇, 朱秀芳. 中国不同植被区对极端气候的响应差异. 生态学报, 2023, 43(24): 10202-10215
[29] Guo ZJ, Lou W, Sun C, et al. Trend changes of the vegetation activity in northeastern East Asia and the connections with extreme climate indices. Remote Sensing, 2022, 14: 3151
[30] IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group Ⅰ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, 2013
[31] Hijioka Y, Lin E, Pereira JJ, et al. Climate Change 2014: Impacts, Adaptation and Vulnerability: Part B: Regional Aspects. Contribution of Working Group Ⅱ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, 2014
[32] Nemani RR, Keeling CD, Hashimoto H, et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 2003, 300: 1560-1563
[33] Heino M, Kinnunen P, Anderson W, et al. Increased probability of hot and dry weather extremes during the growing season threatens global crop yields. Scientific Reports, 2023, 13: 3583
[34] 刘盼, 赵西宁, 高晓东, 等. 黄土高原极端气温变化特征及其与平均气温的相关性. 应用生态学报, 2022, 33(7): 1975-1982
[35] 苏日罕, 郭恩亮, 王永芳, 等. 1982—2020年内蒙古地区极端气候变化及其对植被的影响. 生态学报, 2023, 43(1): 419-431
[36] 吴运力, 张钰, 田佳榕. 气候变化和人类活动对内蒙古高原不同植被类型NDVI的影响. 中国农业气象, 2023, 44(12): 1155-1168

2001—2020年全球植被对极端气候的响应

Global vegetation response to extreme climate from 2001 to 2020

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