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地理学报 >

2025 , Vol. 80 >Issue 5: 1212 - 1225

DOI: https://doi.org/10.11821/dlxb202505004

植被与碳循环

中国植被光学厚度的时空变化及其影响因素

  • 石曼青 , 1 ,
  • 杨小玉 1 ,
  • 邱建秀 , 1 ,
  • 罗明 1 ,
  • 王前锋 2 ,
  • 王大刚 1
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  • 1.中山大学地理科学与规划学院，广州 510006
  • 2.福州大学环境与安全工程学院，福州 350116
邱建秀(1986-), 女, 福建上杭人, 博士, 教授, 博士生导师, 研究方向为全球水循环关键要素遥感反演、土壤—植被—大气系统水热平衡过程模拟等。E-mail:

石曼青(1999-), 女, 河南洛阳人, 博士生, 研究方向为极端天气变化。E-mail:

收稿日期: 2023-10-31

  修回日期: 2025-04-15

  网络出版日期: 2025-05-23

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Spatio-temporal patterns of vegetation optical depth and its influencing factors over China

  • SHI Manqing , 1 ,
  • YANG Xiaoyu 1 ,
  • QIU Jianxiu , 1 ,
  • LUO Ming 1 ,
  • WANG Qianfeng 2 ,
  • WANG Dagang 1
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  • 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
  • 2. College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China

Received date: 2023-10-31

  Revised date: 2025-04-15

  Online published: 2025-05-23

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摘要

本文基于Ku波段、X波段和C波段微波遥感数据，采用新兴时空热点分析法解析2002—2017年中国植被光学厚度（Vegetation Optical Depth, VOD）的时空演变规律，并探究气候因子与人类活动对其变化的影响。研究发现：① 中国VOD空间分布呈东南向西北递减趋势，中部与南部为VOD热点区，而新疆及内蒙古高原中部为冷点区；三波段分析表明，研究初期全国植被稀疏区普遍呈现“变绿”特征。② 土地利用类型转变对VOD的时空变化具有显著影响，例如草地转为林地与VOD持续和增强的热点区空间一致性高。③ 基于偏最小二乘结构方程模型的归因分析显示：湿润区在热量与水分条件充足时，温度与降水增加会抑制植被生长；干旱区温度抑制作用不显著；青藏高原高寒区的植被生长则受暖湿化协同促进。本文研究成果可为大尺度生态环境评估、驱动机制解析及生态修复政策制定提供方法支撑与数据参考。

关键词： 植被光学厚度; 新兴时空热点分析; 偏最小二乘结构方程模型

本文引用格式

石曼青 , 杨小玉 , 邱建秀 , 罗明 , 王前锋 , 王大刚 . 中国植被光学厚度的时空变化及其影响因素[J]. 地理学报, 2025 , 80(5) : 1212 -1225 . DOI: 10.11821/dlxb202505004

Abstract

This study utilizes emerging hotspot analysis to explore the spatio-temporal trends of vegetation optical depth (VOD) observed in Ku, X, and C microwave bands over China from 2002 to 2017. Furthermore, it analyzes the impacts of anthropogenic activities, represented by land use change, on the spatial and temporal changes in VOD, and employs Partial Least Squares Structural Equation Model to quantitatively assess the climatic effects on VOD changes. Overall, VOD exhibits a southeast-to-northwest gradient over China, with central and southern regions identified as VOD hotspots, while Xinjiang and the central Inner Mongolia Plateau are identified as VOD cold spots. Regions with consistent emerging hotspot analysis results across the three bands demonstrate a "greening" phenomenon in sparsely-vegetated regions nationwide. Additionally, the association between land use change and emerging hotspots reveals strong impacts of human activities on VOD variations. Specifically, persistent and intensified VOD hotspots predominantly correspond to scenarios where grassland is converted to forest. Attribution of VOD changes using Partial Least Squares Structural Equation Modeling indicates that, in the humid zone, where hydrothermal conditions are favorable and soil moisture is abundant, further increases in temperature and precipitation may inhibit vegetation growth. In contrast, in the arid zone, the inhibitory effect of temperature is less prominent. In the Tibetan Plateau, increases in both temperature and precipitation will promote vegetation growth. The insights from this study are expected to provide scientific support for monitoring ecosystem changes, uncovering their driving forces, and assessing the effectiveness of ecological measures.

Key words： vegetation optical depth; emerging hotspot analysis; Partial Least Squares Structural Equation Model

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
Zhao Jing, Li Jing, Mu Xihan, et al. Validation and analysis the fractional vegetation cover product from GF-1 satellite data in China. National Remote Sensing Bulletin, 2023, 27(3): 689-699.

[赵静, 李静, 穆西晗, 等. 高分一号卫星中国植被覆盖度高时空分辨率产品验证与分析. 遥感学报, 2023, 27(3): 689-699.]

[2]
Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 2003, 421(6918): 37-42.

[3]
Li X J, Wigneron J P, Frappart F, et al. Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products. Remote Sensing of Environment, 2021, 253: 112208. DOI: 10.1016/j.rse.2020.112208.

[4]
Li Y Q, Shi J C. Microwave vegetation indices and the application for vegetation optical depth retrieval using WindSat data. URSI General Assembly and Scientific Symposium (URSI GASS), 2014. DOI: 10.1109/URSIGASS.2014.6929694.

[5]
Qiu J X, He C X, Liu X P, et al. Projecting dry-wet abrupt alternation across China from the perspective of soil moisture. Climate and Atmospheric Science, 2024, 7(1): 269. DOI: 10.1038/s41612-024-00808-w.

[6]
Brandt M, Wigneron J P, Chave J, et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nature Ecology & Evolution, 2018, 2(5): 827-835.

[7]
Jin Kai, Wang Fei, Han Jianqiao, et al. Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015. Acta Geographica Sinica, 2020, 75(5): 961-974.

DOI

[金凯, 王飞, 韩剑桥, 等. 1982—2015年中国气候变化和人类活动对植被NDVI变化的影响. 地理学报, 2020, 75(5): 961-974.]

DOI

[8]
Liu Yue, Liu Huanhuan, Chen Yin, et al. Spatio-temporal dynamics of vegetation optical depth and its driving forces in China from 2000 to 2018. Acta Geographica Sinica, 2023, 78(3): 729-745.

DOI

[刘悦, 刘欢欢, 陈印, 等. 2000—2018年中国植被光学厚度时空动态特征及驱动因素. 地理学报, 2023, 78(3): 729-745.]

DOI

[9]
Wang Qi, Chai Linna, Zhao Shaojie, et al. Inversion of winter wheat optical depth based on multi-angular microwave brightness temperature. Remote Sensing Technology and Application, 2015, 30(3): 424-430.

[王琦, 柴琳娜, 赵少杰, 等. 基于多角度微波辐射亮温数据反演冬小麦光学厚度. 遥感技术与应用, 2015, 30(3): 424-430.]

DOI

[10]
Fornell C. A Second Generation of Multivariate Analyses:Volumes I. and II. New York: Praeger Publishers, 1982.

[11]
Moesinger L, Dorigo W, de Jeu R, et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth System Science Data, 2020, 12(1): 177-196.

DOI

[12]
Harris I, Osborn T J, Jones P, et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 2020, 7(1): 109. DOI: 10.1038/s41597-020-0453-3.

PMID

[13]
Hamed K H, Rao A R. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 1998, 204(1-4): 182-196.

[14]
Barboza G E, Schiamberg L B, Pachl L. A spatiotemporal analysis of the impact of COVID-19 on child abuse and neglect in the city of Los Angeles, California. Child Abuse & Neglect, 2021, 116: 104740. DOI: 10.1016/j.chiabu.2020.104740.

[15]
Pandey B, Khatiwada J R, Zhang L, et al. Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China. Ecology and Evolution, 2020, 10(17): 9474-9485.

DOI PMID

[16]
Hair J F, Astrachan C B, Moisescu O I, et al. Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 2021, 12(3): 100392. DOI: 10.1016/j.jfbs.2020.100392.

[17]
Dash G, Paul J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 2021, 173: 121092. DOI: 10.1016/j.techfore.2021.121092.

[18]
Henseler J, Sarstedt M. Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 2013, 28(2): 565-580.

[19]
Xin Z B, Xu J X, Zheng W. Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981-2006): Impacts of climate changes and human activities. Science in China Series D: Earth Sciences, 2008, 51(1): 67-78.

[20]
Tian F, Brandt M, Liu Y Y, et al. Mapping gains and losses in woody vegetation across global tropical drylands. Global Change Biology, 2017, 23(4): 1748-1760.

DOI PMID

[21]
Yang Xuhong, Jin Xiaobin, Yang Yongke, et al. Spatial and temporal explicit analysis of forestland of Northeast China in 1950-2020. Scientia Geographica Sinica, 2022, 42(11): 1996-2005.

DOI

[杨绪红, 金晓斌, 杨永可, 等. 1950—2020年东北地区林地时空变化特征分析. 地理科学, 2022, 42(11): 1996-2005.]

DOI

[22]
Bai Kunli, Chen Leiyi, Deng Hongtao, et al. Vegetation carbon status of different types of forest ecosystems in south China. Forestry and Environmental Science, 2022, 38(6): 102-108.

[白昆立, 陈蕾伊, 邓洪涛, 等. 华南地区不同类型森林生态系统植被碳现状研究. 林业与环境科学, 2022, 38(6): 102-108.]

[23]
Zhu Junqiang. The national project of returning grazing land to grassland issued a new policy. Industry of China, 2011,(10): 18-19.

[朱军强. 国家退牧还草工程出台新政策. 中国产业, 2011(10): 18-19.]

[24]
Ren Y J, Qiu J X, Zeng Z Z, et al. Earlier spring greening in Northern Hemisphere terrestrial biomes enhanced net ecosystem productivity in summer. Communications Earth & Environment, 2024, 5(1): 122. DOI: 10.1038/s43247-024-01270-5.

[25]
Jiao W Z, Wang L X, Smith W K, et al. Observed increasing water constraint on vegetation growth over the last three decades. Nature Communications, 2021, 12(1): 3777. DOI: 10.1038/s41467-021-24016-9.

[26]
Li Maohua, Du Jinkang, Li Wantong, et al. Global vegetation change and its relationship with precipitation and temperature based on GLASS-LAI in 1982-2015. Scientia Geographica Sinica, 2020, 40(5): 823-832.

DOI

[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.]

DOI

[27]
Mo Xingguo, Liu Suxia, Hu Shi. Co-evolution of climate-vegetation-hydrology and its mechanisms in the source region of Yellow River. Acta Geographica Sinica, 2022, 77(7): 1730-1744.

DOI

[莫兴国, 刘苏峡, 胡实. 黄河源区气候—植被—水文协同演变及成因辨析. 地理学报, 2022, 77(7): 1730-1744.]

DOI

[28]
Brandt M, Mbow C, Diouf A A, et al. Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Global Change Biology, 2015, 21(4): 1610-1620.

DOI PMID

[29]
Teskey R, Wertin T, Bauweraerts I, et al. Responses of tree species to heat waves and extreme heat events. Plant, Cell & Environment, 2015, 38(9): 1699-1712.

[30]
Piao S L, Cui M D, Chen A P, et al. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology, 2011, 151(12): 1599-1608.

[31]
Guo W W, Huang S Z, Huang Q, et al. Drought trigger thresholds for different levels of vegetation loss in China and their dynamics. Agricultural and Forest Meteorology, 2023, 331: 109349. DOI: 10.1016/j.agrformet.2023.109349.

[32]
Li W T, Migliavacca M, Forkel M, et al. Widespread increasing vegetation sensitivity to soil moisture. Nature Communications, 2022, 13(1): 3959. DOI: 10.1038/s41467-022-31667-9.

[33]
Shen M G, Wang S P, Jiang N, et al. Plant phenology changes and drivers on the Qinghai-Tibetan Plateau. Nature Reviews Earth & Environment, 2022, 3(10): 633-651.

[34]
Shen M G, Piao S L, Chen X Q, et al. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Global Change Biology, 2016, 22(9): 3057-3066.

DOI PMID

[35]
Peng S S, Piao S L, Ciais P, et al. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 2013, 501(7465): 88-92.

[36]
Yang Tianyao, Qiu Jianxiu, Xiao Guoan. Agricultural drought monitoring and winter wheat yield estimation in North China. Acta Ecologica Sinica, 2023, 43(5): 1936-1947.

[杨天垚, 邱建秀, 肖国安. 华北农业干旱监测与冬小麦估产研究. 生态学报, 2023, 43(5): 1936-1947.]

[37]
Ma N, Zhang Y Q. Increasing Tibetan Plateau terrestrial evapotranspiration primarily driven by precipitation. Agricultural and Forest Meteorology, 2022, 317: 108887. DOI: 10.1016/j.agrformet.2022.108887.

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