地理学报 ›› 2019, Vol. 74 ›› Issue (3): 534-543.doi: 10.11821/dlxb201903010

• 气候变化与地表过程 • 上一篇    下一篇

1982-2013年中国植被NDVI空间异质性的气候影响分析

高江波1(), 焦珂伟1,2, 吴绍洪1,3   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101
    2. 中国科学院沈阳应用生态研究所 中国科学院森林生态与管理重点实验室,沈阳 110016
    3. 中国科学院大学,北京 100049
  • 收稿日期:2017-08-21 修回日期:2018-12-06 出版日期:2019-03-25 发布日期:2019-03-19
  • 作者简介:

    高江波(1984- ), 男, 山东临沂人, 副研究员, 主要从事地气相互作用、土地利用—地表过程—资源环境效应研究。E-mail: gaojiangbo@igsnrr.ac.cn

  • 基金资助:
    国家自然科学基金项目(41530749, 41671098);中国科学院战略性先导科技专项(XDA20020202);国家重点基础研究发展计划课题(2015CB452702);国家重点研发计划课题(2018YFC1508801)

Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013

Jiangbo GAO1(), Kewei JIAO1,2, Shaohong WU1,3   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, CAS, Shenyang 110016, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-08-21 Revised:2018-12-06 Online:2019-03-25 Published:2019-03-19
  • Supported by:
    National Natural Science Foundation of China, No.41530749, No.41671098;Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA20020202;National Basic Research Program of China, No.2015CB452702;National Key R&D Program of China, No.2018YFC1508801

摘要:

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。

关键词: NDVI, 气候变化, 空间异质性, 地理加权回归, 中国

Abstract:

Climate change is a major driver of vegetation activity, and thus its complex processes become a frontier and difficulty in global change research. To understand the complex relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2013 in China were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China. The significant negative temperature-vegetation relationship was distributed in northeast, northwest and southeast parts of the country, while the positive correlation was more concentrated from southwest to northeast. And then, by comparing the normalized regression coefficients for different climate factors, regions with moisture dominants for NDVI were observed in North China and the Tibetan Plateau, and regions with temperature dominants for NDVI were distributed in the East, Central and Southwest China, where the annual mean maximum temperature accounts for the largest areas. In addition, regression coefficients between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, there was a positive correlation between vegetation dynamics and climate variability. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

Key words: NDVI, climate change, spatial heterogeneity, GWR, China