1982-2013年中国植被NDVI空间异质性的气候影响分析

doi:10.11821/dlxb201903010

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

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. 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 JIAO^1,², Shaohong WU^1,³

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/Abstract

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

高江波, 焦珂伟, 吴绍洪. 1982-2013年中国植被NDVI空间异质性的气候影响分析[J]. 地理学报, 2019, 74(3): 534-543.

Jiangbo GAO, Kewei JIAO, Shaohong WU. Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013[J]. Acta Geographica Sinica, 2019, 74(3): 534-543.

图/表 7

表1

图1

图2

表2

图3

图4

图5

参考文献 40

[1]	Wang Q, Zhang Q P, Zhou W.Grassland coverage changes and analysis of the driving forces in Maqu County. Physics Procedia, 2012, 33: 1292-1297. doi: 10.1016/j.phpro.2012.05.213
[2]	Fu Bojie, Yu Dandan, Lü Nan.An indicator system for biodiversity and ecosystem services evaluation in China. Acta Ecologica Sinica, 2017, 37(2): 341-348. doi: 10.5846/stxb201611092273
	[傅伯杰, 于丹丹, 吕楠. 中国生物多样性与生态系统服务评估指标体系. 生态学报, 2017, 37(2): 341-348.] doi: 10.5846/stxb201611092273
[3]	Gao J B, Jiao K W, Wu S H, et al.Past and future effects of climate change on spatially heterogeneous vegetation activity in China. Earth’s Future, 2017, 5(7): 679-692. doi: 10.1002/2017EF000573
[4]	Fang J Y, Tang Y H, Son Y.Why are East Asian ecosystems important for carbon cycle research? Science China-Life Sciences, 2010, 53(7): 753-756. doi: 10.1007/s11427-010-4032-2 pmid: 20697864
[5]	Fang J Y, Piao S L, He J S, et al.Increasing terrestrial vegetation activity in China, 1982-1999. Science in China Series C: Life Sciences, 2004, 47(3): 229-240. doi: 10.1007/BF03182768 pmid: 15524280
[6]	Andrew R L, Guan H D, Batelaan O.Large-scale vegetation responses to terrestrial moisture storage changes. Hydrology and Earth System Sciences, 2017, 21(9): 4469-4478. doi: 10.5194/hess-2016-545
[7]	Zhao M S, Running S W.Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 2010, 329(5994): 940-943. doi: 10.1126/science.1192666 pmid: 20724633
[8]	Jiang L L, Jiapaer G, Bao A M, et al.Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment, 2017, 599: 967-980. doi: 10.1016/j.scitotenv.2017.05.012 pmid: 28505889
[9]	Ding Yongjian, Zhou Chenghu, Shao Mingan, et al.Studies of earth surface processes: Progress and prospect. Advances in Earth Science, 2013, 28(4): 407-419. doi: 10.11867/j.issn.1001-8166.2013.04.0407
	[丁永建, 周成虎, 邵明安, 等. 地表过程研究进展与趋势. 地球科学进展, 2013, 28(4): 407-419.] doi: 10.11867/j.issn.1001-8166.2013.04.0407
[10]	Levine J M.Ecology: A trail map for trait-based studies. Nature, 2015, 529(7585): 163-164. doi: 10.1038/nature16862 pmid: 26700809
[11]	Krishnaswamy J, John R, Joseph S.Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Global Change Biology, 2014, 20(1): 203-215. doi: 10.1111/gcb.12362 pmid: 23966269
[12]	Zhou Guangsheng, He Qijin, Yin Xiaojie.Adaptability and Vulnerability of Chinese Vegetation/ Terrestrial Ecosystems under Climate Change. Beijing: Meteorology Press, 2015.
	[周广胜, 何奇瑾, 殷晓洁. 中国植被/陆地生态系统对气候变化的适应性与脆弱性. 北京: 气象出版社, 2015.]
[13]	Del Grosso S, Parton W, Stohlgren T, et al.Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology, 2008, 89(8): 2117-2126. doi: 10.1890/07-0850.1 pmid: 18724722
[14]	Zeppel M J B, Wilks J V, Lewis J D. Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences, 2014, 11(11): 3083-3093. doi: 10.5194/bg-11-3083-2014
[15]	Seddon A W R, Macias-Fauria M, Long P R, et al. Sensitivity of global terrestrial ecosystems to climate variability. Nature, 2016, 531(7593): 229-243. doi: 10.1038/nature16986 pmid: 26886790
[16]	Baez S, Collins S L, Pockman W T, et al.Effects of experimental rainfall manipulations on Chihuahuan Desert grassland and shrubland plant communities. Oecologia, 2013, 172(4): 1117-1127. doi: 10.1007/s00442-012-2552-0 pmid: 23263528
[17]	Du Jiaqiang, Shu Jianmin, Zhang Linbo, et al.Responses of vegetation to climate change in the headwaters of China's Yellow River Basin based on zoning of dry and wet climate. Chinese Journal of Plant Ecology, 2011, 35(11): 1192-1201. doi: 10.3724/SP.J.1258.2011.01192
	[杜加强, 舒俭民, 张林波, 等. 黄河上游不同干湿气候区植被对气候变化的响应. 植物生态学报, 2011, 35(11): 1192-1201.] doi: 10.3724/SP.J.1258.2011.01192
[18]	Hoover D L, Knapp A K, Smith M D.Resistance and resilience of a grassland ecosystem to climate extremes. Ecology, 2014, 95(9): 2646-2656. doi: 10.1890/13-2186.1
[19]	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. doi: 10.1038/nature12434 pmid: 24005415
[20]	Piao S L, Nan H J, Huntingford C, et al.Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature Communications, 2014, 5: 5018. doi: 10.1038/ncomms6018 pmid: 25318638
[21]	Reyer C P O, Leuzinger S, Rammig A, et al. A plant's perspective of extremes: terrestrial plant responses to changing climatic variability. Global Change Biology, 2013, 19(1): 75-89. doi: 10.1111/gcb.12023 pmid: 3857548
[22]	Wu Shaohong, Zhao Yan, Tang Qiuhong, et al.Land surface pattern study under the framework of Future Earth. Progress in Geography, 2015, 34(1): 10-17. doi: 10.11820/dlkxjz.2015.01.002
	[吴绍洪, 赵艳, 汤秋鸿, 等. 面向“未来地球”计划的陆地表层格局研究. 地理科学进展, 2015, 34(1): 10-17. doi: 10.11820/dlkxjz.2015.01.002
[23]	Han Ya, Zhu Wenbo, Li Shuangcheng.Modelling Relationship between NDVI and Climatic Factors in China Using Geographically Weighted Regression. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 52(6): 1125-1133. doi: 10.13209/j.0479-8023.2015.130
	[韩雅, 朱文博, 李双成. 基于GWR模型的中国NDVI与气候因子的相关分析. 北京大学学报(自然科学版), 2016, 52(6): 1125-1133.] doi: 10.13209/j.0479-8023.2015.130
[24]	Zhao Yufei, Zhu Jiang, Xu Yan.Establishment and assessment of the grid precipitation datasets in China for recent 50 years. Journal of the Meteorological Sciences, 2014, 34(4): 414-420. doi: 10.3969/2013jms.0008
	[赵煜飞, 朱江, 许艳. 近50a中国降水格点数据集的建立及质量评估. 气象科学, 2014, 34(4): 414-420.] doi: 10.3969/2013jms.0008
[25]	Wright C K, de Beurs K M, Henebry G M. Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt. Frontiers of Earth Science, 2012, 6(2): 177-187. doi: 10.1007/sl1707-012-0327-x
[26]	Mao D H, Wang Z M, Luo L, et al.Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China. International Journal of Applied Earth Observation and Geoinformation, 2012, 18(1): 528-536. doi: 10.1016/j.jag.2011.10.007
[27]	Kong Dongdong, Zhang Qiang, Huang Wenlin, et al.Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 2017, 72(1): 39-52. doi: 10.11821/dlxb201701004
	[孔冬冬, 张强, 黄文琳, 等. 1982-2013年青藏高原植被物候变化及气象因素影响. 地理学报, 2017, 72(1): 39-52.] doi: 10.11821/dlxb201701004
[28]	Duo A, Zhao W, Qu X, et al.Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 103-117. doi: 10.1016/j.jag.2016.08.008
[29]	Brunsdon C, Fotheringham A S, Charlton M E.Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 1996, 28(4): 281-298. doi: 10.1111/j.1538-4632.1996.tb00936.x
[30]	Brown S, Versace V L, Laurenson L, et al.Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environmental Modeling and Assessment, 2012, 17(3): 241-254. doi: 10.1007/s10666-011-9289-8
[31]	Zhang Xuemei, Wang Kelin, Yue Yuemin, et al.Factors impacting on vegetation dynamics and spatial non-stationary relationships in karst regions of southwest China. Acta Ecologica Sinica, 2017, 37(12): 4008-4018. doi: 10.5846/stxb201611192354
	[张雪梅, 王克林, 岳跃民, 等. 生态工程背景下西南喀斯特植被变化主导因素及其空间非平稳性. 生态学报, 2017, 37(12): 4008-4018.] doi: 10.5846/stxb201611192354
[32]	Piao S L, Wang X H, Ciais P, et al.Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Global Change Biology, 2011, 17(10): 3228-3239. doi: 10.1111/j.1365-2486.2011.02419.x
[33]	Urban M C.Accelerating extinction risk from climate change. Science, 2015, 348(6234): 571-573. doi: 10.1126/science.aaa4984
[34]	Michaletz S T, Cheng D, Kerkhoff A J, et al.Convergence of terrestrial plant production across global climate gradients. Nature, 2014, 512(7512): 39-43. doi: 10.1038/nature13470 pmid: 25043056
[35]	Brohan P, Kennedy J J, Harris I, et al.Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. Journal of Geophysical Research-Atmospheres, 2006, 111(D12): 121-133. doi: 10.1029/2005JD006548
[36]	Feng X M, Fu B J, Piao S L, et al.Revegetation in China's Loess Plateau is approaching sustainable water resource limits. Nature Climate Change, 2016, 6(11): 1019-1022. doi: 10.1038/nclimate3092
[37]	Wang Q, Ni J, Tenhunen J.Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecology and Biogeography, 2005, 14(4): 379-393. doi: 10.1111/geb.2005.14.issue-4
[38]	Li Hengkai, Liu Xiaosheng, Li Bo, et al.Vegetation Coverage Variations and correlation with geomorphologic factors in Red Soil Region: A case in South Jiangxi Province. Scientia Geographica Sinica, 2014, 34(1): 103-109.
	[李恒凯, 刘小生, 李博, 等. 红壤区植被覆盖变化及与地貌因子关系: 以赣南地区为例. 地理科学, 2014, 34(1): 103-109.]
[39]	Wang J M, Wang H D, Cao Y G, et al.Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area. Scientific Reports, 2016, 6: 22058. doi: 10.1038/srep22058 pmid: 4768095
[40]	Lü Y H, Zhang L W, Feng X M, et al.Recent ecological transitions in China: Greening, browning, and influential factors. Scientific Reports, 2015, 5: 8732. doi: 10.1038/srep08732 pmid: 4348646

回归参数		平均气温	最高气温	最低气温	总降水量	相对湿度
Moran's I	均值	0.75	0.75	0.74	0.76	0.76
Moran's I	变率	0.68	0.73	0.70	0.70	0.63
Z	均值	41.58	42.03	41.22	42.07	41.99
Z	变率	34.18	39.72	36.64	37.28	30.54

	平均气温	最高气温	最低气温	总降水量	相对湿度	其它要素
栅格数量	187	743	131	1278	454	202
比例(%)	6.24	24.81	4.37	42.67	15.16	6.74

1982-2013年中国植被NDVI空间异质性的气候影响分析

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

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 40

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	佟彪, 党安荣, 许剑. 300 BC-1900 AD无定河流域城镇时空格局演变[J]. 地理学报, 2019, 74(8): 1508-1524.
[2]	朱艳硕, 王铮, 程文露. 中国装备制造业的空间枢纽—网络结构[J]. 地理学报, 2019, 74(8): 1525-1533.
[3]	陈小强, 袁丽华, 沈石, 梁晓瑶, 王元慧, 王翔宇, 叶思菁, 程昌秀, 宋长青. 中国及其周边国家间地缘关系解析[J]. 地理学报, 2019, 74(8): 1534-1547.
[4]	高超,汪丽,陈财,罗纲,孙艳伟. 海平面上升风险中国大陆沿海地区人口与经济暴露度[J]. 地理学报, 2019, 74(8): 1590-1604.
[5]	郑景云,刘洋,吴茂炜,张学珍,郝志新. 中国中世纪气候异常期温度的多尺度变化特征及区域差异[J]. 地理学报, 2019, 74(7): 1281-1291.
[6]	刘娟,姚晓军,刘时银,郭万钦,许君利. 1970-2016年冈底斯山冰川变化[J]. 地理学报, 2019, 74(7): 1333-1344.
[7]	周沂,贺灿飞. 中国城市出口产品演化[J]. 地理学报, 2019, 74(6): 1097-1111.
[8]	王少剑,黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素[J]. 地理学报, 2019, 74(6): 1131-1148.
[9]	涂建军,唐思琪,张骞,吴越,罗运超. 山地城市格局对餐饮业区位选择影响的空间异质性[J]. 地理学报, 2019, 74(6): 1163-1177.
[10]	宋雪茜,邓伟,周鹏,张少尧,万将军,刘颖. 两层级公共医疗资源空间均衡性及其影响机制——以分级诊疗改革为背景[J]. 地理学报, 2019, 74(6): 1178-1189.
[11]	杨晴青,刘倩,尹莎,张戬,杨新军,高岩辉. 秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例[J]. 地理学报, 2019, 74(6): 1236-1251.
[12]	杨宇,李小云,董雯,洪辉,何则,金凤君,刘毅. 中国人地关系综合评价的理论模型与实证[J]. 地理学报, 2019, 74(6): 1063-1078.
[13]	马丹阳, 尹云鹤, 吴绍洪, 郑度. 中国干湿格局对未来高排放情景下气候变化响应的敏感性[J]. 地理学报, 2019, 74(5): 857-874.
[14]	程维明,周成虎,李炳元,申元村. 中国地貌区划理论与分区体系研究[J]. 地理学报, 2019, 74(5): 839-856.
[15]	刘俊,黄莉,孙晓倩,李宁馨,张恒锦. 气候变化对中国观鸟旅游的影响——基于鸟类物候变化的分析[J]. 地理学报, 2019, 74(5): 912-922.