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地理科学进展    2019, Vol. 38 Issue (5): 718-730     DOI: 10.18306/dlkxjz.2019.05.009
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
青藏高原植被NDVI对气候因子响应的格兰杰效应分析
周玉科()
中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室,北京 100101
Detecting Granger effect of vegetation response to climatic factors on the Tibetan Plateau
ZHOU Yuke()
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Nature Resources Research, CAS, Beijing 100101, China
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摘要 

多变的气候和复杂的地理环境使得青藏高原植被对气候变化响应敏感,因此分析高原植被与气候因子之间的动态关系对气候变化研究和生态系统管理具有重要意义。论文基于1982—2012年青藏高原气象数据(气温、降水)以及GIMMS NDVI3g遥感数据,在像素级别上运用格兰杰因果关系检验方法,在月尺度和季节尺度上分析了高原植被NDVI(主要是草原)与平均气温、降水量之间的响应情况及因果关系。研究表明:① 月尺度上NDVI与平均气温之间、NDVI与降水量之间的时序平稳性比例高于季节尺度,月尺度下达到平稳性的植被区域分别占99.13%和98.68%,季节尺度下分别占64.01%和71.97%;② 月尺度下高原平均气温和降水量对NDVI影响的滞后期都集中在第12~13个月,荒漠草原、典型草原和草甸3种植被类型的滞后期一致,季节尺度下平均气温和降水量对NDVI影响的滞后期主要分布在第3~4和第6个季度,3种植被类型的滞后期差异性较大;③ 月尺度下,青藏高原约98.95%的植被覆被区的平均气温是引起NDVI变化的格兰杰原因,反之,大部分地区(约89.05%,除高原东南区域)内NDVI也是引起平均气温变化的格兰杰原因;季节尺度下,青藏高原中部以外植被区域(约92.03%)内的平均气温是引起NDVI变化的格兰杰原因,而在东部和西部部分地区(约50.55%)中NDVI也是引起平均气温变化的格兰杰原因;④ 月尺度下,高原东北和西北地区(约72.05%)内的降水量是引起NDVI变化的格兰杰原因,大部分地区(约94.86%,除东南部少量区域)中NDVI是引起降水量变化的格兰杰原因;季节尺度下,高原东南部(约61.43%)地区内的降水量是引起NDVI变化的格兰杰原因,高原中东部地区(约48.98%)中NDVI是引起降水量变化的格兰杰原因。总之,高原植被NDVI与气温、降水的相互作用显著,彼此均可构成格兰杰因果效应,但总体上气候因子的影响程度大于植被的反馈作用,月尺度的效应区域大于季节尺度的效应区域。

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周玉科
关键词 青藏高原平稳性检验格兰杰因果关系多尺度分析滞后阶NDVI气候变化 
Abstract

Due to the complex plateau climate and unique geographical environment, the vegetation responds strongly to climatic shifts on the Tibetan Plateau. Therefore, it is of great significance to discuss the causality between vegetation and climate changes. Using the meteorological dataset including average temperature and precipitation and the GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g remote sensing data from 1982 to 2012 to analyze the causal relationship between NDVI and its influencing factors at the monthly and seasonal scales by the Granger causality test on the pixel level, this study examined the response of plateau vegetation (mainly grassland) to average temperature and precipitation change and causality. The results show that: 1) The stationarity proportion of vegetation NDVI and average temperature (99.13%), NDVI and precipitation (98.68%) at the monthly scale was higher than at the seasonal scale (64.01% and 71.97% respectively). 2) Lagging effects of average temperature on NDVI and precipitation on NDVI were around 12-13 months at the monthly scale and mainly 3, 4, and 6 quarters at the seasonal scale on the Tibetan Plateau. The three vegetation types—desert steppe, typical steppe, and meadow steppe—showed high similarities at the monthly scale, while greater heterogeneity was observed at the seasonal scale. 3) For 98.95% of the area covered by vegetation on the Tibetan Plateau, it is believed that average temperature change was the Granger cause of NDVI change, while for 89.05% of the region (except for the southeast), NDVI change was supposed to be the Granger cause of average temperature change at the monthly scale. At the seasonal scale, average temperature change was considered the Granger cause of NDVI change in 92.03% of the regsion (except for the central part of the Tibetan Plateau). Nevertheless, in the eastern and western parts of the plateau (about 50.55% of the region), NDVI change was interpreted as the Granger cause of average temperature change. 4) In the northeast and northwest of the region (about 98.95% of the area) precipitation change was believed to be the Granger cause of NDVI change, while in 94.86% of the region (except for a few areas in the southeast) NDVI change was supposed to be the Granger cause of precipitation change at the monthly scale. At the seasonal scale, precipitation change was considered the Granger cause of NDVI change in the southeastern part of the plateau (61.43% of the area). Nevertheless, in the central and eastern parts of the region (about 48.98% of the area), NDVI change was interpreted as the Granger cause of precipitation change. Overall, climatic factors on the Tibetan Plateau have an interactive relationship with vegetation and each can cause a Grainger effect to the other, with climatic factors having stronger Grainger effect on vegetation than the other way round. The Granger effect region on the Tibetan Plateau at the monthly scale is larger than the Granger effect region at the seasonal scale.

Key wordsTibetan Plateau    stationarity test    Granger causality    multi-scale analysis    lag order    NDVI    climate change
收稿日期: 2018-09-11      出版日期: 2019-05-28
基金资助:国家自然科学基金项目(41601478, 31700417);国家重点研发计划项目(2016YFC0500103);中国科学院STS项目(KFJ-SW-STS-167);资源与环境信息系统国家重点实验室开放基金(2016)
引用本文:   
周玉科 . 青藏高原植被NDVI对气候因子响应的格兰杰效应分析[J]. 地理科学进展, 2019, 38(5): 718-730.
ZHOU Yuke . Detecting Granger effect of vegetation response to climatic factors on the Tibetan Plateau[J]. PROGRESS IN GEOGRAPHY, 2019, 38(5): 718-730.
链接本文:  
http://www.progressingeography.com/CN/10.18306/dlkxjz.2019.05.009      或      http://www.progressingeography.com/CN/Y2019/V38/I5/718
1 曹鸿兴, 郑艳, 虞海燕, . 2008. 气候检测与归因的格兰杰检验法[J]. 气候变化研究进展, 4(1): 37-41.
[Cao H X, Zheng Y, Yu H Y, et al.2008. Granger causality test for detection and attribution of climate change. Advances in Climate Change Research, 4(1): 37-41. ]
2 曹永福. 2006. 格兰杰因果性检验评述[J]. 数量经济技术经济研究, 23(1): 155-160.
[Cao Y F.2006. A comment on Granger causality test. The Journal of Quantitative & Technical Economics, 23(1): 155-160. ]
3 范广洲, 程国栋. 2002. 影响青藏高原植被生理过程与大气CO2浓度及气候变化的相互作用[J]. 大气科学, 26(4): 509-518. [Fan G Z, Cheng G D.2002. Interactions between physiological process of the Tibetan Plateau vegetation and CO2 concentration and climate change. Chinese Journal of Atmospheric Sciences, 26(4): 509-518. ]
4 范广洲, 华维, 黄先伦, . 2008. 青藏高原植被变化对区域气候影响研究进展[J]. 高原山地气象研究, 28(1): 72-80.
[Fan G Z, Hua W, Huang X L, et al.2008. Advances of the study on influence of vegetation change over Tibetan Plateau on regional climate. Plateau and Mountain Meteorology Research, 28(1): 72-80. ]
5 方锋, 孙兰东, 郭俊琴, . 2014. 中国西北地区城市经济发展对降水趋势的影响[J]. 自然资源学报, 29(11): 1878-1887.
[Fang F, Sun L D, Guo J Q, et al.2014. The impact of urban economic development on precipitation changing trends over northwestern China. Journal of Natural Resources, 29(11): 1878-1887. ]
6 宫攀, 陈仲新. 2009. 基于MODIS数据的东北地区植被物候参数提取[J]. 土壤通报, 40(2): 213-217.
[Gong P, Chen Z X.2009. Regional vegetation phenology monitoring based on MODIS. Chinese Journal of Soil Science, 40(2): 213-217. ]
7 郭燕枝, 刘旭. 2011. 基于格兰杰因果检验和典型相关的农民收入影响因素研究[J]. 农业技术经济, (10): 92-97.
[Guo Y Z, Liu X.2011. Research on the factors affecting farmers' income based on Granger causality test and typical correlation. Journal of Agrotechnical Economics, (10): 92-97. ]
8 侯美亭, 赵海燕, 王筝, . 2013. 基于卫星遥感的植被NDVI对气候变化响应的研究进展[J]. 气候与环境研究, 18(3): 353-364.
[Hou M T, Zhao H Y, Wang Z, et al.2013. Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: A review. Climatic and Environmental Research, 18(3): 353-364. ]
9 孔冬冬, 张强, 黄文琳, . 2017. 1982—2013年青藏高原植被物候变化及气象因素影响[J]. 地理学报, 72(1): 39-52.
[Kong D D, Zhang Q, Huang W L, et al.2017. Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 72(1): 39-52. ]
10 李瑞萍, 郭雪梅, 李智才. 2015. 影响山西省气温变化的可能原因分析与检验[J]. 气象与环境科学, 38(2): 77-81.
[ Li R P, Guo X M, Li Z C.2015. Analysis and test about possible reasons for temperature change in Shanxi Province. Meteorological and Environmental Sciences, 38(2): 77-81. ]
11 李晓兵, 王瑛, 李克让. 2000. NDVI对降水季节性和年际变化的敏感性[J]. 地理学报, 55(S1): 82-89.
[Li X B, Wang Y, Li K R.2000. NDVI sensitivity to seasonal and interannual rainfall variations in northern China. Acta Geographica Sinica, 55(S1): 82-89. ]
12 梁四海, 陈江, 金晓媚, . 2007. 近21年青藏高原植被覆盖变化规律[J]. 地球科学进展, 22(1): 33-40.
[Liang S H, Chen J, Jin X M, et al.2007. Regularity of vegetation coverage changes in the Tibetan Plateau over the last 21 years. Advances in Earth Science, 22(1): 33-40. ]
13 刘劲龙, 徐刚, 杨娟. 2013. 利用格兰杰因果检验法分析重庆极端气温事件及其与平均气温和降水量变化的关系[J]. 中国农业气象, 34(2): 236-242. [Liu J L, Xu G, Yang J.2013. Extreme temperature events in chongqing and their relation with the mean temperature and precipitation change based on Granger causality test. Chinese Journal of Agrometeorology, 34(2): 236-242. ]
14 刘双俞, 张丽, 王翠珍, . 2014. 基于MODIS数据的青藏高原植被物候变化趋势研究(2000年—2010年)[J]. 遥感信息, 29(6): 25-30.
[Liu S Y, Zhang L, Wang C Z, et al.2014. Vegetation phenology in the Tibetan Plateau using MODIS data from 2000 to 2010. Remote Sensing Information, 29(6): 25-30. ]
15 刘亚龙, 王庆, 毕景芝, . 2010. 基于Mann-Kendall方法的胶东半岛海岸带归一化植被指数趋势分析[J]. 海洋学报, 32(3): 79-87.
[Liu Y L, Wang Q, Bi J Z, et al.2010. The analysis of NDVI trends in the coastal zone based on Mann-Kendall test: A case in the Jiaodong Peninsula. Acta Oceanologica Sinica, 32(3): 79-87. ]
16 马晓芳, 陈思宇, 邓婕, . 2016. 青藏高原植被物候监测及其对气候变化的响应[J]. 草业学报, 25(1): 13-21. [Ma X F, Chen S Y, Deng J, et al.2016. Vegetation phenology dynamics and its response to climate change on the Tibetan Plateau. Acta Prataculturae Sinica, 25(1): 13-21. ]
17 穆少杰, 李建龙, 陈奕兆, . 2012. 2001—2010年内蒙古植被覆盖度时空变化特征[J]. 地理学报, 67(9): 1255-1268.
[Mu S J, Li J L, Chen Y Z, et al.2012. Spatial differences of variations of vegetation coverage in Inner Mongolia during 2001-2010. Acta Geographica Sinica, 67(9): 1255-1268. ]
18 孙颖, 尹红, 田沁花, . 2013. 全球和中国区域近50年气候变化检测归因研究进展[J]. 气候变化研究进展, 9(4): 235-245. [Sun Y, Yi H, Tian X H, et al.2013. Recent progress in studies of climate change detection and attribution in the globe and China in the past 50 years. Climate Change Research, 9(4): 235-245. ]
19 王丰龙, 刘云刚. 2013. 中国城市建设用地扩张与财政收入增长的面板格兰杰因果检验[J]. 地理学报, 68(12): 1595-1606.
[Wang F L, Liu Y G.2013. Panel Granger test on urban land expansion and fiscal revenue growth in China's prefecture-level cities. Acta Geographica Sinica, 68(12): 1595-1606. ]
20 严应存, 肖建设, 刘宝康. 2009. 青海省2008年牧草长势综述[J]. 青海气象, (1): 47-50.
[Yan Y C, Xiao J S, Liu B K.2009. Summary of pasture growth in Qinghai Province in 2008. Journal of Qinghai Meteorology, (1): 47-50. ]
21 易会文. 2006. 格兰杰因果检验用法探讨[J]. 中南财经政法大学研究生学报, (5): 34-36.
[Yi H W.2006. Discussion on how to do Granger causality test. Journal of the Postgraduate of Zhongnan University of Economics and Law, (5): 34-36. ]
22 郑祚芳, 张秀丽, 高华. 2012. 北京气候变暖与主要极端气温指数的归因分析[J]. 热带气象学报, 28(2): 277-282.
[Zheng Z F, Zhang X L, Gao H.2012. An attribution analysis between the climate warming and extreme temperature indices in Beijing in the past 49 years. Journal of Tropical Meteorology, 28(2): 277-282. ]
23 周建. 2005. 时间序列建模中滞后阶数选取准则函数的检测效力及其特征[J]. 系统工程理论与实践, 25(11): 20-27.
DOI:     
[Zhou J.2005. The researches on the test power and features on the lagging number selecting criteria about the time series models. Systems Engineering—Theory & Practice, 25(11): 20-27. ]
24 朱玉祥, 赵亮. 2014. 中国近百年地面温度变化自然因子的因果链分析[J]. 气象科技进展, 4(3): 36-40.
[Zhu X Y, Zhao L.2014. The causal chain analysis of natural factors for China surface temperature variation during the recent 100 years. Advances in Meteorological Science and Technology, 4(3): 36-40. ]
25 Aybar A, Iftar A.2002. Overlapping decompositions and expansions of Petri nets[J]. IEEE Transactions on Automatic Control, 47(3): 511-515.http://ieeexplore.ieee.org/document/989151/
DOI: 10.1109/9.989151     
26 Ding M J, Zhang Y L, Sun X M, et al.2013. Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009[J]. Science Bulletin, 58(3): 396-405.
27 Ferrarini L.2002. On the reachability and reversibility problems in a class of Petri nets[J]. IEEE Transactions on Systems Man & Cybernetics, 24(10): 1474-1482.
28 Gülen S G.1996. Is OPEC a cartel? Evidence from cointegration and causality tests[J]. Energy Journal, 17(2): 43-57.
29 He B, Chen A, Jiang W, et al.2017. The response of vegetation growth to shifts in trend of temperature in China[J]. Journal of Geographical Sciences, 27(7): 801-816.http://link.springer.com/10.1007/s11442-017-1407-3
30 Ichii K, Kawabata A, Yamaguchi Y.2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990[J]. International Journal of Remote Sensing, 23(18): 3873-3878.https://www.tandfonline.com/doi/full/10.1080/01431160110119416
31 Nemani R R, Keeling C D, Hashimoto H, et al.2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300: 1560-1563.http://www.sciencemag.org/cgi/doi/10.1126/science.1082750
32 Piao S, Mohammat A, Fang J, et al.2006. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China[J]. Global Environmental Change, 16(4): 340-348.https://linkinghub.elsevier.com/retrieve/pii/S0959378006000227
33 Wang L N, Zheng Q L, Song Q L.2002. Influences of surface conditions over the Tibetan Pleateau on China summer circulation[J]. Journal of Nanjing Institute of Meteorology, 25(2): 186-191.
34 Yang K, Wu H, Qin J, et al.2014. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review[J]. Global and Planetary Change, 112: 79-91.https://linkinghub.elsevier.com/retrieve/pii/S0921818113002713
35 Yang K, Ye B, Zhou D, et al.2011. Response of hydrological cycle to recent climate changes in the Tibetan Plateau[J]. Climatic change, 109(3-4): 517-534.http://link.springer.com/10.1007/s10584-011-0099-4
36 Zhang X, Friedl M A, Schaaf C B, et al.2012. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 84(3): 471-475.
37 Zhou D W, Fan G Z, Huang R H, et al.2007. Interannual variability of the normalized difference vegetation index on the Tibetan Plateau and its relationship with climate change[J]. Advances in Atmospheric Sciences, 24(3): 474-484.http://link.springer.com/10.1007/s00376-007-0474-2
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