1982—2015年中国气候变化和人类活动对植被NDVI变化的影响

doi:10.11821/dlxb202005006

地理学报 ›› 2020, Vol. 75 ›› Issue (5): 961-974.doi: 10.11821/dlxb202005006

• 土地覆被变化与生态系统服务 • 上一篇下一篇

1982—2015年中国气候变化和人类活动对植被NDVI变化的影响

金凯^1,², 王飞^1,^3,⁴(), 韩剑桥^1,³, 史尚渝^3,⁵, 丁文斌¹

1.西北农林科技大学水土保持研究所,杨凌 712100
2.青岛农业大学资源与环境学院,青岛 266109
3.中国科学院水利部水土保持研究所,杨凌 712100
4.中国科学院大学,北京 100049
5.中国科学院地理科学与资源研究所,北京 100101

收稿日期:2019-09-18 修回日期:2020-03-03 出版日期:2020-05-25 发布日期:2020-07-25
通讯作者: 王飞 E-mail:wafe@ms.iswc.ac.cn
作者简介:金凯(1988-), 男, 山东平邑人, 博士, 讲师, 主要从事土地利用/覆被变化及其生态效应研究。E-mail: jinkai- 2014@outlook.com; jinkai@qau.edu.cn
基金资助:
国家重点研发计划(2016YFC0501707);中国科学院国际合作局对外合作重点项目(16146KYSB20150001);国家自然科学基金项目(41771558)

Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015

JIN Kai^1,², WANG Fei^1,^3,⁴(), HAN Jianqiao^1,³, SHI Shangyu^3,⁵, DING Wenbin¹

1.Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi, China
2.College of Resources and Environment, Qingdao Agricultural University, Qingdao 266109, Shandong, China
3.Institute of Soil and Water Conservation, CAS and Ministry of Water Resources, Yangling 712100, Shaanxi, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2019-09-18 Revised:2020-03-03 Online:2020-05-25 Published:2020-07-25
Contact: WANG Fei E-mail:wafe@ms.iswc.ac.cn
Supported by:
National Key R&D Program of China(2016YFC0501707);External Cooperation Program of BIC, Chinese Academy of Sciences(16146KYSB20150001);National Natural Science Foundation of China(41771558)

摘要/Abstract

摘要：

基于中国603个气象站的地表气温和降水观测资料以及GIMMS NDVI3g数据,采用变化趋势分析和多元回归残差分析等方法研究了1982—2015年中国植被NDVI变化特征及其主要驱动因素（即气候变化和人类活动）的相应贡献。结果表明：① 1982—2015年中国植被恢复明显,在选择的32个省级行政区中,山西、陕西和重庆的生长季NDVI增加最快,仅上海生长季NDVI呈减小趋势。② 气候变化和人类活动的共同作用是中国植被NDVI呈现整体快速增加和巨大空间差异的主要原因,其中气候变化对各省生长季NDVI变化的影响在-0.01×10 ^-3~1.05×10 ^-3 a ^-1之间,而人类活动的影响在-0.32×10 ^-3~1.77×10 ^-3 a ^-1之间。③ 气候变化和人类活动分别对中国近34年来植被NDVI的增加贡献了40%和60%;人类活动贡献率超过80%的区域主要集中在黄土高原中部、华北平原以及中国东北和西南等地;人类活动贡献率大于50%的省份有22个,其中贡献率最大的3个地区为上海、黑龙江和云南。研究结果建议应更加重视人类活动在植被恢复中的作用。

关键词: 植被变化, 气候变化, 人类活动, NDVI, 残差分析, 中国

Abstract:

Based on the observed daily temperature and precipitation of the land surface of 603 meteorological stations in China, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) 3rd generation dataset, the changing patterns of NDVI in China during 1982-2015 were investigated and the corresponding contributions of the main driving forces, climatic change and human activities, to these changes were distinguished using the methods of trend analysis and multiple regression residuals analysis. The results showed that vegetation recovered in whole China in research period significantly. Shanghai was the single case with a decrease in growing season NDVI in the selected 32 provincial-level administrative regions, while the growing season NDVI in Shanxi, Shaanxi, and Chongqing increased much faster compared with other regions. The climatic change and human activities drove the NDVI change jointly as main forces in China and induced both a rapid increasing trend on the whole and a huge spatial difference. The impacts of climatic change on NDVI change in the growing-season ranged from -0.01×10 ^-3 a ^-1 to 1.05×10 ^-3 a ^-1, while the impacts of human activities changed from -0.32×10 ^-3 a ^-1 to 1.77×10 ^-3 a ^-1. The contributions of climatic change and human activities accounted for 40% and 60%, respectively, to the increase of NDVI in China in the past 34 years. The regions where the contribution rates of human activities were more than 80% were mainly distributed in the central part of the Loess Plateau, the North China Plain, and the northeast and the southwest of China. There were 22 provincial-level regions where the contributions of human activities were more than 50%, and the shares of contribution induced by human activities in Shanghai, Heilongjiang, and Yunnan were much greater than those of any other regions. The results suggest that we should focus more on the role of human activities in vegetation restoration in the whole country.

Key words: vegetation variation, climatic change, human activities, NDVI, residuals analysis, China

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

JIN Kai, WANG Fei, HAN Jianqiao, SHI Shangyu, DING Wenbin. Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015[J]. Acta Geographica Sinica, 2020, 75(5): 961-974.

图/表 10

图1

表1

表2

植被NDVI变化的驱动因素判定标准及贡献率计算方法"

slope(NDVI_obs)^a	驱动因素	驱动因素的划分标准		驱动因素的贡献率(%)
slope(NDVI_obs)^a	驱动因素	slope(NDVI_CC)^b	slope(NDVI_HA)^c	气候变化	人类活动
> 0	CC & HA	> 0	> 0	$slope (NDV I CC) slope (NDV I obs)$	$slope (NDV I HA) slope (NDV I obs)$
	CC	> 0	< 0	100	0
	HA	< 0	> 0	0	100
< 0	CC & HA	< 0	< 0	$slope (NDV I CC) slope (NDV I obs)$	$slope (NDV I HA) slope (NDV I obs)$
	CC	< 0	> 0	100	0
	HA	> 0	< 0	0	100

表2

图2

图3

表3

图4

图5

图6

图7

参考文献 48

[1]	IPCC. Summary for policymakers//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, United Kingdom and New York, USA: Cambridge University Press, 2013.
[2]	Sun Hongyu, Wang Changyao, Niu Zheng , et al. Analysis of the vegetation cover change and the relationship between NDVI and environmental factors by using NOAA time series data. Journal of Remote Sensing, 1998,2(3):204-210. doi: 10.11834/jrs.19980309
	[ 孙红雨, 王长耀, 牛铮 , 等. 中国地表植被覆盖变化及其与气候因子关系: 基于NOAA时间序列数据分析. 遥感学报, 1998,2(3):204-210.] doi: 10.11834/jrs.19980309
[3]	Fang J Y, Song Y C, Liu H Y , et al. Vegetation-climate relationship and its application in the division of vegetation zone in China. Acta Botanica Sinica, 2002,44(9):1105-1122.
[4]	Piao S, Friedlingstein P, Ciais P , et al. Effect of climate and CO₂ changes on the greening of the Northern Hemisphere over the past two decades. Geophysical Research Letters, 2006,33:L23402. Doi: 10.1029/2006GL028205. doi: 10.1029/2006GL028205
[5]	Kong D, Zhang Q, Singh V P , et al. Seasonal vegetation response to climate change in the Northern Hemisphere (1982-2013). Global and Planetary Change, 2017,148:1-8. doi: 10.1016/j.gloplacha.2016.10.020
[6]	Liu Y, Li Y, Li S , et al. Spatial and temporal patterns of global NDVI trends: Correlations with climate and human factors. Remote Sensing, 2015,7(10):13233-13250. doi: 10.3390/rs71013233
[7]	Yang Xuemei, Yang Taibao, Liu Haimeng , et al. Vegetation variation in the North Hemisphere under climate warming in the last 30 years. Arid Zone Research, 2016,33(2):379-391.
	[ 杨雪梅, 杨太保, 刘海猛 , 等. 气候变暖背景下近30a北半球植被变化研究综述. 干旱区研究, 2016,33(2):379-391.]
[8]	Zhou L, Tucker C J, Kaufmann R K , et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 2001,106(D17):20069-20083. doi: 10.1029/2000JD000115
[9]	Sun W, Song X, Mu X , et al. Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau. Agricultural and Forest Meteorology, 2015, 209- 210(1):87-99. doi: 10.1016/j.agrformet.2015.05.002
[10]	Dana E D, Vivas S, Mota J F . Urban vegetation of Almería City: A contribution to urban ecology in Spain. Landscape and Urban Planning, 2002,59:203-216. doi: 10.1016/S0169-2046(02)00039-7
[11]	Jin K, Wang F, Li P . Responses of vegetation cover to environmental change in large cities of China. Sustainability, 2018,10(1):270. Doi: 10.3390/su10010270. doi: 10.3390/su10010270
[12]	Zhao A, Zhang A, Liu X , et al. Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China. Theoretical and Applied Climatology, 2018,132:555-567. doi: 10.1007/s00704-017-2107-8
[13]	Myneni R B, Hall F G, Sellers P J , et al. The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(2):481-486. doi: 10.1109/TGRS.36
[14]	Lucht W, Prentice I C, Myneni R B , et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 2002,296(31):1687-1689. doi: 10.1126/science.1071828
[15]	Wang J, Meng J J, Cai Y L . Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time-series. Environmental Geology, 2008,54(6):1185-1195. doi: 10.1007/s00254-007-0901-9
[16]	Zhang Y, Zhu Z, Liu Z , et al. Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia. Agricultural and Forest Meteorology, 2016,224:1-10. doi: 10.1016/j.agrformet.2016.04.009
[17]	Tucker C, Pinzόn J, Brown M , et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 2005,26:4485-4498. doi: 10.1080/01431160500168686
[18]	Cheng Fangyan, Liu Shiliang, Yi Yijie , et al. The dynamics and main driving factors of coastal vegetation in Guangxi based on MODIS NDVI. Acta Ecologica Sinica, 2017,37(3):788-797.
	[ 成方妍, 刘世梁, 尹艺洁 , 等. 基于MODIS NDVI的广西沿海植被动态及其主要驱动因素. 生态学报, 2017,37(3):788-797.]
[19]	Fensholt R, Proud S R . Evaluation of earth observation based global long term vegetation trends: Comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment, 2012,119(16):131-147. doi: 10.1016/j.rse.2011.12.015
[20]	Munyati C, Mboweni G . Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: A case study in northwestern South Africa. International Journal of Remote Sensing, 2013,34(7):2253-2267. doi: 10.1080/01431161.2012.743692
[21]	Jin K, Wang F, Yu Q , et al. Varied degrees of urbanization effects on observed surface air temperature trends in China. Climate Research, 2018,76(2):131-143. doi: 10.3354/cr01531
[22]	Piao S, Ciais P, Huang Y , et al. The impacts of climate change on water resources and agriculture in China. Nature, 2010,467:43-51. doi: 10.1038/nature09364
[23]	Pang G, Wang X, Yang M . Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012. Quaternary International, 2017,444(2017):87-96. doi: 10.1016/j.quaint.2016.08.038
[24]	Zhang Chunsen, Hu Yan, Shi Xiaoliang . Analysis of spatial-temporal evolution of vegetation cover in Loess Plateau in recent 33 years based on AVHRR NDVI and MODIS NDVI. Journal of Applied Sciences, 2016,34(6):702-712. doi: 10.3969/j.issn.0255-8297.2016.06.006
	[ 张春森, 胡艳, 史晓亮 . 基于AVHRR和MODIS NDVI数据的黄土高原植被覆盖时空演变分析. 应用科学学报, 2016,34(6):702-712.] doi: 10.3969/j.issn.0255-8297.2016.06.006
[25]	Mao D, Wang Z, 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 Observations and Geoinformation, 2012,18:528-536. doi: 10.1016/j.jag.2011.10.007
[26]	Zhang Yuecong, Zhao Zhiqiang, Li Shuangcheng , et al. Indicating variation of surface vegetation cover using SPOT NDVI in the northern part of North China. Geographical Research, 2008,27(4):745-754.
	[ 张月丛, 赵志强, 李双成 , 等. 基于SPOT NDVI的华北北部地表植被覆盖变化趋势. 地理研究, 2008,27(4):745-754.]
[27]	Piao S, Yin G, Tan J , et al. Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 2015,21(4):1601-1609. doi: 10.1111/gcb.12795
[28]	Deng Chenhui, Bai Hongying, Gao Shan , et al. Spatial-temporal variation of the vegetation coverage in Qinling Mountains and its dual response to climate change and human activities. Journal of Natural Resources, 2018,33(3):425-438.
	[ 邓晨晖, 白红英, 高山 , 等. 秦岭植被覆盖时空变化及其对气候变化与人类活动的双重响应. 自然资源学报, 2018,33(3):425-438.]
[29]	Zheng Jingyun, Bian Juanjuan, Ge Quansheng , et al. The climate regionalization in China for 1981-2010. Chinese Science Bulletin, 2013,58(30):3088-3099. doi: 10.1007/s11434-013-5948-2
	[ 郑景云, 卞娟娟, 葛全胜 , 等. 1981—2010年中国气候区划. 科学通报, 2013,58(30):3088-3099.] doi: 10.1007/s11434-013-5948-2
[30]	Zhu Z C, Bi J, Pan Y Z , et al. Global data sets of vegetation Leaf Area Index (LAI)3g and Fraction of Photo synthetically Active Radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 2013,5(2):927-948. doi: 10.3390/rs5020927
[31]	Anyamba A, Small J, Tucker C , et al. Thirty-two years of Sahelian zone growing season non-stationary NDVI3g patterns and trends. Remote Sensing, 2014,6(4):3101-3122. doi: 10.3390/rs6043101
[32]	Du Jiaqiang, Shu Jianmin, Zhao Chenxi , et al. Comparison of GIMMS NDVI3g and GIMMS NDVIg for monitoring vegetation activity and its responses to climate changes in Xinjiang during 1982-2006. Acta Ecologica Sinica, 2016,36(21):6738-6749.
	[ 杜加强, 舒俭民, 赵晨曦 , 等. 两代AVHRR GIMMS NDVI 数据集的对比分析: 以新疆地区为例. 生态学报, 2016,36(21):6738-6749.]
[33]	Jin Kai . Spatio-temporal variations of vegetation cover and its relationships between climate change and human activities over China[D]. Yangling: Northwest A&F University, 2019.
	[ 金凯 . 中国植被覆盖时空变化及其与气候和人类活动的关系[D]. 杨凌: 西北农林科技大学, 2019.]
[34]	Wang F, Ge Q S . A new estimation of urbanization's contribution to the warming trend in China. Journal of Climate, 2015,28:8923-8938. doi: 10.1175/JCLI-D-14-00427.1
[35]	Song Fuqiang, Kang Muyi, Yang Peng , et al. Comparison and validation of GIMMS, SPOT-VGT and MODIS global NDVI products in the Loess Plateau of northern Shaanxi Province, northwestern China. Journal of Beijing Forestry University, 2010,32(4):72-80.
	[ 宋富强, 康慕谊, 杨朋 , 等. 陕北地区GIMMS、SPOT-VGT和MODIS归一化植被指数的差异分析. 北京林业大学学报, 2010,32(4):72-80.]
[36]	Fang Jingyun, Piao Shilong, He Jinsheng , et al. Vegetation of China invigorated in last 20 years. Science in China: Series C, 2003,33(6):554-565.
	[ 方精云, 朴世龙, 贺金生 , 等. 近20年来中国植被活动在增强. 中国科学(C辑), 2003,33(6):554-565.]
[37]	Zhao Jie, Du Ziqiang, Wu Zhitao , et al. Seasonal variations of day and nighttime warming and their effects on vegetation dynamics in China's temperate zone. Acta Geographica Sinica, 2018,73(3):395-404. doi: 10.11821/dlxb201803001
	[ 赵杰, 杜自强, 武志涛 , 等. 中国温带昼夜增温的季节性变化及其对植被动态的影响. 地理学报, 2018,73(3):395-404.] doi: 10.11821/dlxb201803001
[38]	Evans J, Geerken R . Discrimination between climate and human-induced dryland degradation. Journal of Arid Environments, 2004,57(4):535-554. doi: 10.1016/S0140-1963(03)00121-6
[39]	Wessels K J, Prince S D, Malherbe J , et al. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 2007,68(2):271-297. doi: 10.1016/j.jaridenv.2006.05.015
[40]	Li Jing, Liu Hongbing, Li Caiyun , et al. Changes of green-up day of vegetation growing season based on GIMMS 3g NDVI in northern China in recent 30 years. Scientia Geographica Sinica, 2017,37(4):620-629. doi: 10.13249/j.cnki.sgs.2017.04.016
	[ 李净, 刘红兵, 李彩云 , 等. 基于GIMMS 3g NDVI 的近30年中国北部植被生长季始期变化研究. 地理科学, 2017,37(4):620-629.] doi: 10.13249/j.cnki.sgs.2017.04.016
[41]	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.
[42]	An Youzhi . Vegetation NDVI and phenology change in Northern China based on remote sensing[D]. Shanghai: East China Normal University, 2014.
	[ 安佑志 . 基于遥感的中国北部植被NDVI和物候变化研究[D]. 上海: 华东师范大学, 2014.]
[43]	Xie Baoni . Vegetation dynamics and climate change on the Loess Plateau, China: 1982-2014[D]. Yangling: Northwest A&F University, 2016.
	[ 谢宝妮 . 黄土高原近30年植被覆盖变化及其对气候变化的响应[D]. 杨凌: 西北农林科技大学, 2016.]
[44]	Zhang Qian . Contributions of climate change and human activities to vegetation dynamics of Loess Plateau[D]. Lanzhou: Lanzhou Jiaotong University, 2014.
	[ 张倩 . 气候和人类因素在黄土高原植被覆盖变化中的贡献率研究[D]. 兰州: 兰州交通大学, 2014.]
[45]	Luo L H, Ma W, Zhuang Y L , et al. The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor. Ecological Indicators, 2018,93:24-35. doi: 10.1016/j.ecolind.2018.04.067
[46]	Zhang Baoqing, Wu Pute, Zhao Xining . Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982-2009. Transactions of the CSAE, 2011,27(4):287-293.
	[ 张宝庆, 吴普特, 赵西宁 . 近30a黄土高原植被覆盖时空演变监测与分析. 农业工程学报, 2011,27(4):287-293.]
[47]	Tian Haijing . Assessment of non-climate triggered vegetation trends in China from time series of remotely sensed data: A case study of government-dominated forest construction [D]. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 2017.
	[ 田海静 . 非气候因素引起的中国植被变化遥感诊断: 以林业工程为例[D]. 北京: 中国科学院遥感与数字地球研究所, 2017.]
[48]	Liu X, Zhu X, Pan Y , et al. Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014. Journal of Geographical Sciences, 2016,26(1):45-58. doi: 10.1007/s11442-016-1253-8

slope(NDVI)^a	影响程度
< -2.0	明显抑制
-2.0~-1.0	中度抑制
-1.0~-0.2	轻微抑制
-0.2~0.2	基本无影响
0.2~1.0	轻微促进
1.0~2.0	中度促进
≥ 2.0	明显促进

地区	slope (NDVI_obs)^a	对植被恢复的作用		驱动力	地区	slope(NDVI_obs)^a	对植被恢复的作用		驱动力
地区	slope (NDVI_obs)^a	CC^b	HA^c	驱动力	地区	slope(NDVI_obs)^a	CC^b	HA^c	驱动力
上海	-0.33	基本无影响	轻微抑制	CC & HA	海南	1.18	轻微促进	轻微促进	CC & HA
西藏	0.04	基本无影响	基本无影响	CC	甘肃	1.39	轻微促进	轻微促进	CC & HA
吉林	0.26	基本无影响	基本无影响	CC & HA	河南	1.41	轻微促进	轻微促进	CC & HA
黑龙江	0.44	基本无影响	轻微促进	CC & HA	湖北	1.42	轻微促进	轻微促进	CC & HA
青海	0.47	轻微促进	基本无影响	CC & HA	湖南	1.43	轻微促进	轻微促进	CC & HA
四川	0.51	轻微促进	基本无影响	CC & HA	江西	1.46	轻微促进	轻微促进	CC & HA
内蒙古	0.55	基本无影响	轻微促进	CC & HA	贵州	1.54	轻微促进	中度促进	CC & HA
云南	0.64	基本无影响	轻微促进	CC & HA	宁夏	1.55	轻微促进	轻微促进	CC & HA
浙江	0.68	轻微促进	基本无影响	CC & HA	安徽	1.58	轻微促进	轻微促进	CC & HA
新疆	0.7	轻微促进	轻微促进	CC & HA	广西	1.62	轻微促进	中度促进	CC & HA
台湾	0.83	轻微促进	轻微促进	CC & HA	北京	1.7	轻微促进	中度促进	CC & HA
天津	0.85	轻微促进	轻微促进	CC & HA	山东	1.74	轻微促进	中度促进	CC & HA
广东	0.9	基本无影响	轻微促进	CC & HA	河北	1.78	轻微促进	中度促进	CC & HA
福建	0.91	轻微促进	轻微促进	CC & HA	重庆	2.16	中度促进	中度促进	CC & HA
江苏	1	轻微促进	轻微促进	CC & HA	陕西	2.24	轻微促进	中度促进	CC & HA
辽宁	1.1	基本无影响	轻微促进	CC & HA	山西	2.71	轻微促进	中度促进	CC & HA

1982—2015年中国气候变化和人类活动对植被NDVI变化的影响

Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015

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