中国东北地区植被生产力控制因素分析

doi:10.11821/dlxb202001005

摘要/Abstract

摘要：

植被生长季长度和生长强度是形态上影响植被生产力变化的重要因子。全球变暖情景下,北半球中高纬度大部分地区植被生长季显著延长并对植被生产力产生正向反馈,而植被生长强度变化情形及对生产力的控制作用并不清晰。中国东北地区属于中纬度温带地区,具有较高的植被覆盖度和丰富的植被类型,探索其植被生长季长度和强度的变化及对生产力的控制作用有利于理解和应对该地区的生态系统变化。以中国东北为研究区,基于1982—2015年长时序遥感植被指数数据（NDVI3g）,利用曲率求导法确定植被生长季开始点（SOS）、结束点（EOS）、生长季长度（LOS）和夏季最大生长季强度（GM）等关键物候参数,然后利用相对重要性（RI）方法定量分析了生长季长度和强度对植被生产力长期变化趋势的相对贡献及时空格局。结果表明：① 研究区整体的植被生产力和生长强度呈现增强趋势,而生长季长度呈现缩短趋势,导致生长强度成为控制生产力变化趋势的主要因素（RI = 70%）;② 在不同植被覆盖区域,生长季长度和生长强度对生产力的影响程度具有显著的空间差异。西部草原区植被生产力受生长强度控制最为显著（RI = 93%）,其次为针叶林（RI = 66%）和阔叶林区（RI = 62%）,农作物区生产力受生长强度影响最小（RI = 56%）。生长季长度对植被生产力的控制在农作物区最为显著（RI = 40%）,在其他区域的影响约为27%~35%。各植被覆盖区生长强度与生产力均为正相关,生长季长度与生产力均为负相关;③ 气候因素（降水、温度）和物候变化均对主要贡献因子生长强度产生影响,其中SOS的变化对生长强度的影响程度和空间范围最为显著,主要表现为SOS推迟促进生长强度增强。本研究基于遥感数据发现1982—2015年间中国东北地区植被生长更加旺盛,但是植被生长活动主要受生长强度的影响,该研究可以为植被生产力变化模拟的参数选择提供新的线索。

关键词: 植被物候, 植被生产力, 生长季长度, 生长强度, 长期趋势, NDVI

Abstract:

The length and magnitude of vegetation growing season are important factors affecting the change of vegetation productivity during the growth process. Under the context of global warming, vegetation growing season at the middle and high latitudes of the Northern Hemisphere has prolonged significantly and caused positive feedback on vegetation productivity. However, the change of vegetation growth magnitude and its impact on vegetation productivity are still unclear. Northeast China is located in the mid-latitude temperate zone with high vegetation coverage and various vegetation types. Exploring the change of vegetation growth season length and magnitude and their influence on productivity is meaningful for understanding and coping with ecosystem changes in the study area. Based on the long-term GIMMS NDVI3g remote sensing data (1982-2015), the curvature derivation method was used to extract the key vegetation phenological parameters such as start of season (SOS), end of season (EOS), growth season length (LOS) and growth magnitude (GM). Then the relative importance (RI) method was employed to detect the relative contribution of LOS and GM to vegetation productivity (expressed as mean NDVI value in growing season, MGS) in growing season. The results showed that: (1) The overall vegetation productivity and growth magnitude in the study area showed an increasing trend, while the LOS showed a decreasing trend, which led to the GM becoming the main factor controlling the change trend of productivity (RI = 70%); (2) In different vegetation coverage areas, the impact of growth season length and magnitude on productivity showed significant spatial discrepancy. Vegetation productivity in the western grassland region was most significantly controlled by GM (RI = 93%), followed by coniferous forest and broad-leaved forest (RI = 66%, 62%) and crop area was least affected by GM (RI = 56%). The impact of LOS on vegetation productivity is most significant in croplands (RI = 40%) and affects about 27%-35% in other areas. GM was positively correlated with productivity in all vegetation cover areas, while LOS was negatively correlated with productivity; (3) Both climate factors (precipitation, temperature) and phenological changes affect the main contributing factor GM. In detail, the change of SOS has the most significant effect on the GM in a large spatial range. The main manifestation is that delayed SOS can promote GM. Based on remote sensing technique, this study found that vegetation in Northeast China is generally growing more vigorously, but vegetation growth activities are mainly affected by growth magnitude. This study can provide direct evidence for the study of vegetation phenological changes and productivity response under the background of global change.

Key words: vegetation phenology, vegetation productivity, growth season length, growth magnitude, long-term trend, NDVI

周玉科. 中国东北地区植被生产力控制因素分析[J]. 地理学报, 2020, 75(1): 53-67.

ZHOU Yuke. Analysis of controlling factors for vegetation productivity in Northeast China[J]. Acta Geographica Sinica, 2020, 75(1): 53-67.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 47

[1]	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(5573):1687-1689.
[2]	Guo Jian, Chen Shi, Xu Bin , et al. Remote sensing monitoring of grassland vegetation greenup based on SPOT-VGT in XiLingol League. Geographical Research, 2017,36(1):37-48.
	[ 郭剑, 陈实, 徐斌 , 等. 基于SPOT-VGT数据的锡林郭勒盟草原返青期遥感监测. 地理研究, 2017,36(1):37-48.]
[3]	Li Zhengguo, Tang Huajun, Yang Peng , et al. Progress in remote sensing of vegetation phenology and its application in agriculture. Chinese Journal of Agricultural Resources and Regional Planning, 2012,33(5):20-28.
	[ 李正国, 唐华俊, 杨鹏 , 等. 植被物候特征的遥感提取与农业应用综述. 中国农业资源与区划, 2012,33(5):20-28.]
[4]	Song Chunqiao, Ke Linghong, You Songcai , et al. Comparison of three NDVI time-series fitting methods based on TIMESA: Taking the grassland in northern Tibet as case. Remote Sensing Technology and Application, 2011,26(2):147-155.
	[ 宋春桥, 柯灵红, 游松财 , 等. 基于TIMESAT的3种时序NDVI拟合方法比较研究: 以藏北草地为例. 遥感技术与应用, 2011,26(2):147-155.]
[5]	Xu Yunjia, Dai Junhu, Wang Huanjiong , et al. Variations of main phenophases of natural calendar and analysis of responses to climate change in Harbin in 1985-2012. Geographical Research, 2015,34(9):1662-1674.
	[ 徐韵佳, 戴君虎, 王焕炯 , 等. 1985—2012年哈尔滨自然历主要物候期变动特征及对气温变化的响应. 地理研究, 2015,34(9):1662-1674.]
[6]	Keenan T F, Gray J, Friedl M A , et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 2014,4(7):598-604.
[7]	Zhou Yuke . Comparative study of vegetation phenology extraction methods based on digital images. Progress in Geography, 2018,37(8):1031-1044.
	[ 周玉科 . 基于数码照片的植被物候提取多方法比较研究. 地理科学进展, 2018,37(8):1031-1044.]
[8]	Myneni R B, Keeling C D, Tucker C J , et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 1997,386(6626):698-702.
[9]	Richardson A D, Keenan T F, Migliavacca M , et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 2013,169(3):156-173.
[10]	Goetz S J, Epstein H E, Bhatt U S , et al. Recent changes in Arctic vegetation: Satellite observations and simulation model predictions//Gutman G, Reissell A. Eurasian Arctic Land Cover and Land Use in a Changing Climate. Amsterdam, Netherlands: Springer, 2011: 9-36.
[11]	Zhang K, Kimball J S, Mu Q Z , et al. Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. Journal of Hydrology, 2009,379(1):92-110.
[12]	Buermann W, Bikash P R, Jung M , et al. Earlier springs decrease peak summer productivity in North American boreal forests. Environmental Research Letters, 2013,8(2):24-27.
[13]	Reed B C, Brown J F, Vanderzee D , et al. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 1994,5(5):703-714.
[14]	Hudson I L, Keatley M R . Phenological Research: Methods for Environmental and Climate Change Analysis. Amsterdam, Netherland: Springer, 2010.
[15]	Fan Deqin, Zhao Xuesheng, Zhu Wenquan , et al. Phenology of Leymus chinensis steppe in Inner Mongolia and its response to climate changes. Progress in Geography, 2016,35(3):304-319.
	[ 范德芹, 赵学胜, 朱文泉 , 等. 植物物候遥感监测精度影响因素研究综述. 地理科学进展, 2016,35(3):304-319.]
[16]	Dragoni D, Rahman A F . Trends in fall phenology across the deciduous forests of the eastern USA. Agricultural and Forest Meteorology, 2012,157:96-105.
[17]	Hmimina G, Dufrêne E, Pontailler J Y , et al. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment, 2013,132:145-158.
[18]	Fisher J I, Richardson A D, Mustard J F . Phenology model from surface meteorology does not capture satellite-based greenup estimations. Global Change Biology, 2006,13(3):707-721.
[19]	Zhang X, Friedl M A, Schaaf C B , et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 2003,84(3):471-475.
[20]	Wang Zhi . Study on vegetation dynamic based on vegetation phenology and NOAA/AVHRR NDVI in the North-South Transect of Eastern China[D]. Beijing: Chinese Academy of Forestry, 2008.
	[ 王植 . 基于物候表征的中国东部南北样带上植被动态变化研究[D]. 北京: 中国林业科学研究院, 2008.]
[21]	Pei Shunxiang . Phenological response of typical plants at high latitudes, widely distributed species Prunus persica and species Prunus davidina to climate change in China[D]. Beijing: Chinese Academy of Forestry, 2011.
	[ 裴顺祥 . 我国高纬度地区典型植物及全国广布种毛桃、山桃物候对气候变化的响应[D]. 北京: 中国林业科学研究院, 2011.]
[22]	Yu Xinfang, Zhuang Dafang . Monitoring forest phenophases of Northeast China based on MODIS NDVI data. Resources Science, 2006,28(4):111-117.
	[ 于信芳, 庄大方 . 基于MODIS NDVI数据的东北森林物候期监测. 资源科学, 2006,28(4):111-117.]
[23]	Hou Xuehui, Niu Zheng, Gao Shuai , et al. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(1):142-150.
	[ 侯学会, 牛铮, 高帅 , 等. 基于SPOT-VGT NDVI时间序列的农牧交错带植被物候监测. 农业工程学报, 2013,29(1):142-150.]
[24]	Qiu Yue, Fan Deqin, Zhao Xuesheng , et al. Spatio-temporal changes of NPP and its responses to phenology in Northeast China. Geography and Geo-Information Science, 2017,33(5):21-27.
	[ 邱玥, 范德芹, 赵学胜 , 等. 中国东北地区植被NPP时空变化及其对物候的响应研究. 地理与地理信息科学, 2017,33(5):21-27.]
[25]	Wang Hong, Li Xiaobing, Li Xia , et al. The variability of vegetation growing season in the northern China based on NOAA NDVI and MSAVI from 1982 to 1999. Acta Ecologica Sinica, 2007,27(2):504-515.
	[ 王宏, 李晓兵, 李霞 , 等. 基于NOAA NDVI和MSAVI研究中国北方植被生长季变化. 生态学报, 2007,27(2):504-515.]
[26]	Editorial Committee for Vegetation Map of China. Vegetation Map of the People's Republic of China (1:1000000). Beijing: Geological Publishing House, 2007.
	[ 中国科学院中国植被图编辑委员会. 中华人民共和国植被图(1:1000000). 北京: 地质出版社, 2007.]
[27]	Tucker C J, Pinzon J E, Brown M E , et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal Remote Sensing, 2005,26(20):4485-4498.
[28]	Beck H E, McVicar T R, van Dijk A I J M , et al. Global evaluation of four AVHRR-NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sensing of Environment, 2011,115(10):2547-2563.
[29]	Zhang Wen, Bao Gang, Bao Yuhai . Vegetation SOS dynamic monitoring in Inner Mongolia from 1982 to 2013 and its responses to climatic changes. China Agricultural Informatics, 2018,30(2):63-75.
	[ 张雯, 包刚, 包玉海 . 1982—2013年内蒙古植被返青期动态监测及其对气候变化的响应. 中国农业信息, 2018,30(2):63-75.]
[30]	Wang J, Dong J, Yi Y , et al. Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012. Journal of Geophysical Research: Biogeosciences, 2017,122(1):261-278.
[31]	Beck P S A, Atzberger C, Høgda K A , et al. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 2006,100(3):321-334.
[32]	Zhou Yuke, Liu Jianwen . Spatio-temporal analysis of vegetation phenology with multiple methods over the Tibetan Plateau based on MODIS NDVI data. Remote Sensing Technology and Application, 2018,33(3):486-498.
	[ 周玉科, 刘建文 . 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析. 遥感技术与应用, 2018,33(3):486-498.]
[33]	Piao S L, Yin G D, Tan J G , et al. Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 2015,21(4):1601-1609.
[34]	Fu Z, Dong J W, Zhou Y K , et al. Long term trend and interannual variability of land carbon uptake: The attribution and processes. Environment Research Letters, 2017,12(1):014018.
[35]	Meng Shan . Estimation and interaction of marine and terrestrial ecosystem services value in coastal provinces and cities of China. Journal of Green Science and Technology, 2018,16:299-302.
	[ 孟珊 . 沿海省市海洋/陆地生态服务价值估算及相互作用关系. 绿色科技, 2018,16:299-302.]
[36]	Groemping U . Relative importance for linear regression in R: The Package relaimpo. Journal of Statistical Software, 2006,17(1):1-27.
[37]	Huang K, Xia J Y, Wang Y P , et al. Enhanced peak growth of global vegetation and its key mechanisms. Nature Ecology & Evolution, 2018,2(12):1897-1905.
[38]	Zhu W Q, Tian H Q, Xu X F , et al. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006. Global Ecology and Biogeography, 2012,21(2):260-271.
[39]	Wu C Y, Wang X J, Wang H J , et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nature Climate Change, 2018,8(12):1092-1096.
[40]	Buermann W, Forkel M, O’Sullivan M , et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature, 2018,562(7725):110-114.
[41]	Forkel M, Carvalhais N, Rödenbeck C , et al. Enhanced seasonal CO₂ exchange caused by amplified plant productivity in northern ecosystems. Science, 2016,351(6274):696-699.
[42]	Gonsamo A, Chen J M, Ooi Y W . Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Global Change Biology, 2018,24(5):2117-2128.
[43]	Wu D H, Zhao X, Liang S L , et al. Time-lag effects of global vegetation responses to climate change. Global Change Biology, 2015,21(9):3520-3531.
[44]	Ahlström A, Raupach M R, Schurgers G , et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO₂ sink. Science, 2015,348(6237):895-899.
[45]	Gilmanov T G, Tieszen L L, Wylie B K , et al. Integration of CO₂ flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: Potential for quantitative spatial extrapolation. Global Ecology and Biogeography, 2005,14(3):271-292.
[46]	Liu Jiyuan, Kuang Wenhui, Zhang Zengxiang , et al. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica, 2014,69(1):3-14.
	[ 刘纪远, 匡文慧, 张增祥 , 等. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局. 地理学报, 2014,69(1):3-14.]
[47]	Zhang X Y, Liu L L, Henebry G M . Impacts of land cover and land use change on long-term trend of land surface phenology: A case study in agricultural ecosystems. Environmental Research Letters, 2019,14(4):044020.