基于蒙特卡洛生存分析探究东北森林物候的影响因素

doi:10.11821/dlxb201903007

摘要/Abstract

摘要：

植被是生态环境变化的指示器,分析植被物候的影响因素不仅有助于气候变化分析,提高区域气候模式的模拟精度,而且对于准确评估植被生长趋势、生产力以及全球碳收支均具有重要意义。基于遥感的植物物候监测已取得了长足的发展和进步,但当前利用大范围、长时间序列的遥感数据分析植被物候影响因素的研究尚不多,采用线性回归模型对非线性的植被物候影响因素进行分析可能存在偏误。因此,本文提出一种基于蒙特卡洛模拟的生存分析方法,对东北森林物候的影响因素进行量化分析。首先利用东北森林地区1982-2009年间AVHRR GIMMS NDVI数据,应用双Logistic曲线拟合方法对植被春季返青期（SOS）、秋季落叶期（EOS）及植被生长期（GSL）进行提取;然后基于蒙特卡洛模拟和生存分析构建植被物候影响因素分析模型;最后运用所构建模型探讨了东北森林区春季返青期、秋季落叶期的可能影响因素。结果发现：温度、降水和风力对中国东北森林关键物候期有一定影响,其中温度是春季返青期和秋季落叶期的最主要驱动因素,长期平均温度比短期内的温度突变对物候影响更显著,落叶期前的风速增加有可能使落叶时间提前;除了环境因素,春季返青早的年间秋季落叶倾向于更晚。研究表明,结合蒙特卡洛方法的生存分析可以较好地对物候期的影响因素进行定量分析,可为物候现象的归因分析提供一种新的方法。

关键词: 生存分析, 蒙特卡洛模拟, 植被物候, 东北森林区, 响应

Abstract:

Quantitative analysis of the influencing factors of vegetation phenology is not only helpful to accurate assessment of impacts of climate change on vegetation but also has great importance in the improvement of regional climate models, as well as accurate estimation of vegetation net primary productivity and carbon balance. Vegetation phenology monitoring based on remote sensing data has made great progress, however, few studies have focused on analyses of the influencing factors of vegetation phenology based on large-scale and time series remote sensing data. The use of the linear regression model in some existing studies has certain limitations due to the nonlinearity of vegetation phenology. In this paper, we propose a Monte Carlo based survival analysis method, which was applied to the forest regions of Northeast China. Start of season (SOS), end of season (EOS) and growing season length (GSL) were firstly extracted from time series AVHRR GIMMS NDVI data of the study area in the period of 1982-2009, using the double logistic curve fitting method. And then the survival analysis model of vegetation phenological influencing factors based on Monte Carlo estimation was constructed. Finally, the proposed method was applied to the forest regions in Northeast China to investigate possible influencing factors of vegetation phenology in the rejuvenation period and deciduous period. Results show that temperature, precipitation, and wind can influence phenology of the forest in the region, with temperature being the primary influencing factor for both start and end of seasons. Long-term changes of average temperature have more significant impacts on the forest phenology, compared with short-term temperature variations. The increase of wind speed before the EOS may lead to an early EOS. In addition to environmental factors, EOS tends to be later if SOS is early. The results also prove that the proposed survival analysis method can provide a good scheme to quantitatively analyze the influencing factors of the phenological periods.

Key words: survival analysis, Monte Carlo, phenology, Northeast Forest Region, Response

卓莉, 张子彦, 雷小雨, 李秋萍, 陶海燕. 基于蒙特卡洛生存分析探究东北森林物候的影响因素[J]. 地理学报, 2019, 74(3): 490-503.

Li ZHUO, Ziyan ZHANG, Xiaoyu LEI, Qiuping LI, Haiyan TAO. Monte Carlo survival analysis on the influencing factors of forest phenology in Northeast China[J]. Acta Geographica Sinica, 2019, 74(3): 490-503.

图/表 6

图1

表1

图2

图3

图4

图5

参考文献 33

[1]	Keenlyside N S, Latif M, Jungclaus J, et al.Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 2008, 453(7191): 84-88. doi: 10.1038/nature06921 pmid: 18451859
[2]	Foster G, Rahmstorf S.Global temperature evolution 1979-2010. Environmental Research Letters, 2011, 6(4): 526-533. doi: 10.1088/1748-9326/6/4/044022
[3]	Tao Zexing, Zhong Shuying, Ge Quansheng, et al.Spatiotemporal variations in flowering duration of woody plants in China from 1963 to 2012. Acta Geographica Sinica, 2017, 72(1): 53-63. doi: 10.11821/dlxb201701005
	[陶泽兴, 仲舒颖, 葛全胜, 等. 1963-2012年中国主要木本植物花期长度时空变化. 地理学报, 2017, 72(1): 53-63.] doi: 10.11821/dlxb201701005
[4]	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. doi: 10.11821/dlyj201701003
	[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.] doi: 10.11821/dlyj201701003
[5]	Kong Dongdong, Zhang Qiang, Huang Wenlin, et al.Vegetation phenology change in Tibetan Plateau from 1982 to2013 and its related meteorological factors. Acta Geologica 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
[6]	Wang Zhiying, Wu Yanhong, Chang Jun, et al.Temporal and spatial variation of lake ice phenology and its influencing factors in the Tibetan Plateau. Journal of Beijing University of Technology, 2017, 43(5): 701-709. doi: 10.11936/bjutxb2016110002
	[王智颖, 吴艳红, 常军, 等. 青藏高原湖冰物候的时空变化及其影响因素. 北京工业大学学报, 2017, 43(5): 701-709.] doi: 10.11936/bjutxb2016110002
[7]	Li Qi, Xue Xue, Li Jianping, et al.Response of natural phenology of elm trees to climate change in Guyuan from 1981 to 2006. Journal of Anhui Agricultural Science, 2010, 38(7): 3552-3555. doi: 10.3969/j.issn.0517-6611.2010.07.089
	[李琪, 薛雪, 李剑萍, 等. 1981-2006年固原市榆树自然物候对气候变化的响应. 安徽农业科学, 2010, 38(7): 3552-3555.] doi: 10.3969/j.issn.0517-6611.2010.07.089
[8]	Zeng H, Jia G, Epstein H.Recent changes in phenology over the northern high latitudes detected from multi-satellite data. Environmental Research Letters, 2011, 6(4): 45508. doi: 10.1088/1748-9326/6/4/045508
[9]	Cong N, Piao S, Chen A, et al.Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis. Agricultural and Forest Meteorology, 2012, 165: 104-113. doi: 10.1016/j.agrformet.2012.06.009
[10]	Cong N, Wang T, Nan H, et al.Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: A multimethod analysis. Global Change Biology, 2013, 19(3): 881-891. doi: 10.1111/gcb.12077 pmid: 23504844
[11]	Cleland E E, Chiariello N R, Loarie S R, et al.Diverse responses of phenology to global changes in a grassland ecosystem. Proceedings of the National Academy of Sciences of USA, 2006, 103(37): 13740-13744. doi: 10.1073/pnas.0600815103 pmid: 16954189
[12]	Chen Xiaoxia, Yan Xiaodong.The effect on climate change on population dynamics of quercus mongolica in Notth Greater Xing'an Mountain. Acta Ecologica Sinica, 2007, 26(8): 1277-1284.
	[程肖侠, 延晓冬. 气候变化对中国大兴安岭森林演替动态的影响. 生态学杂志, 2007, 26(8): 1277-1284.]
[13]	Chen Tingyi.The northeastern China immense woodland, a broad snowland. Land & Resources, 2005(7): 6-14. doi: 10.3969/j.issn.1671-1904.2005.07.001
	[陈廷一. 林海雪原—东北森林资源现状的忧思和出路. 国土资源, 2005(7): 6-14.] doi: 10.3969/j.issn.1671-1904.2005.07.001
[14]	Yu Xinfang, Zhuang Dafang.Monitoring forest phenophases of Northeast China based on MODIS NDVI data. Resources Science, 2006, 28(4): 111-117. doi: 10.3321/j.issn:1007-7588.2006.04.023
	[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.] doi: 10.3321/j.issn:1007-7588.2006.04.023
[15]	Chen Xiaoxia, Yan Xiaodong.Effects of climate change on typical forest in the northeast of China. Acta Ecologica Sinica, 2008, 28(2): 534-543. doi: 10.3321/j.issn:1000-0933.2008.02.011
	[程肖侠, 延晓冬. 气候变化对中国东北主要森林类型的影响. 生态学报, 2008, 28(2): 534-543.] doi: 10.3321/j.issn:1000-0933.2008.02.011
[16]	Hou xuehui, Niu zheng, Gao shuai. Phenology of forest vegetation in northeast of China in ten years using remote sensing. Spectroscopy and Spectral Analysis, 2014, 34(2): 515-519.
	[侯学会, 牛铮, 高帅. 近十年中国东北森林植被物候遥感监测. 光谱学与光谱分析, 2014, 34(2): 515-519.]
[17]	Li Rongping, Zhou Guangsheng.Responses of woody plants phenology to air temperature in Northeast China in 1980-2005. Chinese Journal of Ecology, 2010, 29(12): 2317-2326.
	[李荣平, 周广胜. 1980-2005年中国东北木本植物物候特征及其对气温的响应. 生态学杂志, 2010, 29(12): 2317-2326.]
[18]	Li Rongping, Zhou Guangsheng, Guo Chunming, et al.Phenology characteristics of ulmus pumila and its 1981 to 2005 in Northeast China. Journal of Meteorology and Environment, 2008, 24(5): 20-24. doi: 10.3969/j.issn.1673-503X.2008.05.005
	[李荣平, 周广胜, 郭春明, 等. 1981-2005年中国东北榆树物候变化特征及模拟研究. 气象与环境学报, 2008, 24(5): 20-24.] doi: 10.3969/j.issn.1673-503X.2008.05.005
[19]	Pei Shunxiang, Guo Quanshui, Xin Xuebing, et al.Response of 4 common broad-leaved arbors phenology to climate change in the Northeast China. Scientia Silvae Sinicae, 2011, 47(11): 181-187. doi: 10.11707/j.1001-7488.20111129
	[裴顺祥, 郭泉水, 辛学兵, 等. 我国东北4种常见阔叶乔木物候对气候变化的响应. 林业科学, 2011, 47(11): 181-187.] doi: 10.11707/j.1001-7488.20111129
[20]	Li Ming, Wu Zhengfang, Du Haibo, et al.Study on the change trend of vegetation phenophase in Changbai Mountain based on remote sensing method. Scientia Geographica Sinica, 2011, 31(10): 1242-1248.
	[李明, 吴正方, 杜海波, 等. 基于遥感方法的长白山地区植被物候期变化趋势研究. 地理科学, 2011, 31(10): 1242-1248.]
[21]	Zhang Xiaodong, Zhu Wenbo, Zhang Jingjing, et al.Phenology of forest vegetation and its response to climate change in the Funiu Mountains. Acta Geologica Sinica, 2018, 73(1): 41-53.
	[张晓东, 朱文博, 张静静, 等. 伏牛山地森林植被物候及其对气候变化的响应. 地理学报, 2018, 73(1): 41-53.]
[22]	Park H, Jeong S, Ho C, et al.Nonlinear response of vegetation green-up to local temperature variations in temperate and boreal forests in the Northern Hemisphere. Remote Sensing of Environment, 2015, 165: 100-108. doi: 10.1016/j.rse.2015.04.030
[23]	Iler A M, Høye T T, Inouye D W, et al.Nonlinear flowering responses to climate: are species approaching their limits of phenological change? Philosophical Transactions of the Royal Society of London, 2013, 368(1624): 20120489. doi: 10.1098/rstb.2012.0489 pmid: 3720060
[24]	Diez J M, Ibáñez I, Jr S J, et al.Beyond seasonal climate: Statistical estimation of phenological responses to weather. Ecological Applications, 2014. 24(7): 1793-1802. doi: 10.1890/13-1533.1 pmid: 29210238
[25]	Shigeta K, Kikuchi E, Hagiwara M, et al.The conditional survival with time of intravesical recurrence of Upper Tract Urothelial Carcinoma. Journal of Urology, 2017, 198(6): 1279-1286. doi: 10.1016/j.juro.2017.06.073 pmid: 28634017
[26]	Sullivan J L, Weksler B.Neuroendocrine Tumors of the thymus: Analysis of factors affecting survival in 254 patients. The Annals of Thoracic Surgery, 2017, 103(3): 935-939. doi: 10.1016/j.athoracsur.2016.07.050 pmid: 27720367
[27]	Zhao Junfang, Yan Xiaodong, Jia Gensuo.Simulating the responses of forest net primary productivity and carbon budget to climate change in Northeast China. Acta Ecologica Sinica, 2007, 28(1): 92-102. doi: 10.3321/j.issn:1000-0933.2008.01.011
	[赵俊芳, 延晓冬, 贾根锁. 东北森林净第一性生产力与碳收支对气候变化的响应. 生态学报, 2007, 28(1): 92-102.] doi: 10.3321/j.issn:1000-0933.2008.01.011
[28]	Zhang X, Dan T, Sullivan J T.Diverse responses of vegetation phenology to a warming climate. Geophysical Research Letters, 2007, 34(19): 255-268. doi: 10.1029/2007GL031447
[29]	Chen J, Jönsson P, Tamura M, et al.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 2004, 91(3): 332-344. doi: 10.1016/j.rse.2004.03.014
[30]	Xia Chuanfu, Li Jing, Liu Qinhuo.Review of advances in vegetation phenology monitoring by remote sensing. Journal of Remote Sensing, 2013, 17(1): 1-16. doi: 10.11834/jrs.20131363
	[夏传福, 李静, 柳钦火. 植被物候遥感监测研究进展. 遥感学报, 2013, 17(1): 1-16.] doi: 10.11834/jrs.20131363
[31]	Korkmaz S, Goksuluk D, Zararsiz G, et al.GeneSurv: An interactive web-based tool for survival analysis in genomics research. Computers in Biology and Medicine, 2017, 89: 487-496. doi: 10.1016/j.compbiomed.2017.08.031 pmid: 28889076
[32]	Cong N, Wang T, Nan H, et al.Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: A multimethod analysis. Global Change Biology, 2013, 19(3): 881-891. doi: 10.1111/gcb.12077 pmid: 23504844
[33]	Shen Ke, Wang Fen, Zhang Chao, et al.Using OpenBUGS software implementation network Meta-analysis. Journal of Hubei University of Medicine, 2013(6): 476-479.
	[沈可, 王芬, 张超, 等. 应用OpenBUGS软件实现网状Meta分析. 湖北医药学院学报, 2013(6): 476-479.]

站点号	站点名称	经度(°)	纬度(°)	站点号	站点名称	经度(°)	纬度(°)
1	塔河	124.72	52.35	14	罗子河	130.25	43.7
2	新林	124.33	51.7	15	汪清	129.78	43.3
3	小二沟	123.72	49.2	16	清原	124.92	42.1
4	孙吴	127.35	49.43	17	靖宇	126.82	42.35
5	博克图	121.92	48.77	18	东岗	127.57	42.1
6	伊春	128.92	47.73	19	松江	128.25	42.53
7	鹤岗	130.27	47.33	20	通化	125.9	41.68
8	宝清	132.18	46.32	21	桓仁	125.35	41.28
9	尚志	127.97	45.22	22	临江	126.92	41.8
10	绥芬河	131.17	44.38	23	集安	126.15	41.1
11	永吉	126.52	43.7	24	草河口	123.9	40.88
12	蛟河	127.33	43.7	25	岫岩	123.28	40.28
13	敦化	128.2	43.37	26	宽甸	124.78	40.72