基于生态地理分区的大兴安岭植被物候时空变化

doi:10.13287/j.1001-9332.201609.003

应用生态学报 ›› 2016, Vol. 27 ›› Issue (9): 2797-2806.doi: 10.13287/j.1001-9332.201609.003

基于生态地理分区的大兴安岭植被物候时空变化

浮媛媛, 赵建军, 张洪岩^*, 贺红士, 郭笑怡

东北师范大学地理科学学院, 长春 130024

收稿日期:2016-01-12 发布日期:2016-09-18
通讯作者: * E-mail: zhy@nenu.edu.cn
作者简介:浮媛媛, 女, 1990年生, 硕士研究生. 主要从事遥感与GIS应用研究. E-mail: fuyy108@nenu.edu.cn
基金资助:
国家自然科学基金项目(41501449)、吉林省科技发展计划项目(20150520069JH)、中央高校基本科研业务费专项资金项目 (2412016KJ026,14QIVJJ025)、中国博士后科学基金项目(2014M561272)和吉林省博士后科研项目启动经费(RB201353)资助

Spatiotemporal variation of vegetation phenology in the Daxing’an Mountains stratified by eco-geographical regions.

FU Yuan-yuan, ZHAO Jian-jun, ZHANG Hong-yan^*, HE Hong-shi, GUO Xiao-yi

School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

Received:2016-01-12 Published:2016-09-18
Contact: * E-mail: zhy@nenu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (41501449), the Science and Technology Development Project of Jilin Province (20150520069JH), the Fundamental Research Funds for the Central Universities (2412016KJ026, 14QIVJJ025), the China Postdoctoral Science Foundation (2014M561272) and the Jilin Postdoctoral Science Foundation (RB201353)

摘要/Abstract

摘要： 植被与气候的关系十分密切,植被物候可作为全球气候变化的指示器.大兴安岭位于我国最北部,对气候变化较为敏感,研究该区植被物候的时空变化对评估全球变化对陆地生态系统的影响具有重要意义.依据中国生态地理区划图,将大兴安岭划分为4个生态研究区域,本文利用GIMMS NDVI 3g遥感数据集分析1982—2012年大兴安岭整体及各生态地理分区植被物候变化.结果表明: 研究期间,所有分区植被生长季开始日期均表现为提前趋势,生长季结束日期均表现为推迟趋势.植被物候对气候因子变化敏感,尤其是对气温的敏感程度高于降水,其中,北段山地落叶针叶林区植被生长季开始日期与春季温度呈显著负相关;除南段草原区外,其他3个分区植被生长季结束日期均与秋季降水呈显著负相关.从整体来看,植被物候随海拔、纬度的变化趋势明显.

Abstract: Vegetation phenology is a good indicator of climate change, because of the close correlation between vegetation and climate. The Daxing’an Mountains, located in Chinese northernmost region, are more sensitive to climate change. Researching the spatio-temporal variation of vegetation phenology in this region is of great significance for assessing the influence of global change on terrestrial ecosystem. According to the eco-geographical zoning of China, the Daxing’an Mountains can be divided into for four eco-geographical regions. In this study, the spatial distribution and dynamics of vegetation phenology in the Daxing’an Mountains were analyzed using 8-km resolution GIMMS NDVI 3g dataset from 1982 to 2012. Results showed that the start of growing season in all partitions exhibited an advancing trend, and the end of growing season in all partitions had an extending trend. Vegetation phenology which was sensitive to the change of meteorological factors had a stronger correlation with temperature than with precipitation for all four eco-geographical regions. There was a significant negative correlation between the start of growing season in the northern region and spring temperature. Except for the south of the Daxing’an Mountains, the ending days of growing season in the other three eco-geographical regions had significant negative correlations with summer precipitation. The change of vegetation phenology in the whole study was obvious along with altitude and latitude.

浮媛媛, 赵建军, 张洪岩, 贺红士, 郭笑怡. 基于生态地理分区的大兴安岭植被物候时空变化[J]. 应用生态学报, 2016, 27(9): 2797-2806.

FU Yuan-yuan, ZHAO Jian-jun, ZHANG Hong-yan, HE Hong-shi, GUO Xiao-yi. Spatiotemporal variation of vegetation phenology in the Daxing’an Mountains stratified by eco-geographical regions.[J]. Chinese Journal of Applied Ecology, 2016, 27(9): 2797-2806.

参考文献

[1] Fang X-Q (方修琦), Yu W-H (余卫红). Progress in the studies on the phonological responding to global warming. Advances in Earth Science (地球科学进展), 2005, 17(5): 715-727 (in Chinese)
[2] Zhang H, Yuan W, Liu S, et al. Sensitivity of flowering phenology to changing temperature in China. Journal of Geophysical Research: Biogeosciences, 2015, 120: 1658-1665
[3] Wu C, Gonsamo A, Gough CM, et al. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of Environment, 2014, 147: 79-88
[4] Richardson AD, Keenan TF, Migliavacca M, et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 2013, 169: 156-173
[5] Hmimina G, Dufrene E, Pontailler J, 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
[6] Wang H, Liu D, Lin H, et al. NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. International Journal of Climatology, 2015, 35: 687-698
[7] Helman D, Lensky IM, Tessler N, et al. A phenology-based method for monitoring woody and herbaceous vegetation in mediterranean forests from NDVI time series. Remote Sensing, 2015, 7: 12314-12335
[8] Osunmadewa BA, Wessollek C, Karrasch P. Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria. Journal of Applied Remote Sensing, 2015, 9: 96029
[9] Delbart N, Le Toan T, Kergoat L, et al. Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004). Remote Sensing of Environment, 2006, 101: 52-62
[10] Wang X, Piao S, Xu X, et al. Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the last three decades? Global Ecology and Biogeography, 2015, 24: 621-631
[11] Hou X-H (侯学会), Niu Z (牛铮), Gao S (高帅). Phenology of forest vegetation in northeast of China in ten years using remote sensing. Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2014, 34(2): 515-519 (in Chinese)
[12] Guo Z-X (国志兴), Zhang X-N (张晓宁), Wang Z-M (王宗明), et al. Responses of vegetation phenology in Northeast China to climate change. Chinese Journal of Ecology (生态学杂志), 2010, 29(3): 578-585 (in Chinese)
[13] Myneni RB, Keeling CD, Tucker CJ, et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 1997, 386: 698-702
[14] Jeganathan C, Dash J, Atkinson PM. Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 2014, 143: 154-170
[15] Xu X-F (于信芳), Zhuang D-F (庄大方). Monitoring forest phenophases of northeast China based on MODIS NDVI data. Resources Science (资源科学), 2006, 28(4): 111-117 (in Chinese)
[16] Zheng D (郑度). The Systematic Research of Chinese Eco-geographical Area. Beijing: Commercial Press, 2008 (in Chinese)
[17] Zhang G-L (张戈丽), Ouyang H (欧阳华), Zhang X-Z (张宪洲), et al. Vegetation change and its responses to climatic variation based on eco-geographical regions of Tibetan Plateau. Geographical Research (地理研究), 2010, 29(11): 2004-2016 (in Chinese)
[18] Guo X-Y (郭笑怡), Zhang H-Y (张洪岩). The vegetation dynamic research under of eco-geographical region framework on Greater Khingan Mountains. Scientia Geographica Sinica (地理科学), 2013, 33(2):181-188 (in Chinese)
[19] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. Geoscience and Remote Sensing, 2002, 40: 1824-1832
[20] Eklundh L, Jonsson P. TIMESAT 3.0 Software Manual. Sweden: Lund University, 2010
[21] Gao F, Morisette JT, Wolfe RE, et al. An algorithm to produce temporally and spatially continuous MODIS-LAI time series. Geoscience and Remote Sensing Letters, 2008, 5: 60-64
[22] Yin H (殷贺), Li Z-G (李正国), Wang Y-L (王仰麟), et al. Assessment of desertification using time series analysis of hyper-temporal vegetation indicator in Inner Mongolia. Acta Geographica Sinica (地理学报), 2011, 66(5): 653-661 (in Chinese)
[23] Cai B-F (蔡博峰), Yu R (于嵘). Advance and evaluation in the long time series vegetation trends research based on remote sensing. Journal of Remote Sen-sing (遥感学报), 2009, 13(6): 1170–1186 (in Chinese)
[24] Zhao J-J (赵建军). The Study of Vegetation Dynamic and Responses to Climate Change along the Appalachian Trail Corridor. PhD Thesis. Changchun: Northeast Normal University, 2013 (in Chinese)
[25] Yu H, Luedeling E, Xu J. Winter and spring warming result in delayed spring phenology on the Tibetan Pla-teau. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107: 22151-22156
[26] Liang C-S (梁灿盛), Yin S-Y (殷淑燕), Li M-R (李美荣), et al. Variation of climate and its impact on phonological period and growth of Pyrus bretschneideri Rehd in Binxian of Shaanxi Province in last 53 years. Agricultural Research in the Arid Areas (干旱地区农业研究), 2012, 30(4): 190-194 (in Chinese)
[27] Hua W (华维), Fan G-Z (范广洲), Chen Q-L (陈权亮), et al. Simulation of influence of climate change on vegetation physiological process and process and feedback effect in Gaize Region. Plateau Meteorology (高原气象), 2010, 29(4): 875-883 (in Chinese)
[28] Zu J-X (俎佳星),Yang J (杨健). Temporal variation of vegetation phenology in northeastern China. Acta Ecologica Sinica (生态学报), 2016, 36(7): 1-9 (in Chinese)
[29] Zhang G, Zhang Y, Dong J, et al. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110: 4309-4314

基于生态地理分区的大兴安岭植被物候时空变化

Spatiotemporal variation of vegetation phenology in the Daxing’an Mountains stratified by eco-geographical regions.

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价