地理科学进展 ›› 2012, Vol. 31 ›› Issue (11): 1433-1442.doi: 10.11820/dlkxjz.2012.11.003
刘玲玲1,2, 刘良云1, 胡勇1,2
收稿日期:
2011-11-01
修回日期:
2012-04-01
出版日期:
2012-11-25
发布日期:
2012-11-25
通讯作者:
刘良云(1975-),男,湖南省邵阳人,研究员,博士生导师,从事光学遥感及应用研究.E-mail: lyliu@ceode.ac.cn
作者简介:
刘玲玲(1987-),女,河南周口人,博士研究生,主要从事全球变化与植物物候研究.E-mail:liulingling1002@126.com
基金资助:
国家973 项目(2009CB723902);国家自然科学基金项目(40971197).
LIU Lingling1,2, LIU Liangyun1, HU Yong1,2
Received:
2011-11-01
Revised:
2012-04-01
Online:
2012-11-25
Published:
2012-11-25
摘要: 植被物候是环境条件季节和年际变化最直观、最敏感的生物指示器,物候变化可以反映陆地生态系统对气候变化的快速响应.论文基于1982-2006 年连续25 年的GIMMS AVHRR NDVI数据,采用动态阈值法、延迟滑动平均法,双Logistic 和Savitzky-Golay 方法提取欧亚大陆植被的生长季开始时间,并对不同方法的提取结果进行比较和分析.然后以动态阈值法的物候提取结果,研究了1982-2006 年期间植被物候变化趋势以及物候对温度变化的响应情况.结果表明:动态阈值法在欧亚大陆地区生长季开始时间提取率高,在纬度上的变化趋势稳定;北方森林/针叶林和苔原地区的生长季开始时间提取结果最稳定,低纬度区域的变率最大.1982-2006 年,大部分植被类型的生长季开始时间表现出提早趋势,其中森林覆盖区域提早趋势明显,变化幅度为11.45~15.61 d/25a;除了郁闭式至开放式( > 15%) 灌木丛( < 5 m)植被类型外,植被物候和温度表现出负相关关系,变化幅度为1.32~3.47d/℃,这也验证了近几十年气候变暖的趋势.
刘玲玲, 刘良云, 胡勇. 1982-2006 年欧亚大陆植被生长季开始时间遥感监测分析[J]. 地理科学进展, 2012, 31(11): 1433-1442.
LIU Lingling, LIU Liangyun, HU Yong. Assessment and Intercomparison of Satellite-derived Start-of-Season (SOS) Measures in Eurasia for 1982-2006[J]. PROGRESS IN GEOGRAPHY, 2012, 31(11): 1433-1442.
[1] 任国玉. 气候变暖成因研究的历史, 现状和不确定性.地球科学进展, 2008, 23(10): 1084-1091.[2] 武永峰, 何春阳, 马瑛, 等. 基于计算机模拟的植物返青期遥感监测方法比较研究. 地球科学进展, 2005, 20(7):724-731.[3] Schwartz M, R. Ahas, Aasa A. Onset of spring startingearlier across the Northern Hemisphere. Global ChangeBiology, 2006, 12(2): 343-351.[4] Myneni R, Keeling C, Tucker C, et al. Increased plantgrowth in the northern high latitudes from 1981 to 1991.Nature, 1997, 386(6626): 698-702.[5] Delbart N, Le Toan T, Kergoat L, et al. Remote sensingof spring phenology in boreal regions: A free of snow-effectmethod using NOAA-AVHRR and SPOT-VGT data(1982-2004). Remote Sensing of Environment, 2006, 101(1): 52-62.[6] Zhang X, Friedl M, Schaaf C. Global vegetation phenologyfrom moderate resolution imaging spectroradiometer(MODIS): evaluation of global patterns and comparisonwith in situ measurements. Journal of Geophysical Research,2006, 111(G4): G04017.[7] Studer S, R St ckli, Appenzeller C, et al. A comparativestudy of satellite and ground-based phenology. InternationalJournal of Biometeorology, 2007, 51(5): 405-414.[8] Schwartz M, Reed B, White M. Assessing satellite-derivedstart-of-season measures in the conterminous USA.International Journal of Climatology, 2002, 22(14):1793-1805.[9] Maignan F, Bréon F, Bacour C, et al. Interannual vegetationphenology estimates from global AVHRR measurements:Comparison with in situ data and applications. RemoteSensing of Environment, 2008, 112(2): 496-505.[10] White M, de Beursk K, Didan K, et al. Intercomparison,interpretation, and assessment of spring phenology inNorth America estimated from remote sensing for1982-2006. Global Change Biology, 2009, 15(10):2335-2359.[11] Zhou L, Kaufmann R K, Shabanov N V, et al. Variationsin northern vegetation activity inferred from satellite dataof vegetation index during 1981 to 1999. Journal of GeophysicalResearch, 2001, 106(D17): 20069-20083.[12] Studer S, Appenzeller C, Defila C. Inter-annual variabilityand decadal trends in alpine spring phenology: A multivariateanalysis approach. Climatic Change, 2005, 73(3):395-414.[13] Piao S, Ciais P, Friedlingstein P, et al. Net carbon dioxidelosses of northern ecosystems in response to autumnwarming. Nature, 2008, 451(7174): 49-52.[14] Cleland E, Chiariello N, Loarie S, et al. Diverse responsesof phenology to global changes in a grassland ecosys-tem. Proceedings of the National Academy of Sciences,2006, 103(37): 13740.[15] Piao S, Fang J, Zhou L, et al. Variations in satellite-derivedphenology in China's temperate vegetation. GlobalChange Biology, 2006, 12(4): 672-685.[16] Sparks T, Jeffree E, Jeffree C. An examination of the relationshipbetween flowering times and temperature at thenational scale using long-term phenological records fromthe UK. International Journal of Biometeorology, 2000,44(2): 82-87.[17] Kramer K Phenology. Growth of European trees in relationto climate change[D]. Landbouw-Universität Wageningen,1996.[18] Rötzer T, Chmielewski F M. Phenological maps of Europe.Climate Research, 2001, 18(3): 249-257.[19] 于嵘. 基于遥感时序数据的中国陆地植被覆盖变化分析研究[D]. 北京: 中国科学院遥感应用研究所, 2006.[20] 范锦龙. 复种指数遥感监测方法研究[D]. 北京: 中国科学院研究生院, 2003.[21] Julien Y, Sobrino J. Comparison of cloud-reconstructionmethods for time series of composite NDVI data. RemoteSensing of Environment, 2010, 114(3): 618-625.[22] Menenti M, Azzali S, Verhoef W, et al. Mapping agroecologicalzones and time lag in vegetation growth by meansof Fourier analysis of time series of NDVI images. Advancesin Space Research, 1993, 13(5): 233-237.[23] Roerink G, Su Z, Menenti M. S-SEBI: A simple remotesensing algorithm to estimate the surface energy balance.Physics and Chemistry of the Earth: Part B, Oceans andAtmosphere, 2000, 25(2): 147-157.[24] 方修琦, 余卫红. 物候对全球变暖响应的研究综述. 地球科学进展, 2002, 17(5): 714-719.[25] Yu X, Zhuang D. Monitoring forest phenophases ofNortheast China based on MODIS NDVI Data. ResourcesScience, 2006, 28(4): 111-117.[26] Reed B C, Brown J F, VanderZee D, et al. Measuring phenologicalvariability from satellite imagery. Journal ofVegetation Science, 1994, 5(5): 703-714.[27] Fisher J, Mustard J, Vadeboncoeur M. Green leaf phenologyat Landsat resolution: Scaling from the field to thesatellite. Remote Sensing of Environment, 2006, 100(2):265-279.[28] Jönsson P, Eklundh L. Seasonality extraction by functionfitting to time-series of satellite sensor data. Geoscienceand Remote Sensing, 2002, 40(8): 1824-1832.[29] Zhang X, Friedl M, Schaaf C, et al. Monitoring vegetationphenology using MODIS. Remote Sensing of Environment,2003, 84(3): 471-475.[30] Fisher J, Mustard J. Cross-scalar satellite phenology fromground, Landsat, and MODIS data. Remote Sensing ofEnvironment, 2007, 109(3): 261-273.[31] Delbart N, Kergoat L, Le Toan T, et al. Determination ofphenological dates in boreal regions using normalized differencewater index. Remote Sensing of Environment,2005, 97(1): 26-38.[32] van Leeuwen W J D. Monitoring the effects of forest restorationtreatments on post-fire vegetation recovery withMODIS multitemporal data. Sensors, 2008, 8(3):2017-2042.[33] Jönsson P, Eklundh L. TIMESAT: A program for analyzingtime-series of satellite sensor data. Computers & Geosciences,2004, 30(8): 833-845.[34] Zeng H, Jia G, Epstein H. Recent changes in phenologyover the northern high latitudes detected from multi-satellitedata. Environmental Research Letters, 2011, 6:045508.[35] Zeng H, Jia G, Epstein H. Recent changes in phenologyover the northern high latitudes detected from multi-satellitedata. Environmental Research Letters, 2011, 6(4):045508.[36] Reed B. Trend analysis of time-series phenology of NorthAmerica derived from satellite data. GIScience & RemoteSensing, 2006, 43(1): 24-38.[37] Baldocchi D, Black T, Curtis P, et al. Predicting the onsetof net carbon uptake by deciduous forests with soil temperatureand climate data: A synthesis of FLUXNET data.International Journal of Biometeorology, 2005, 49(6):377-387.[38] Richardson A, T Andy Black, Ciais P, et al. Influence ofspring and autumn phenological transitions on forest ecosystemproductivity. Philosophical Transactions of theRoyal Society B: Biological Sciences, 2010, 365(1555):3227. |
[1] | 邓国富, 李明启. 树轮密度对气候的响应及重建研究进展[J]. 地理科学进展, 2021, 40(2): 343-356. |
[2] | 敖雪, 翟晴飞, 崔妍, 周晓宇, 沈历都, 赵春雨, 宁喜龙. 基于EOF分解的辽宁省城市化气候效应检测[J]. 地理科学进展, 2020, 39(9): 1532-1543. |
[3] | 周美君, 李飞, 邵佳琪, 杨海娟. 气候变化背景下中国玉米生产潜力变化特征[J]. 地理科学进展, 2020, 39(3): 443-453. |
[4] | 宋臻, 史兴民. 雨养农业区农户的气候变化适应行为及影响因素路径分析[J]. 地理科学进展, 2020, 39(3): 461-473. |
[5] | 张学珍, 郑景云, 郝志新. 中国主要经济区的近期气候变化特征评估[J]. 地理科学进展, 2020, 39(10): 1609-1618. |
[6] | 谢正辉, 刘斌, 延晓冬, 孟春雷, 徐宪立, 刘宇, 秦佩华, 贾炳浩, 谢瑾博, 李锐超, 王龙欢, 王妍, 陈思. 应对气候变化的城市规划实施效应评估研究[J]. 地理科学进展, 2020, 39(1): 120-131. |
[7] | 董晓宇, 姚华荣, 戴君虎, 朱梦瑶. 2000—2017年内蒙古荒漠草原植被物候变化及对净初级生产力的影响[J]. 地理科学进展, 2020, 39(1): 24-35. |
[8] | 张湜溪, 戴君虎, 葛全胜. 植物始花期对气候变化响应的激素调控机理研究进展[J]. 地理科学进展, 2019, 38(7): 1045-1055. |
[9] | 方佳毅, 史培军. 全球气候变化背景下海岸洪水灾害风险评估研究进展与展望[J]. 地理科学进展, 2019, 38(5): 625-636. |
[10] | 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. |
[11] | 张会, 李铖, 程炯, 吴志峰, 吴艳艳. 基于“H-E-V”框架的城市洪涝风险评估研究进展[J]. 地理科学进展, 2019, 38(2): 175-190. |
[12] | 赵彦茜, 肖登攀, 柏会子, 陶福禄. 中国作物物候对气候变化的响应与适应研究进展[J]. 地理科学进展, 2019, 38(2): 224-235. |
[13] | 吴其慧, 李畅游, 孙标, 史小红, 赵胜男, 韩知明. 1986—2017年呼伦湖湖冰物候特征变化[J]. 地理科学进展, 2019, 38(12): 1933-1943. |
[14] | 周玉科. 基于数码照片的植被物候提取多方法比较研究[J]. 地理科学进展, 2018, 37(8): 1031-1044. |
[15] | 萧凌波. 1736-1911年中国水灾多发区分布及空间迁移特征[J]. 地理科学进展, 2018, 37(4): 495-503. |
|