遥感物候学研究进展

doi:10.11820/dlkxjz.2009.01.005

摘要/Abstract

摘要：

植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器，其发生时间可以反映陆地生态系统对气候变化的快速响应。近年来，遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点，已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理方法的基础上，从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展，并针对目前研究中存在的问题，提出近期遥感物候研究的主要方向：(1)发展一种更具普适性的物候生长季节划分方法；(2)通过开展植物群落的物候观测和选择合适的尺度转换方法，统一地面与遥感的空间信息；(3)定量分析植物物候变化对人类活动的响应机制；(4)选择适宜的数学方法和模型，实现各种不同分辨率遥感数据的融合；(5)通过动态模拟，预测植物物候对未来气候变化的响应。

关键词: 净初级生产量, 农作物估产, 气候变化, 生长季节, 土地覆盖, 遥感物候学

Abstract:

Plant phenological phenomena are the most salient and sensitive bio-indicators of the environmental change at seasonal and interannual scales. Timings of plant phenological phenomena can indicate the rapid response of terrestrial ecosystems to climate change. Since the remote sensed phenology observation is characterized by multi-temporal, broad coverage, spatial continuality, and relatively long time series, recently, it has been an important means for detecting responses and feedbacks of vegetation dynamics to global climate change. On the basis of introducing remote sensing data sets and processing methods for monitoring plant phenology, we systematically reviewed important progresses in remote sensing phenology during the last five years worldwide focusing on identification of the phenological growing season, plant phenology and climate change, plant phenology and net primary production, plant phenology and land cover, and plant phenology and crop yield estimate, and so on. Then, we pointed out some existing problems in the current research, and tried to propose some main research aspects in the near future as follows: (1) developing a kind of more general technique for identifying the phenological growing season using remote sensing data; (2) unifying surface observed and satellite derived spatial information by carrying out plant community phenology observations and selecting appropriate scale transition methods; (3) analyzing quantitatively response mechanisms of plant phenology to human activities; (4) implementing amalgamation of remote sensing data with different spatial resolutions using suitable mathematical methods and models; and (5) estimating possible responses of plant phenology to future climate change by dynamic simulations.

Key words: climate change, crop yield estimate, growing season, land cover, net primary production, remote sensing phenology

陈效逑,王林海. 遥感物候学研究进展[J]. 地理科学进展, 2009, 28(1): 33-40.

CHEN Xiaoqiu, WANG Linhai. Progress in Remote Sensing Phenological Research[J]. PROGRESS IN GEOGRAPHY, 2009, 28(1): 33-40.

参考文献

[1] 竺可桢，宛敏渭. 物候学(第版). 长沙:湖南教育出版社, 1999, 1~4.

[2] Lieth H (ed.). Phenology and Seasonality Modeling. New York: Springer-Verlag, 1974, 4.

[3] Schwartz M D. Green-wave phenology. Nature, 1998, 394: 839~840.

[4] Chen X Q, Hu B, Yu R. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Global Change Biology, 2005, 11: 1118~1130.

[5] Reed B C, Brown J F, VanderZee D, et al. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 1994, 5:703~714.

[6] Moulin S, Kergoat L, Viovy N. Global-scale assessment of vegetation phenology using NOAA/- AVHRR satellite measurements. Journal of Climate, 1997, 10:1154~1170.

[7] 陈效逑, 喻蓉. 1982~1999 年我国东部暖温带植被生长季节的时空变化. 地理学报，2007, 62(1): 41~51.

[8] Reed B C, Brown J F. Trend analysis of time-series phenology derived from satellite data. IEEE Analysis of Multi- temporal Remote Sensing Image, 2005, 5:166~168.

[9] Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing, 1990, 11:2269 ~ 2279.

[10] 张峰, 吴炳方. 利用时序植被指数监测作物物候的方法研究. 农业工程学报, 2004, 20(1):155~159.

[11] Bradley B A, Jacob R W. A curve fitting procedure to derive inter -annual phonologies from time series of noisy satellite NDVI data. Remote Sensing of Environment, 2007, 106:137~145.

[12] 顾娟, 李新. NDVI 时间序列数据集重建方法述评. 遥感技术与应用, 2006,21(4):391~395.

[13] 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, 386:698~702.

[14] Lim C, Kafatos M. Frequency analysis of natural vegetation distribution using NDVI/AVHRR data from 1981 to 2000 for North America: Correlations with SOI. International Journal of Remote Sensing, 2002, 23(17):3347~3383.

[15] Potter C, Klooster S, Myneni R, et al. Continental -scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982 -1998.Global and Planetary Change,2003,39:201~213.

[16] Moody A, Johnson D M. Land -surface phenologies from AVHRR using the discrete Fourier transform. Remote Sensing of Environment, 2001, 75(3):305~323.

[17] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40: 1824~1832.

[18] Chen J, Jonsson 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, 91:332~344.

[19] Zhang X Y, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 2003, 84:471~475.

[20] Fisher J I, Mustard J F, Vadeboncoeur M A. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment, 2006, 100: 265~279.

[21] Sakamoto T,Yokozawa M,Toritani H, et al. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 2005, 96:366~374.

[22] Hermance J F, Jacob R W, Bradley B A, et al. Extracting phenological signals from multiyear AVHRR NDVI time series: Framework for applying high-order annual splines with roughness damping. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10): 3264~3276.

[23] Chen X Q, Pan W F. Relationships among phenological growing season, time-integrated NDVI and climate forcing in the temperate region of Eastern China. International Journal of Climatology, 2002, 22(14): 1781~1792.

[24] 王宏,李晓兵,莺歌等. 基于NOAA-NDVI 的植被生长季模拟方法研究. 地理科学进展, 2006, 25(6):21~32.

[25] Delbart N, Kergoat L, Toan T L, et al. Determination of phenological dates in boreal regions using normalized difference water index. Remote Sensing of Environment, 2005, 97:26~38.

[26] Suzuki R, Nomaki T, Yasunari T. West -east contrast of phenology and climate in northern Asia revealed using a remotely sensed vegetation index. International Journal of Biometeorology, 2003, 47:126~138.

[27] Delbart N, Toan T L, 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.

[28] Chen X Q, Tan Z J, Schwartz M D, et al. Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China. International Journal of Biometeorology, 2000, 44(2): 97~101.

[29] 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.

[30] 王宏,李晓兵,李霞等. 基于NOAA NDVI 和MSAV 研究中国北方植被生长季变化. 生态学报, 2007, 27(2): 504~ 515.

[31] 丁登, 陈效逑. 我国遥感植被生长季节的地面检验研究———以温带草原和暖温带落叶阔叶林区为例. 遥感技术与应用, 2007, 22(3): 382~386.

[32] 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, 2006, 103:13740~13744.

[33] Piao S L, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 2008, 451:49~53.

[34] Zhou L, Tucker C J, Kaufmann RK, et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, 2001, 106(D17): 20069~20083.

[35] Tateishi R, Ebata M. Analysis of phenological change patterns using 1982 -2000 Advanced Very High Resolution Radiometer (AVHRR) data. International Journal of Remote Sensing, 2004, 25(12):2287~2300.

[36] Zhang X Y, Friedl M A, Schaaf C B, et al. Climate controls on vegetation phenological patterns in northern midand high latitudes inferred from MODIS data. Global Change Biology, 2004, 10: 1133～1145.

[37] Yu F F, Price K P, Ellis J, et al. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment, 2003, 87: 42~54.

[38] St?ckli R, Vidale P L. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. International Journal of Remote Sensing, 2004, 25(17):3303~3330.

[39] Weiss J L, Gutzler D S, Allred Coonrod J E, et al. Longterm vegetation monitoring with NDVI in a diverse semiarid setting, central New Mexico, USA. Journal of Arid Environment, 2004, 58:249~272.

[40] Xiao X M, Hagen S, Zhang Q, et al. Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images. Remote Sensing of Environment, 2006, 103:465~473.

[41] Huete A R, Didan K, Shimabukuro Y E, et al. Amazon rainforests green -up with sunlight in dry season. Geophysical Research Letters, 2006, 33, L06405, doi: 10. 1029/2005GL025583.

[42] Piao S L, Fang J Y. Zhou L M, et al. Variations in satellite- derived phenology in China's temperate vegetation. Global Change Biology, 2006, 12: 672~685.

[43] Nemani R, Keeling C, Hashimoto H, et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 2003, 300: 1560~1563.

[44] Hazarika M K,Yasuoka Y, Ito A,et al. Estimation of net primary productivity by integrating remote sensing data with an ecosystem model. Remote Sensing of Environment, 2005, 94:298~310.

[45] Kimball J S, Mcdonald K C, Running S W, et al. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sensing of Environment, 2004, 90: 243~258.

[46] Kimball J S, Zhao M, Mcdonald K C, et al. Satellite remote sensing of terrestrial net primary production for the Pan-Arctic Basin and Alaska. Mitigation and Adaptation Strategies for Global Change, 2006, 11: 783~804.

[47] Picard G, Quegan S, Delbart N, et al. Bud-burst modeling in Siberia and its impact on quantifying the carbon budget. Global Change Biology, 2005, 11:2164~2176.

[48] Xiao X M, Hollinger D, Aber J, et al. Satellite -based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 2004, 89:519~534.

[49] Churkina G, Schimel D, Braswell B H, et al. Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biology, 2005, 11:1777~1787.

[50] Townshend J R, Justice C, Li W, et al. Global land cover classification by remote sensing: Present capabilities and future possibilities. Remote Sensing of Environment, 1991, 35: 243~255.

[51] Aurdal L,Huseby R B,Eikvil L, et al. Use of hidden Markov models and phenology for multitemporal satellite image classification: Applications to mountain vegetation classification. Analysis of Multi-Temporal Remote Sensing Images, 2005, 5:220~224.

[52] Boles S H, Xiao X M, Liu J Y, et al. Land cover characterization of temperate East Asia using multi -temporal VEGETATION sensor data. Remote Sensing of Environment, 2004, 90:477~489.

[53] DeBeurs KM, Henebry G M. Land surface phenology, cli-matic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment, 2004, 89:497~509.

[54] Souza Jr C M. Mapping land use of tropical regions from space. Proceedings of the National Academy of Sciences, 2006, 103(39): 14261~14262.

[55] Morton D C, DeFries R S, Shimabukuro Y E, et al. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon. Proceedings of the National Academy of Sciences, 2006, 103(39):14637~14641.

[56] 郑玉坤,庄大方. 多时相AVHRR 数据的傅立叶分析. 中国科学院研究生院学报, 2003,20(1):62~68.

[57] 宫攀, 陈仲新, 唐华俊等. 基于MODIS 温度/植被指数的东北地区土地覆盖分类. 农业工程学报,2006, 22 (9): 94~99.

[58] 张霞, 孙睿, 张兵等. 基于MODIS 植被指数时间谱的华北平原土地覆盖分类. 农业工程学报,2006,22 (12):128~ 132.

[59] Vina A, Gitelson A A, Rundquist D C, et al. Monitoring maize(Zea mays L) phenology with remote sensing. Agronomy Journal, 2004, 96:1139~1147.

[60] Lobell D B, Asner G P, Ortiz-Monasterio J I,et al. Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties, Agriculture, Ecosystems and Environment, 2003, 94:205~220.

[61] Duchemin B,Hadria R,Rodriguez J C, et al. Spatialisation of a crop model using phenology derived from remote sensing data. Geoscience and Remote Sensing Symposium, 2003, 4:2200~2202.

[62] 张明伟, 周清波,陈仲新等.基于物候模型的作物种植面积变化监测方法. 农业工程学报,2006,22(10):139~144.

[63] 张峰,吴炳方. 泰国水稻种植面积月变化的遥感监测. 遥感学报, 2004, 8(6):664~671.

[64] 武永峰,李茂松,李京. 中国植被绿度期遥感监测方法研究. 遥感学报, 2008,12(1):92~103.

[65] Chen X Q, Xu C X, Tan Z J. An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China. International Journal of Biometeorology, 2001, 45(4): 170~177.

[66] Studer S, St觟ckli R, Appenzeller C, et al. A comparative study of satellite and ground -based phenology. International Journal of Biometeorology, 2007, 51:405~414.