基于NOAA NDVI的植被生长季模拟方法研究
收稿日期: 2006-04-01
修回日期: 2006-08-01
网络出版日期: 2006-11-25
基金资助
国家重点基础研究发展计划项目("973") ( 2006CB400505) ; 国家自然科学基金( 30670398) 资助.
The Methods of Simulating Vegetation Growing Season Based on NOAA NDVI
Received date: 2006-04-01
Revised date: 2006-08-01
Online published: 2006-11-25
在近几年里， 大尺度的植被生长季监测已经成为全球气候变化研究中的一个重要科学问 题。NOAA/NDVI 数据为研究植被生长季时空变化规律提供了重要手段。文章综述并分析比较了 基于NDVI 估测植被生长季开始、结束、长度等特征参数的方法: NDVI 阈值、时间序列分析、物候 期的频率分布型与NDVI 相结合、主分量分析、利用曲线进行拟合等方法。受不同因素影响， 各方 法有不同的应用局限性， 因此， 在以前研究的基础上， 利用较常用的四种方法: 阈值法、滑动平均 法、最大变化斜率、曲线拟合模型模拟了锡林浩特1991～1999 年的草原生长季， 最后利用野外实 测的草原返青期验证了监测结果。结果表明: 与地面观测数据相结合， 基于阈值可得到较好的草 原返青期; 基于曲线拟合模型能适用于大尺度上的植被生长季变化监测， 但存在问题是拟合曲线 很难接近于实际曲线， 因此， 需要进一步研究的内容是选择合适的曲线估测模型， 监测不同植被 类型生长季的年际变化规律。
王宏,李晓兵,莺歌,王丹丹,龙慧灵 . 基于NOAA NDVI的植被生长季模拟方法研究[J]. 地理科学进展, 2006 , 25(6) : 21 -32 . DOI: 10.11820/dlkxjz.2006.06.003
In recent years, estimating vegetation growing season on large scale has become a sig-nificant scientific field in global climate change studies. NOAA/AVHRR NDVI provides important means of research on temporal and spatial variability of vegetation growing season. This paper has summarized, compared and analyzed methods of estimating beginning, end, length of vegetation growing season on the basis of NDVI: NDVI threshold, NDVI time series analysis, combination of frequency distribution pattern of plant phenology with NDVI, principal component analysis, curves fitting model and so on. Influenced by different factors, each method has different limitations in application. Therefore, it is necessary to seek better methods for determining interannual and regional distribution variability of vegetation growing season. And on the basis of previous studies, growing season of grassland in Xilinhaote from 1991 to 1999 is estimated by NDVI threshold method, smoothed moving average, the greatest change slope, and curves fitting model. Then the results were verified with grassland greenup by field survey. Results shows that, combining with field surveying data, grassland greeup with better accuracy is acquired with NDVI threshold method, and curves fitting model can be applied to monitoring vegetation growing season on large scale. However, it is difficult for actual curves to match the simulated curves. Therefore, it is very important to choose appropriate fitting curves for subsequent studies. Furthermore, it is necessary to carry out further research on interannual variability of vegetation growing season.
[1] 朴世龙, 方精云. 1982~1999 年我国陆地植被活动对气候变化响应的季节差异. 地理学报, 2003, 58( 1) : 119～125.
[2] 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.
[3] Intergovernmental Panel on Climate Change. Climate Change 2001:The Scienticfic Basis. Third Assessment Report of Working Group I. Cambridge: Cambrigdge University Press, 2001.
[4] Shigehara K. Phonological observation data in Japan to be utilized as an indicator of climatic variation. In: Proceedings of the International Conference on Climatic Impacts on the Environment and Society. University of Tsukuba, Ibaraki, Japan, 1991,pp C1～C6.
[5] D’Arrigo R, Jacoby G C, Fung I Y. Boreal forest and atmosphere exchange of carbon dioxide. Nature, 1987,329: 321～323.
[6] Chen X, 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. Intermational Journal of Biometeorol, 2001,45:170～177.
[7] 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: 695～702.
[8] 温刚, 符淙斌. 中国东部季风区植被物候季节变化对气候响应的大尺度特征: 多年平均结果. 大气科学, 2000, 24 ( 5) : 676～682.
[9] 史培军, 宫鹏, 李晓兵等. 土地利用/覆盖变化研究的方法与实践. 北京: 科学出版社, 2000.
[10] 李本纲, 陶澍. AVHRR NDVI 与气候因子的相关分析. 生态学报, 2000, 20( 5) : 898～902.
[11] Yu F. Vegetation phonological metrics extraction from AVHRR NDVI time series data sets. Internal Document Kansas Applied Remote Sensing(KARS)Program. 1998,University of Kansas, Lawrence, KS.
[12] Tucker C J, Slayback D A, Pinzon J E, et al. Higher northern latitude photosynthetic and growing season trends from 1982 to1999. International Journal of Biometeorology, 2001, 45: 184～190.
[13] Chen X, Pan W. Relationships among phonological growing season, time- integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China. Intermational Journal of Climatology, 2002, 22: 112～123.
[14] Eleonora R, Akihoko K, Ketut W, et al. NDVI - derived length of the growth period estimations for different vegetation types in Monsoon Asia. IECI Chapter Japan Series, 2001,3:106～109.
[15] Justice C O, Townshend J R G, Holben B N, et al. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing ,1985,6,1271～1318.
[16] Fischer A. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 1994, 48, 220～230.
[17] Markon C J, Fleming MD, Binnian E F. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time- series data. Polar Record, 1995,31:179～190.
[18] Groten S ME, Ocatre R. Monitoring the length of the growing season with NOAA. International Journal of Remote Sensing, 2002, 23(14): 2797～2815.
[19] Li X B, Chen Y H, Fan Y D, et al. Detecting inter- annual variations of vegetation growth based on satellite- sensed vegetation index data from 1983 to 1999. Proceedings of IGARSS, 2003, 5: 3263～3265.
[20] Hoff J C. A practical guide to box- jenkins forecasting. Lifetime Learning Publications,1983, Belmont, CA.
[21] Reed B C, Brown J F, VanderZee D, et al. Measuring phonological variability from satellite imagery. Journal of Vegetation Science, 1994,5:703～714.
[22] Schwartz M D, Reed B C, White M A. Assessing satellite- derived start- of- season(SOS) measures in the Conterminous USA. International Journal of Climatology, 2002,22(14): 1793～1805.
[23] Yu F, Price K P, Lee R Y, et al. Analysis of the relationships between climatic variation and seasonal vegetation develop-ment in eastern central Asia. ASPRS 2000 Annual Conference. Washington D.C. May, 2000.
[24] Hogda K A, Karlsen S R, Solheim I. Climatic change impact on growing season in Fennoscandia studied by a time series of NOAA AVHRR NDVI data. Proceedings of IGARSS, 2001, 9～13.
[25] White M A, Thorton P E, Running S W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles, 1997,11: 217～234.
[26] White M A, Schwartz M D, Running S W. Young students, satellites aid understanding of climate- biosphere link. EOS Transactions, 1999,81:1～5.
[27] 陈效逑, 曹志萍. 植物物候期的频率分布型及其在季节划分中的应用. 地理科学, 1999, 19( 1) : 21～27.
[28] Chen X, 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 Biometeorol, 2000, 44: 97～101.
[29] Badhwar G D, Austin W W, Carnes J G. A semi- automatic technique for multitemporal classification of a given crop within a Landsat scene
[J]. Pattern Recognition, 1982, 15:217～230.
[30] Badhwar G D. Use of Landsat- derived profile features for spring small- grains classification. International Journal of Remote Sensing, 1984, 5: 783～797.
[31] Vapnik V N. Statistical learning theory, in Advances in Neural Information Processing Systems, 1997,Vol. 9, Wiley Inter- Science, New York.
[32] 黄嘉佑. 气象统计分析与预报方法. 北京: 气象出版社, 2000.
[33] Myneni R B, Tucker C J, Rsrar G, et al. Interannual variations in satellite - sensed vegetation index data from 1981 to 1991. Journal of Geophysical Research, 1998, 103: 6145～6160.
[34] Hogda K A, Karlsen S R, Solheim I, et al. The start dates of birch pollen seasons in Fennoscandia studied by NOAA AVHRR NDVI data. Proceeding of IGARSS, 2002, 24～28.
[35] 陈效逑, 谭仲军, 徐成新. 利用植物物候和遥感资料确定中国北方的生长季节. 地学前缘, 2000, 7( 增刊) : 196.
[36] Holben B N. Characteristics of maximum- value composite images for temporal AVHRR data. International Journal of Remote Sensing, 1994, 17, 3547～3565.
[37] 张文江, 高志强. 青藏高原中东部植被覆盖对水热条件的响应研究. 地理科学进展, 2005, 24( 5) : 13～22.
[38] 江东, 王乃斌, 杨小唤, 刘红辉. 植被指数—地面温度特征空间的生态学内涵及其应用. 地理科学进展, 2001, 20 ( 2) : 146～152.
[39] 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.
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