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遥感技术与应用  2010, Vol. 25 Issue (1): 118-125    DOI: 10.11873/j.issn.1004-0323.2010.1.118
技术方法     
3种滤波算法对NDVI高质量数据保真性研究
曹云锋1,2,王正兴1,邓芳萍3
1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101;
2.中国科学院研究生院, 北京 100049;3.北京师范大学世界资源研究所,,北京 100875
Fidelity Performance of Three Filters for High Quality NDVI Time-series Analysis
CAO Yun-feng1,2,WANG Zheng-xing1,DEN Fang-ping3
1.Institute of Geographical Sciences and Natural Resources Research,State Key Lab of Resources andEnvironmental Information System,Beijing 10010,China;2.The Graduate University,Chinese Academy of Sciences,Beijng 100049,China;3.Beijing Normal University,World Resources Research,Beijing 100875,China
 全文: PDF(3724 KB)  
摘要:

标准MODIS NDVI产品时间序列仍然存在噪声,必须在利用前处理。理想的去除噪声算法,应该最大限度地去噪,同时最大限度地保留原数据中无噪声像元的真值。以往的研究大多关注不同滤波算法对噪声的处理能力,往往忽略不同算法在滤波处理时对原始高质量数据保真性的研究。基于Timesat 2.3时序滤波工具所提供的3个滤波算法,探讨了原始数据质量差异对滤波算法的保真性的影响以及不同滤波算法对原始高质量数据保真性的差异,通过对比分析发现非对称高斯算法(AG)对原始高质量数据保真性最高,双逻辑曲线拟合算法(DL)性能次之,SG算法拟合结果的保真性较差。

关键词: 植被指数时间序列滤波算法保真性能    
Abstract:

Some noises still exist in standard MODIS NDVI product and need to be screened before further applications.An ideal filter should only remove noisy data,and keep the good data as much as possible.Most previous studies only concerned about the filter's performance in terms of handling noisy data,and neglected filter's capacity to retain the original high\|quality data (fidelity).The present study analyzed three filters included in Timesat 2.3 tools,examined the effect of data quality on the performance of three filter algorithms,both qualitatively and quantitatively.Through comparative analysis,we concluded that Asymmetric Gaussians has the highest fidelity performance of all,followed by Double Logistic algorithm,and Savizky\|Glolay algorithm performance worst.

Key words: NDVI    Time-series    Filters    Fidelity performance
收稿日期: 2009-07-23 出版日期: 2011-11-04
基金资助:

资源与环境信息系统国家重点实验室自主研究课题,MODIS植被指数质量改进关键问题研究。

通讯作者: 王正兴 E-mail:wangzx@igsnrr.ac.cn   
作者简介: 曹云锋 (1984-),男,硕士研究生,主要从事植被遥感研究。E-mail:caoyf.07s@igsnrr.ac.cn。
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引用本文:

曹云锋, 王正兴, 邓芳萍. 3种滤波算法对NDVI高质量数据保真性研究[J]. 遥感技术与应用, 2010, 25(1): 118-125.

CAO Yun-feng, WANG Zheng-xing, DENG Fang-ping. Fidelity Performance of Three Filters for High Quality NDVI Time-series Analysis. Remote Sensing Technology and Application, 2010, 25(1): 118-125.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2010.1.118        http://www.rsta.ac.cn/CN/Y2010/V25/I1/118

[1]Wang Zhengxing,Suo Yuxia,Lin Xin,et al.Advances in AVHRR Global Time Serials:PAL-GIMMS_LTDR[J].Resources Science,2008,30(8):1252-1259.[王正兴,索玉霞,林昕,等.AVHRR全球时间序列研究进展:PAL-GIMMS-LTDR[J].资源科学,2008,30(8):1252-1259.]
[2]Yong S S,Harris R.Changing Patterns of Global-scale Vegetation Photosynthesis,1982-1999[J].International Journal of Remote Sensing,2005,26(20):4537-4563.
[3]Christopher S R,Neigh B,et al.North American Vegetation Dynamics Observed with Multi-resolution Satellite Data[J].Remote Sensing of Environment,2008,112(4):1749-1772.
[4]Anyamba A,Tuker C J.Analysis of Sahelian Vegetation Dynamics Using NOAA-AVHRR NDVI Data from 1981-2003[J].Journal of Arid Environments,2005,63 (3):596-614.
[5]Carreiras J,Pereira J,Shimabukuro Y,et al.Evaluation of Compositing Algorithms over the Brazilian Amazon Using SPOT-4 VEGETATION Data[J].International Journal of Remote Sensing,2003,24(17):3427-3440.
[6]Kobayashi H,Dye D.Atmospheric Conditions for Monitoring the Long-term Dynamics in the Amazon Using Normalized Difference Vegetation Index[J].Remote Sensing of Environment,2005,97(4):519-525.
[7]Goward S,Markham B,Dye D.Normalized Difference Vegetation Index Measurements from the Advanced Very High Resolution Radiometer[J].Remote Sensing of Environment,1991,35(2-3):257-277.
[8]Gutman G,Vegetation Indices from AVHRR:An Update and Future Prospects[J].Remote Sensing of Environment,1991,35(2-3):121-136.
[9]Viovy N,Arino O,Belward A S.The Best Index Slope Extraction(B ISE):A Method for Reducing Noise in NDVI Time Series[J].International Journal of Remote Sensing,1992,13:1585-1590.
[10]Gu Juan,Li Xin,Huang Chunlin.Research on the Reconstructing of Time-series NDVI Data[J].Remote Sensing Technology and Application,2006,21(4):391-394.[顾娟,李新,黄春林.NDVI时间序列数据集重建方法述评[J].遥感技术与应用,2006,21(4):391-394.]
[11]Li Ru,Zhang Xia,Liu Bo,et al.Review on Methods of Remote Sensing Time-series Data Reconstruction[J].Journal of Remote Sensing,2009,13(2):335-340.[李儒,张霞,刘波,等.遥感时间序列数据滤波重建算法发展综述[J].遥感学报,2009,13(2):335-340.]
[12]Lovell J L,Graetz R D.Filtering Pathfinder AVHRR Land NDVI Data for Australia[J].International Journal of Remote Sensing,2001,22(13):2649-2654.
[13]Jnsson P,Eklundh L.Seasonality Extraction by Function-fitting to Time Series of Satellite Sensor Data[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(8):1824-1832.
[14]Sellers P,Tucker C,Collatz G,et al.A Global 10 by10 NDVI Data Set for Global Studies.Part 2:The Generation of Global Fields of Terrestrial Biophysical Parameters from the NDVI[J].International Journal of Remote Sensing,1994,15(17):3519-3545.
[15]Jin Chen,Per Jnsson,Masayuki Tamura,et al.A Simple Method for Reconstructing a High-quality NDVI Time-series Data Set Based on the Savitzky-Golay Filter[J].Remote Sensing of Environment,2004,91:332-344.
[16]Jnsson P,Eklundh L.TIMESAT-A Program for Analyzing Time-series of Satellite Sensor Data[J].Computers and Geoscience,2004,30(8):833-845.
[17]Ma M,Veroustraete F.Reconstructing Pathfinder AVHRR land NDVI Time-series Data for the Northwest of China[J].Advances in Space Research,2006,37(4):835-840.
[18]Beck P,Atzberer C,Hgda,K.et al.Improved Monitoring of Vegetation Dynamics at Very High Latitudes:A New Method Using MODIS NDVI[J].Remote Sensing of Environment,100,321-334.
[19]Hird J N,McDermid G J.Noise Reduction of NDVI Time Series:An Empirical Comparison of Selected Techniques[J].Remote Sensing of Environment,2008,113(1):248-258.
[20]Yang Meihua.The Climatic Features of ChangBaiShan and Its Vertical Climatic Zone on the Northern Slop[J].ACTA Meteorologica Sinica,1981,39(3):311-320.[杨美华.长白山的气候特征与北坡的垂直气候带[J].气象学报,1981,39(3):311-320.]
[21]LPDAAC.MODIS User_Guide.[EB/OL].http://tbrs.arizona.edu/project/MODIS.
[22]Savitzky A,Golay M J E.Smoothing and Differentiation of Data by Simplified Least Squares Procedures[J].Ana lytical Chemistry,1964,36:1627-1639.
[23]Gu Zhihui.A Study of Calculating Multiple Cropping Index of Crop in China Using SPOT/VGT Multi-Temporal NDVI Data[D].Institute of Resources Science,Beijing Normal University,2003.[辜智慧.中国农业复种指数的遥感估算方法研究[D].北京师范大学,2003.]
[24]J nsson P,Eklundh L.TIMESAT-A Program for Analyzing Time-series of Satellite Sensor Data:User's Guide for TIMESAT 2.3[Z].Sweden:Malm and Lund,2006.
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