基于NDVI时间序列的水稻面积提取研究
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摘要
随着光谱仪器的飞速发展,它提供的多样性遥感数据同时加快了国内外对农作物提取方法的研究水平。由于MODIS数据具有免费、多时相性等优点逐渐成为国家粮食作物种植面积统计的重点研究对象。然而由于数据本身存在的大量坏数据严重制约了研究中的各项分析结果,特别对MODIS-NDVI时间序列的分析产生了很大影响,导致分类精度不高。本次研究采用一种先进的滤波处理方法—SG滤波平滑处理来改善数据的不足。从结果中可以得出：利用去除MODIS数据中噪声的方法,不会导致数据中细节信息的丢失,更重要的是经过TIMESAT处理后的影像中包含了所有地物物候期的关键信息。通过将其与NDVI时间序列结合分析,可以达到精确区分不同种作物的目的。
通过对重建后的NDVI时间序列进行重点分析,将水体、建筑等典型地物进行剔除；并着重就水稻、小麦、林地等的关键物候信息进行对比研究。利用不同作物间的生长周期、生长幅度、生长长度以及生长中的NDVI最大值各不相同,采用先进的决策树进行分类规则训练,提取江苏省水稻的空间分布及种植面积。最后,通过多种手段,对分类后的面积进行定性和定量评价,得出98.6%的高精度。这不仅满足了国家对大范围内作物预测的要求,而且该方法更具有很强的实际应用价值。
Along with the rapid development of spectral instrument, the multiple remote sensing data which it provided accelerates domestic and foreign to the crops extraction method research speed. With the free, composed multi-temporal MODIS images have gradually become the advantages of national grain equal emphasis of crop planting area of statistics. However, the existence of bad data itself have seriously restricted the data analysis results of the study, especially on MODIS-NDVI time series analysis has great influence, causing the classification accuracy is not high. This study adopts an advanced filter processing methods-SG smoothing filter to improve data. From the results can be obtained:the methods by removing the noise of composed multi-temporal MODIS images, won't cause the loss of data in detail information, more important is TIMESAT after processed image contains all the key information phenology features. Through it with the NDVI time series union analysis, may achieve the precise discrimination different crops the goal.
Based on the reconstruction of NDVI time series analysis, we can be on the typical residential buildings, wafer to eliminate, then we will emphatically on wheat、rice、forest on key phenology information comparative study. Using different crops growth cycle, the growth margin, growth and growing NDVI maximum length of each are not identical, Using different crops growth cycle, the growth margin, growth and growing NDVI maximum length of each are not identical, adopts advanced decision tree to carry on the classified rule training, then we can got the rice planting area and spatial distribution. Finally, through many kinds of methods carry on qualitative and the quantitative evaluation to the area, and obtains 98.6% high accuracy. Not only this has satisfied the country to the wide range in the crops forecast request, moreover this method has the very strong practical application value.
通过对重建后的NDVI时间序列进行重点分析,将水体、建筑等典型地物进行剔除；并着重就水稻、小麦、林地等的关键物候信息进行对比研究。利用不同作物间的生长周期、生长幅度、生长长度以及生长中的NDVI最大值各不相同,采用先进的决策树进行分类规则训练,提取江苏省水稻的空间分布及种植面积。最后,通过多种手段,对分类后的面积进行定性和定量评价,得出98.6%的高精度。这不仅满足了国家对大范围内作物预测的要求,而且该方法更具有很强的实际应用价值。
Along with the rapid development of spectral instrument, the multiple remote sensing data which it provided accelerates domestic and foreign to the crops extraction method research speed. With the free, composed multi-temporal MODIS images have gradually become the advantages of national grain equal emphasis of crop planting area of statistics. However, the existence of bad data itself have seriously restricted the data analysis results of the study, especially on MODIS-NDVI time series analysis has great influence, causing the classification accuracy is not high. This study adopts an advanced filter processing methods-SG smoothing filter to improve data. From the results can be obtained:the methods by removing the noise of composed multi-temporal MODIS images, won't cause the loss of data in detail information, more important is TIMESAT after processed image contains all the key information phenology features. Through it with the NDVI time series union analysis, may achieve the precise discrimination different crops the goal.
Based on the reconstruction of NDVI time series analysis, we can be on the typical residential buildings, wafer to eliminate, then we will emphatically on wheat、rice、forest on key phenology information comparative study. Using different crops growth cycle, the growth margin, growth and growing NDVI maximum length of each are not identical, Using different crops growth cycle, the growth margin, growth and growing NDVI maximum length of each are not identical, adopts advanced decision tree to carry on the classified rule training, then we can got the rice planting area and spatial distribution. Finally, through many kinds of methods carry on qualitative and the quantitative evaluation to the area, and obtains 98.6% high accuracy. Not only this has satisfied the country to the wide range in the crops forecast request, moreover this method has the very strong practical application value.
引文
[1]赵英时.遥感应用分析原理与方法[M].北京：科学出版社,2003
[2]YangBangjie, PeiZhiyuan, ZhouQingbo, etal. Key technologies of crop mo-nitoring using remote sensing gatanational scale; Progress and problems[J].Transactions of the CSAE, 2002,18(3):191-194
[3]周清波,国内外农情遥感现状与发展趋势[J].中国农业资源与区划,第25卷,第5期,2004年10月
[4]范磊,程永政,刘婷等,基于MODIS数据的河南省冬小麦长势监测研究[J],河南农业科学,第8期,2008年
[5]杨小唤、张香平等,基于MODIS时序NDVI特征值提取多作物播种面积的方法,资源科学,2004年06期.
[6]任建强,陈仲新,唐华俊,基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J],应用生态学报,第17卷,第12期,2006年12月.
[7]宫攀,唐华俊,陈仲新等；基于MODIS-LST修正NDVI时序数列的土地覆盖分类[J],资源科学,2006年04期.
[8]徐晓桃,韩涛,颉耀文；基于单时相MODIS数据的土地覆盖三种分类方法对比研究[J],干旱地区农业研究,2008,(03).
[9]刘勇洪,牛铮；基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究[J].遥感技术与应用,2004,(04).
[10]盛昊,李均力,杨辽等；MODIS-NDVI时序数据分析方法研究—以塔里木河下游第七次秋季输水为例[J],干旱区地理,2007,30(2).
[11]荆凤,申旭辉,洪顺英等；遥感技术在地震科学研究中的应用[J]；国土资源遥感；2008年第2期.
[12]刘海启等；美国农业遥感技术应用状况概述[J]；中国农业资源与区划1992,20(2)：56-60.
[13]刘海启等.欧盟MARS计划简介与我国农业遥感应用思路[J]；中国农业资源与区划,1999,20(3)：55-57..
[14]Reed B C, Brown J F, Vander zee D, et al. Measuring phonological varia-bility from satellite imagery. Journal of Vegetation Science,1994,5(5):703-710.
[15]Wu B F, Zhang F, Liu C L, et al. An Integrated Method for Crop Condition Monitoring [J]. Journal of Remote Sensing,2004,8 (6):498-514.
[16]Jonsson P,& Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geo-science and Rem-ote Sensing,2002,40 (8),1824-1832
[17]Young S S and Wang C Y. Land-cover changes analysis of China using glo-bal-scale Pathfinder AVHRR Land cover (PAL) data [J]. Int J Remote Sens-ing,2001,22(2): 1457-1477.
[18]Running S W, Loveland T R, Pierce L L, et al. A remote sensing based vegetation classi-fication logic for global land cover analysis [J]. Remote Sensing of Environment,1995,51 (1):39-48.
[19]吴炳方；中国国情遥感监测研究[J],中国科学院院刊,第3期,第19卷,2004年.
[20]竺可桢,宛敏渭；物候学[M].长沙：湖南教育出版社,1999.
[21]张学霞,葛全胜等；遥感技术在植物物候研究中的应用综述[J].地球科学进展,2003,18(4)：534-544.
[22]王梨村；中国古今物候学[M].成都：四川大学出版社,1990.174-182.
[23]方修琦,余卫红；物候对全球变暖响应的研究综述[J].地球科学进展,2002,17(5)：714-719.
[24]杨小唤,张香平,江东.基于MODIS时序NDVI特征值提取多作物播种面积的方法[J],资源科学,第26卷,第6期,2004年.
[25]张明伟,周清波,陈仲新等；基于MODIS时序数据分析的作物识别方法[J].中国农业资源与区划,第29卷,第1期,2008年2月.
[26]马明国,潘小多,李新等；长时间序列中国植被指数数据集(1998-2008) [EB/OL].兰州：中国科学院寒区旱区环境与工程研究所.2007.
[27]程乾,黄敬峰,王人潮等；水稻叶面积指数与MODIS植被指数、红边位置之间的分析[J],农业工程学报,2005,19(5).
[28]薛利红,曹卫星,罗卫红等,光谱植被指数与水稻叶面积指数相关性的研究[J],植物生态学报,2004,28(1).
[29]王福民,黄敬峰,唐延林等,新型植被指数及其在水稻叶面积指数估算上的应用[J],中国水稻科学,2007,21(2).
[30]Holben B N. Characteristic of Maximum Value Composite Images for Temporal AVHRR Data(J). International Journal of Remote Sensing,1986,7(11):1417-1434.
[31]Mingguo Ma, Frank Veroustraete. Reconstructing Pathfinder AVHRR Land NDVI Time-series Data for the Northwest of China [J]. Advances in Space Research,2006, 37:835-840.
[32]甘世书,周科松；’VEGETATION植被指数数据预处理方法[J],中南林业调查规划.第1期,第24卷,2005年2月.
[33]L. L. Stowe, E. P. McClain, R. Carey, P. Pellegrino, G. Gutman, P. Davis,C. Long, and S. Hart, "Global distribution of cloud cover derived from NOAA/AVHRR operational satell-ite data," Adv. Space Res., vol.3, pp.51-54,1991.
[34]G. Gutman and A. Ignatov, "The relative merit of cloud/clear identification in the NOAA/ NASA Pathfinder AVHRR Land 10-day composites," Int. J. Remote Sens, vol.17, pp. 3295-3304,1996.
[35]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[J],地球科学进展,第20卷,第7期,2005年7月.
[36]李培生,胡松,孙路石等；基于SG算法的燃煤微分热重曲线平滑去噪[J],华中科技大学学报,2005年第33卷第07期.
[37].Savitzky A, Golay M J E. Smoothing and Differentiation of Data by Simp-lified Least Squares Procedures [J]. Analytical Chemistry,1964,36:1627-1639.
[38]辜智慧；中国农作物复种指数的遥感估算方法研究——基于SPOT/VGT多时相NDVI遥感数据[D]；北京师范大学；2003年.
[39]顾娟,李新,黄春林；NDVI时间序列数据集重建方法述评[M],遥感技术与应用.2006.
[40]JONSSON, P. and EKLUNDH, L.,2004, TIMESAT-a program for analy-zing time-series of satellite sensor data, Computers and Geosciences,30,833-845.
[41]JONSSON, P., and EKLUNDH, L.,2002, Seasonality extraction by functi-on fitting to time-series of satellite sensor data. IEEE Transactions on Geo-science and Remote Sen-sing,40 (8),1824-1832.
[42]Groten S M E, Ocatre R.Monitoring the length of the growing season with N OAA.Inter-national Journal of Remote Sensing,2002,23 (14):2797-2815
[43]李爽,张二勋；基于决策树的遥感影像分类方法研究[J],地域研究与开发,2003年,第22卷,第1期。
[44]潘琛,杜培军等；决策树分类法及其在遥感图像处理中的应用[J].测绘科学,第33卷,第1期,2008年1月.
[45]John Durkin,蔡竞峰,蔡自兴；决策树技术及其当前研究方向[J],控制工程,2005,(1)：15-18.
[46]陈建军,张树文；基于MODIS数据的东北地区土地覆盖分类的精度评价研究[J],海洋科学进展,2004年
[2]YangBangjie, PeiZhiyuan, ZhouQingbo, etal. Key technologies of crop mo-nitoring using remote sensing gatanational scale; Progress and problems[J].Transactions of the CSAE, 2002,18(3):191-194
[3]周清波,国内外农情遥感现状与发展趋势[J].中国农业资源与区划,第25卷,第5期,2004年10月
[4]范磊,程永政,刘婷等,基于MODIS数据的河南省冬小麦长势监测研究[J],河南农业科学,第8期,2008年
[5]杨小唤、张香平等,基于MODIS时序NDVI特征值提取多作物播种面积的方法,资源科学,2004年06期.
[6]任建强,陈仲新,唐华俊,基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J],应用生态学报,第17卷,第12期,2006年12月.
[7]宫攀,唐华俊,陈仲新等；基于MODIS-LST修正NDVI时序数列的土地覆盖分类[J],资源科学,2006年04期.
[8]徐晓桃,韩涛,颉耀文；基于单时相MODIS数据的土地覆盖三种分类方法对比研究[J],干旱地区农业研究,2008,(03).
[9]刘勇洪,牛铮；基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究[J].遥感技术与应用,2004,(04).
[10]盛昊,李均力,杨辽等；MODIS-NDVI时序数据分析方法研究—以塔里木河下游第七次秋季输水为例[J],干旱区地理,2007,30(2).
[11]荆凤,申旭辉,洪顺英等；遥感技术在地震科学研究中的应用[J]；国土资源遥感；2008年第2期.
[12]刘海启等；美国农业遥感技术应用状况概述[J]；中国农业资源与区划1992,20(2)：56-60.
[13]刘海启等.欧盟MARS计划简介与我国农业遥感应用思路[J]；中国农业资源与区划,1999,20(3)：55-57..
[14]Reed B C, Brown J F, Vander zee D, et al. Measuring phonological varia-bility from satellite imagery. Journal of Vegetation Science,1994,5(5):703-710.
[15]Wu B F, Zhang F, Liu C L, et al. An Integrated Method for Crop Condition Monitoring [J]. Journal of Remote Sensing,2004,8 (6):498-514.
[16]Jonsson P,& Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geo-science and Rem-ote Sensing,2002,40 (8),1824-1832
[17]Young S S and Wang C Y. Land-cover changes analysis of China using glo-bal-scale Pathfinder AVHRR Land cover (PAL) data [J]. Int J Remote Sens-ing,2001,22(2): 1457-1477.
[18]Running S W, Loveland T R, Pierce L L, et al. A remote sensing based vegetation classi-fication logic for global land cover analysis [J]. Remote Sensing of Environment,1995,51 (1):39-48.
[19]吴炳方；中国国情遥感监测研究[J],中国科学院院刊,第3期,第19卷,2004年.
[20]竺可桢,宛敏渭；物候学[M].长沙：湖南教育出版社,1999.
[21]张学霞,葛全胜等；遥感技术在植物物候研究中的应用综述[J].地球科学进展,2003,18(4)：534-544.
[22]王梨村；中国古今物候学[M].成都：四川大学出版社,1990.174-182.
[23]方修琦,余卫红；物候对全球变暖响应的研究综述[J].地球科学进展,2002,17(5)：714-719.
[24]杨小唤,张香平,江东.基于MODIS时序NDVI特征值提取多作物播种面积的方法[J],资源科学,第26卷,第6期,2004年.
[25]张明伟,周清波,陈仲新等；基于MODIS时序数据分析的作物识别方法[J].中国农业资源与区划,第29卷,第1期,2008年2月.
[26]马明国,潘小多,李新等；长时间序列中国植被指数数据集(1998-2008) [EB/OL].兰州：中国科学院寒区旱区环境与工程研究所.2007.
[27]程乾,黄敬峰,王人潮等；水稻叶面积指数与MODIS植被指数、红边位置之间的分析[J],农业工程学报,2005,19(5).
[28]薛利红,曹卫星,罗卫红等,光谱植被指数与水稻叶面积指数相关性的研究[J],植物生态学报,2004,28(1).
[29]王福民,黄敬峰,唐延林等,新型植被指数及其在水稻叶面积指数估算上的应用[J],中国水稻科学,2007,21(2).
[30]Holben B N. Characteristic of Maximum Value Composite Images for Temporal AVHRR Data(J). International Journal of Remote Sensing,1986,7(11):1417-1434.
[31]Mingguo Ma, Frank Veroustraete. Reconstructing Pathfinder AVHRR Land NDVI Time-series Data for the Northwest of China [J]. Advances in Space Research,2006, 37:835-840.
[32]甘世书,周科松；’VEGETATION植被指数数据预处理方法[J],中南林业调查规划.第1期,第24卷,2005年2月.
[33]L. L. Stowe, E. P. McClain, R. Carey, P. Pellegrino, G. Gutman, P. Davis,C. Long, and S. Hart, "Global distribution of cloud cover derived from NOAA/AVHRR operational satell-ite data," Adv. Space Res., vol.3, pp.51-54,1991.
[34]G. Gutman and A. Ignatov, "The relative merit of cloud/clear identification in the NOAA/ NASA Pathfinder AVHRR Land 10-day composites," Int. J. Remote Sens, vol.17, pp. 3295-3304,1996.
[35]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[J],地球科学进展,第20卷,第7期,2005年7月.
[36]李培生,胡松,孙路石等；基于SG算法的燃煤微分热重曲线平滑去噪[J],华中科技大学学报,2005年第33卷第07期.
[37].Savitzky A, Golay M J E. Smoothing and Differentiation of Data by Simp-lified Least Squares Procedures [J]. Analytical Chemistry,1964,36:1627-1639.
[38]辜智慧；中国农作物复种指数的遥感估算方法研究——基于SPOT/VGT多时相NDVI遥感数据[D]；北京师范大学；2003年.
[39]顾娟,李新,黄春林；NDVI时间序列数据集重建方法述评[M],遥感技术与应用.2006.
[40]JONSSON, P. and EKLUNDH, L.,2004, TIMESAT-a program for analy-zing time-series of satellite sensor data, Computers and Geosciences,30,833-845.
[41]JONSSON, P., and EKLUNDH, L.,2002, Seasonality extraction by functi-on fitting to time-series of satellite sensor data. IEEE Transactions on Geo-science and Remote Sen-sing,40 (8),1824-1832.
[42]Groten S M E, Ocatre R.Monitoring the length of the growing season with N OAA.Inter-national Journal of Remote Sensing,2002,23 (14):2797-2815
[43]李爽,张二勋；基于决策树的遥感影像分类方法研究[J],地域研究与开发,2003年,第22卷,第1期。
[44]潘琛,杜培军等；决策树分类法及其在遥感图像处理中的应用[J].测绘科学,第33卷,第1期,2008年1月.
[45]John Durkin,蔡竞峰,蔡自兴；决策树技术及其当前研究方向[J],控制工程,2005,(1)：15-18.
[46]陈建军,张树文；基于MODIS数据的东北地区土地覆盖分类的精度评价研究[J],海洋科学进展,2004年