收稿日期: 2009-04-21
修回日期: 2009-06-25
网络出版日期: 2010-03-10
基金资助
国家自然科学基金项目“陆面数据同化中的贝叶斯滤波方法研究”(编号:40771036);国家自然科学基金面上项目“陆地碳循环遥感与模型模拟的融合方法研究”（编号：40871190）;国家自然科学基金青年基金项目“基于数据同化方法的蒸散发遥感估算及时间尺度扩展研究”(编号:40801126)资助.
Land Cover Classification Based on Time-series MODIS NDVI Data in Heihe River Basin
Received date: 2009-04-21
Revised date: 2009-06-25
Online published: 2010-03-10
归一化植被指数（NDVI）是植被生长状态及植被覆盖度的最佳指示因子，其时序数据也已成为基于生物气候特征开展大区域植被和土地覆盖分类的基本手段。基于时序NDVI数据的土地覆盖分类，即通过提取NDVI时间信号所包含的植被生物学参数，构建起一个包含植被生物学信息的分类特征空间。利用2006年重建得到的MODIS NDVI 16天合成时间序列数据，并结合1 km分辨率的DEM数据、野外实地调查资料等辅助数据，综合分析了不同土地覆盖类型对应的时序NDVI谱线及其第一、二谐波的特征阈值，建立决策树对黑河流域的土地覆盖开展分类研究。结果表明，基于时序MODIS NDVI谱线特征的决策树分类精度为78%，Kappa系数为0.74。利用1 km时序MODIS NDVI时间序列获得较为准确的黑河流域土地覆盖类型是可行的。
关键词： MODIS NDVI; 时间序列; 土地覆盖; 分类; 黑河流域
顾娟 . 基于时序MODIS NDVI的黑河流域土地覆盖分类研究[J]. 地球科学进展, 2010 , 25(3) : 317 -326 . DOI: 10.11867/j.issn.1001-8166.2010.03.0317
Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI datasets in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. The Timeseries Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets hold considerable promise for large-area land cover classification given their global coverage, intermediate spatial resolution, high temporal resolution (16=day composite period), and cost-free status. This study focused on generating effective classification features from multi-temporal MODIS NDVI datasets to improve classification accuracy in the Heihe River Basin. Two types of features were derived from reconstructed multi-temporal MODIS NDVI datasets. The first are the basic parameters including the annual maximum NDVI, the mean NDVI during the growing season, the inter-annual variability of NDVI and the annual mean NDVI. The second are the amplitude and phase information of the first and second harmonic components derived from the shape of the time-series NDVI profile. Additionally, DEM with 1km resolution has also been used to simplify the current scheme. According to the validated results with 469 ground truth survey samples, the overall land cover classification accuracy using the decision tree was 78% and a Kappa coefficient is 0.74. The results support using decision tree classification based on 1km MODIS NDVI temporal and derived parameters to provide an up-to-date land cover mapping. However, the current decision tree does not work well in the downstream of the Heihe River Basin since the NDVI of non-vegetation types can not represent the temporal feature of these types. Thus, new effort is necessary in the future in order to improve the overall performance on this issue.
Key words： MODIS NDVI; Time series; Land cover Classification; Heihe
[1] Myeong S, Nowak D J, Duggin M J. A temporal analysis of urban forest carbon storage using remote sensing[J].Remote Sensing of Environment,2006, 101:277-282.
[2] Rasmussen M S. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR[J].International Journal of Remote Sensing,1992, 13：3 431-3 442.
[3] Moulin S, Kergoat L, Viovy N. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements[J].Journal of Climate,1997,10:1 154-1 170.
[4] De Beurs K M, Henebry G M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan[J].Remote Sensing of Environment,2004, 89(4):497-509.
[5] Anyamba A, Tucker C J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981-2003[J].Journal of Arid Environments,2005, 63: 596-614.
[6] James M E, Kalluri S N V. Pathfinder AVHRR land data set-an improved coarse resolution data set for terrestrial monitoring[J].International Journal of Remote Sensing,1994, 15(17):3 347-3 363.
[7] Loveland T R, Reed B C, Brown J F, et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data[J].International Journal of Remote Sensing,2000, 21(6/7):1 303-1 330.
[8] Telesca L, Lasaponara R. Quantifying intra-annual persistent behaviour in SPOT-VEGETATION NDVI data for Mediterranean ecosystems of southern Italy[J].Remote Sensing of Environment,2006, 101:95-103.
[9] Ross S L, Joseph F K, Jayantha E, et al. Land-cover change detection using multi-temporal MODIS NDVI data[J].Remote Sensing Environment,2006, 105:142-154.
[10] Lin Zhonghui, Mo Xingguo. Phenologies from harmonics analysis of AVHRR NDVI time series[J].Transactions of the Chinese Society of Garicultural Enginnering,2006, 22(12):138-144.[林忠辉,莫兴国. NDVI时间序列谐波分析与地表物候信息获取[J].农业工程学报, 2006, 22 (12):138-144.]
[11] Wardlow B D, Egbert S L, Kastens J H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plain[J].Remote Sensing of Environment,2007, 108:290-310.
[12] Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains[J].Remote Sensing of Environment,2008, 112: 1096-1 116.
[13] Defries R S, Townshend J R G. NDVI-derived land-cover classifications at a global-scale[J].International Journal of Remote Sensing,1994, 15(17):3 567-3 586.
[14] Song Yang, Wan Youchuan, Shen Shaohong, et al. Application of hybrid classification method based on fourier transform to time-series NDVI images[J].Geomatics and Eomatics and Information Science of Wuhan University,2007,32(5): 406-409.[宋杨, 万幼川,申绍洪,等.基于傅立叶变换的混合分类模型用于NDVI时序影像分析[J].武汉大学学报, 2007,32(5): 406-409.]
[15] Zheng Yukun, Zhuang Dafang. Fourier analysis of multi-temporal AVHRR data[J].Journal of the Graduate School of the Chinese Academy of Sciences,2003,20(1):62-68.[郑玉坤, 庄大方.多时相AVHRR数据的傅立叶分析[J].中国科学院研究生院学报, 2003,20(1): 62-68].
[16] Liu Aixia, Wang Jing, Lü Chunyan. Land coverclassification based on MODIS Data in area to the North-west of Beijing[J].Progress in Geography,2006, 25(2): 96-102.[刘爱霞, 王静, 吕春艳. 基于MODIS数据的北京西北部地区土地覆盖分类研究[J]. 地理科学进展, 2006, 25(2): 96-102.]
[17] Running S W, Loveland T R, Pierce L L, et al. A remote sensing based vegetation classification logic for global land cover analysis[J].Remote Sensing of Environmenmt,1995, 51:39-48
[18] Shi Qingdong,Lü Guanghui,Pan Xiaoling,et al.Vegetation classification method of divided area and DEM at north Xinjiang[J].Arid Land Geography,2003, 26(3):264-268.[师庆东,吕光辉,潘晓玲,等.遥感影像中分区分类法及在新疆北部植被分类中的应用[J].干旱区地理,2003,26(3):264-268.]
[19] Wang Changyao, Luo Chenfeng, Qi Shuhua,et al. A method of land cover classification for China based on NDVI-Ts space[J].Journal of Remote Sensing,2005, 9(1):93-99.[王长耀, 骆成凤, 齐述华,等.NDVI-Ts空间全国土地覆盖分类方法研究[J]. 遥感学报, 2005,9(1):93-99.]
[20] Gong Pan, Chen Zhongxin, Tang Huajun, Land cover classification based on MODIS temperature-vegetation index time-series data in Northeastern China[J].Transactions of the Chinese Society of Garicultural Enginnering, 2006, 22(9): 94-99.
[21] Yan Hao, Wang Changyao, Niu Zheng. Bio-implication of principal component analysis to land cover using multitemporal AVHRR Data[J].Remote Sensing Technology and Application,2001, 16(4): 209-213.[延昊, 王长耀, 牛铮,等. 多时相NOAA-AVHRR数据主成分分析的生物学意义[J].遥感技术与应用, 2001,16(4): 209-213.]
[22] Yan Hao，Wang Chaoyao, Niu Zheng, et al. Application of principal component analysis by using different vegetation index derived from multitemporal AVHRR data[J].Journal of Remote Sensing,2002, 6(1):30-34.[延昊，王长耀，牛铮,等.遥感植被指数对多时相数据主成分分析的影响[J].遥感学报, 2002, 6(1):30-34.]
[23] Lu Ling, Li Xin, Dong Qinghan, et al. The mapping and validation of land cover in northwest China from SPOT4-VEGETATION[J].Journal of Remote Sensing,2003, 7(3): 214-220.[卢玲, 李新, 董庆罕,等. SPOT4-VEGETATION中国西北地区土地覆盖制图与验证[J]. 遥感学报, 2003, 7(3): 214-220].
[24] Lasaponara R.On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series[J]. Ecological Modeling,2006, 194:429-434.
[25] Moody A , Johnson D M. Land-surface phonologies from AVHRR using the discrete fourier transform[J].Remote Sensing of Environment,2001, 75: 305-323.
[26] Zhang X Y, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using MODIS[J].Remote Sensing of Environment,2003, 84:471-475.
[27] Sakamoto T, Yokozawa M, Toritani H, et al. A crop phenology detection method using time-series MODIS data[J].Remote Sensing of Environment,2005, 96:366-374.
[28] Huete A R, Justice C, Leeuwen V.MODIS vegetation index algorithm theoretical basis document, Version 3[EB/OL]. http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html,1999.
[29] Beck P S A, Atzberger C, Hogda K A, et al. Improved monitoring of vegetation dynamics at very high latitudes: A new method using modis NDVI[J].Remote Sensing of Environment,2006, 100:321-334.
[30] Wang Zhengxing, Liu Chuang, Huete A. From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research[J].Acta Ecologica Sinica,2003, 23(5): 979-987.[王正兴, 刘闯, Huete A.植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI[J]. 生态学报, 2003, 23(5): 979-987.]
[31] Gu J, Li X, Huang C L, et al. A simplified data assimalation method for reconstructing time-series MODIS NDVI data[J].Advances in Space Research,2009,44(4):501-509.
[32] Evans J P,Geerken R. Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure[J].Remote Sensing of Environment,2006, 105(1):1-8.
[33] Wei Yuchu, Chen Suozhong.Theory and Methods of Geography modeling[M].Beijing:Science Press,2005:201-261.[韦玉春, 陈锁忠.地理建模原理与方法[M].北京:科学出版社,2005:201-261.]
[34] Wu Zhengyi.China Vegetation[M].Beijing:Science Press,1980:1-50.[吴征镒.中国植被[M].北京:科学出版社,1980:1-50.]
/
| 〈 |
|
〉 |
甘公网安备62010202000687
