WorldView-2影像的湿地典型挺水植物群落含水量估算研究——以北京野鸭湖湿地为例

利用多光谱遥感技术定量估算野鸭湖湿地挺水植物的含水量.基于典型挺水植物的实测冠层光谱及其对应样方的叶片含水量和叶面积指数LAI数据, 首先对芦苇和香蒲的地面实测光谱进行重采样, 以模拟WorldView-2影像的光谱, 然后利用模拟光谱分别构建芦苇和香蒲任意两波段反射率组合而成的比值(SR)和归一化差值植被指数(NDVI), 通过分析植被指数与CWC(冠层含水量,Canopy Water Content)的相关关系, 选择与CWC显著相关的植被指数, 并通过单变量线性与非线性拟合的分析方法确定监测不同挺水植物群落的最佳植被指数, 建立估算模型；结合覆盖研究区的WorldView-2高分辨率多光谱影像, 对研究区的挺水植物群落CWC进行反演及制图.结果表明, 基于模拟WorldView-2影像光谱构建的比值(SR)和归一化差值植被指数(NDVI)与CWC的总体相关性较高；SR(8,3)芦苇为估算CWC芦苇的最优植被指数, 估算模型为y=0.005x+0.003, NDVI(8,3)香蒲为估算CWC香蒲的最优植被指数, 估算模型为y=2.461x2-0.313x+0.032, 通过交叉检验, CWC芦苇和CWC香蒲的预测精度分别为87.42%和82.12%, 预测精度较为理想；利用实测数据对反演的CWC空间分布图进行了验证, 通过验证, 芦苇和香蒲影像估算CWC的均方根差(RMSE)分别为0.0048和0.0052, 估算精度分别为83.56%和80.31%, 表明利用WorldView-2高分辨率多光谱影像反演湿地挺水植物群落CWC具有较高的可行性.

Abstract

Quantitative estimation of emerged plant water content with multi-spectral remote sensing technique is of great significance for emerged plant physiological status and growth trend monitoring. The hyperspectral reflectance of canopy of wetland typical emerged plant(reed and cattail)was measured by Field-Spec 3 wild high-spectrum radiometer. The leaf water content and leaf area index(LAI)of corresponding samples were also measured. First of all, the ground spectral data(reed and cattail)were resampled to simulate the spectral of WorldView-2 imagery, then the simple ratio vegetation index(SR)and normalized difference vegetation index(NDVI)were constructed with arbitrary two band combination from the simulated WorldView-2 spectra, respectively. The correlation between canopy water content(CWC )and vegetation index were analyzed. The estimation models were obtained by using regression and correlation analysis for different emerged plant community. In addition, the research result of ground data was applied to WorldView-2 high resolution multispectral imagery covering the study area, and the CWC of emerged plant community was estimation in spatial scale. The results show that the SR and NDVI constructed by the simulated WorldView-2 spectra had a good overall correlation with CWC. The SR(8, 3)reedwas selected as the optimal vegetation index to estimate the CWCreed, the best models are evaluated and validated as y=0.005x+0.003. The NDVI(8, 3)cattailwas selected as the optimal vegetation index to estimate the CWCcattail, the best models were evaluated and validated as y=2.461x2-0.313x+0.032. According to two K-fold cross validation examination, these estimation models have the satisfactory prediction accuracy. The prediction accuracy of CWCreedwas 87.42% and the prediction accuracy of CWCcattailwas 82.12%. Furthermore, based on the research result of ground data, we made use of WorldView-2 high resolution multispectral imagery to map the CWC of different emerged plant community. According to the examination of measured data, the estimation RMSE of CWCreedand CWCcattailfrom imagery were 0.0048 and 0.0052, respectively. The estimation accuracy were 83.56% and 80.31%, respectively. It was demonstrated that using WorldView-2 high resolution multispectral imagery to estimate the CWC of wetland emerged plant community has a high feasibility.

参考文献

[1] YUAN Gui-Xiang, WU Ai-Ping, GE Da-Bing, et al. Effects of water depth on the growth of four emergent macrophytes[J]. Acta Scientiae Cirumstantiae(袁桂香,吴爱平,葛大兵,等.不同水深梯度对4种挺水植物生长繁殖的影响.环境科学学报), 011, 31(12)： 2690-2697.

[2] LI Dong-Lin, WANG Lei, DING Jing-Jing, et al. Ecological Functions and Resource Utilization of Aquatic Plants[J]. Wetland Science(李冬林,王磊,丁晶晶,等.水生植物的生态功能和资源应用.湿地科学), 2011, 9(3)： 290-296.

[3] LIN Chuan, GONG Zhao-Ning, ZHAO Wen-Ji. Hyperspectral estimation models for plant community water content at both leaf and canopy levels in Wild Duck Lake wetland[J]. Acta Ecologica Sinica(林川,宫兆宁,赵文吉.叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型.生态学报),2011, 31(22)： 6645-6658.

[4] Zhang Jia-Hua, Xu Yun, Yao Feng-Mei, et al. Progress of vegetation water content estimation by optical remote sensing[J]. SCIENCE CHINA Technological Sciences(张佳华,许云,姚凤梅,等.植被含水量光学遥感估算方法研究进展.中国科学：技术科学), 2010, 40(10)： 1121-1129.

[5] Li Yu-Xia, Yang Wu-Nina, Tong Ling, et al. Remote sensing quantitative monitoring and analysis of fuel moisture content based on spectral index[J]. Acta Optica Sinica(李玉霞,杨武年,童玲,等.基于光谱指数法的植被含水量遥感定量监测及分析.光学学报),2009, 29(5)： 1403-1407.

[6] Stagakis S, Markos N, Sykioti O, et al. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery： An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations[J]. Remote Sens Environ, 2010, 114： 977-994.

[7] Danson F M, Steven M D, Malthus T J, et al. High spectral resolution data for determining leaf water content[J]. Int J Remote Sens, 1992, 13(3)： 461-470.

[8] Tian Q J, Tong Q X, Pu R L, et al. Spectroscopic determinations of wheat water status using 1650-1850nm spectral absorption features[J]. Int J Remote Sens, 2001, 22： 2329-2338.

[9] Danson F M, Bowyer P. Estimating live fuel moisture content from remotely sensed reflectance[J]. Remote Sens Environ, 2004, 92： 309-321.

[10] Chuvieco E, Riao D, Aguado I, et al. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data： applications in fire danger assessment[J]. Int J Remote Sens, 2002, 23(11)： 2145-2162.

[11] Fensholt R, Sandholt I. Derivation of a shortwave infrared water stress index from MODIS near and shortwave infrared data in a semiarid environment[J]. Remote Sens Environ, 2003, 87： 111-121.

[12] Peuelas J, Piol J, Ogaya R, et al. Estimation of plant water concentration by the reflectance water index WI(R900/R970)[J]. Int J Remote Sens, 1997, 18： 2869-2875.

[13] Hardy C C, Burgan R E. Evaluation of NDVI for monitoring live moisture in three vegetation types of the Western U.S[J]. Photo Eng Remote Sens, 1999, 65： 603-610.

[14] Zarco-Tejada P J, Rueda C A, Ustin S L. Water content estimation in vegetation with MODIS reflectance data and model inversion methods[J]. Remote Sens Environ, 2003, 85： 109-124.

[15] Chen D, Huang J F, Jackson T J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands[J]. Remote Sens Environ, 2005, 98： 222-236.

[16] Tucker C J. Remote sensing of leaf water content in the near infrared[J]. Remote Sens Environ, 1980, 10： 23-32.

[17] Ceccato P, Flasse S, Tarantola S, et al. Detecting vegetation leaf water content using reflectance in the optical domain[J]. Remote Sens Environ, 2001, 77： 22-33.

[18] Ceccato P, Gobron N, Flasse S, et al. Designing a spectral index to estimate vegetation water content from remote sensing data： Part 1 Theoretical approach[J]. Remote Sens Environ, 2002, 82： 188-197.

[19] Zhang J H, Guo W J. Quantitative retrieval of crop water content under different soil moistures levels[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2006.

[20] Penuelas J, Pinol J, Ogaya R, et al. Estimation of plant water concentration by the reflectance water index WI(R900/R970)[J]. Int J Remote Sens, 1997, 18(13)： 2869-2875.

[21] SHEN Yan, NIU Zheng, WANG Wen, et al. Establishment of Leaf Content Models Based on Derivative Spectrum Variables[J]. Geography and Geo-Information Science(沈艳,牛铮,王汶,等.基于导数光谱变量叶片含水量模型的建立.地理与地理信息科学),2005, 21(4)： 16-19.

[22] YANG Yong, ZHANG Dong-Qiang, LI Shuo, et al. Model for Citrus Leaves Water Content Based on Spectral Signature[J]. Chinese Agricultural Science Bulletin(杨勇,张冬强,李硕,等.基于光谱反射特征的柑橘叶片含水率模型.中国农学通报),2011, 27(02)： 180-184.

[23] SUN Jun, MAO Han-Ping, YANG Yi-Qing, et al. Model of moisture content of paddy rice leaf based on canopy spectral reflectance[J]. Transactions of the CSAE(孙俊,毛罕平,羊一清,等.基于冠层光谱特性的水稻叶片含水率模型.农业工程学报),2009, 25(9)： 133-136.

[24] Colombo R, Meroni M, Marchesi A, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J]. Remote Sens Environ, 2008, 112： 1820-1834.

[25] Gao B C, Goetz A F H. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data[J]. Remote Sens Environ, 1995, 52： 155-162.

[26] Clevers J G P W, Kooistra L, Schaepman M E. Estimating canopy water content using hyperspectral remote sensing data[J]. Int J Appl Earth Obs, 2010, 12： 119-125.

[27] Cheng Y B, Ustin, S L, Riao D, et al. Vanderbilt. Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona[J]. Remote Sens Environ, 2008, 112： 363-374.

[28] CHEN Jun-Ying, TIAN Qing-Jiu, QI Xue-Yong, et al. Rice canopy biochemical concentration retrievals based on Hyperion data[J]. Journal of Remote Sensing(陈君颖, 田庆久,亓雪勇,等.基于Hyperion影像的水稻冠层生化参量反演.遥感学报),2009, 13(6)： 1106-1121.

[29] CHEN Wei, HU Dong, FU Bi-Qian, et al. Studies on Biological Diversity of Wetland in Beijing[M]. Beijing： Science Press(陈卫,胡东,付必谦,等.北京湿地生物多样性研究.北京：科学出版社),2007： 46-58.

[30] GONG Zhao-Ning, ZHAO Wen-Ji, HU Dong. Wetland plant community characteristics and ecological succession mode in Wild Duck Lake along salt and water environment gradient[J]. Progress in Natural Science(宫兆宁,赵文吉,胡东.水盐环境梯度下野鸭湖湿地植物群落特征及其生态演替模式.自然科学进展),2009, 19(11)： 1272-1280.

[31] GONG Zhao-Ning, GONG Hui-Li, HU Dong. Wetland plants in Wild Duke Lake, Beijing[M]. Beijing： China Environmental Science Press(宫兆宁, 宫辉力, 胡东.北京野鸭湖湿地植物.北京：中国环境科学出版社),2012： 20-224.

[32] Yu B, Ostland I M, Gong P, et al. Penalized discriminant analysis of In situ hyperspectral data for conifer species recognition[J]. IEEE T Geosci Remote, 1999, 5(37)： 2569-2576.

[33] Pu R L. Broadleaf species recognition with in situ hyperspectral data[J]. Int J Remote Sens, 2009, 30(11)： 2759-2779.

[34] LIANG Liang, YANG Min-Hua, ZANG Zhuo. Determination of wheat canopy nitrogen content ratio by hyperspectral technology based on wavelet denoising and support vector regression[J]. Transactions of the CSAE(梁亮,杨敏华,臧卓.基于小波去噪与SVR的小麦冠层含氮率高光谱测定.农业工程学报),2010, 26(12)： 248-253.

[35] GENG Xiang, CHEN Bin, YE Jing, et al. Simplification of model for tea caffeine detection by near infrared spectroscopy[J]. Transactions of the CSAE(耿响, 陈斌, 叶静, 等．茶叶咖啡碱近红外光谱模型简化方法．农业工程学报),2009, 25(10)： 345-349.

[36] WANG Qian, CHEN Jing-Ling, SUN Zhi-Qiang. The Utility of LAI-2000 Canopy Analyzer Studying the Sunlight Distribution Characteristics in Different Plant Colonies[J]. Scientia Agricultura Sinica(王谦,陈景玲,孙治强.LAI-2000冠层分析仪在不同植物群体光分布特征研究中的应用.中国农业科学),2006, 39(5)： 922-927.

[37] WU Wei-Bin, HONG Tian-Sheng, WANG Xi-Ping, et al. Advance in Ground-based LAI Measurement Methods[J]. Journal of Huazhong Agricultural University(吴伟斌,洪添胜,王锡平,等．叶面积指数地面测量方法的研究进展．华中农业大学学报),2007, 26(2)： 270-275.

[38] LIU Yi-Bo, JU Wei-Min, ZHU Gao-Long, et al. Retrieval of leaf area index for different grasslands in Inner Mongolia prairie using remote sensing data[J]. Acta Ecologica Sinica(柳艺博,居为民,朱高龙,等.内蒙古不同类型草地叶面积指数遥感估算.生态学报),2011, 31(18)： 5159-5170.

[39] ZHANG Ji-Xiang, WEI Qin-Ping, ZHANG Jing, et al. Leaf Area Index Estimated with Plant Canopy Analyzer in Apple Orchards and Analysis of Its Reliability[J]. Acta Horticulturae Sinica(张继祥,魏钦平,张静,等.利用冠层分析仪测算苹果园叶面积指数及其可靠性分析.园艺学报),2010, 37(2)： 185-192.

[40] LING Chun-Li, ZHU Lan-Yan, WU Li-Min. The research and realization of the forest information extraction based on the WorldView-2 image[J]. Science of Surveying and Mapping(凌春丽,朱兰艳,吴俐民.WorldView-2影像林地信息提取的研究与实现.测绘科学),2010, 35(5)： 205-207.

[41] YANG Jie, TIAN Yong-Chao, YAO Xia, et al. Hyperspectral estimation model for chlorophyll concentrations in top leaves of rice[J]. Acta Ecologica Sinica(杨杰,田永超,姚霞,等.水稻上部叶片叶绿素含量的高光谱估算模型.生态学报),2009, 29(12)： 6561-6571.

[42] Zengeya F M, Mutanga O, Murwira A. Linking remotely sensed forage quality estimates from WorldView-2 multispectral data with cattle distribution in a savanna landscape[J]. Int J Appl Earth Obs, 2013,21： 513-524.

[43] ZHANG Xiu-Ying, FENG Xue-Zhi, JIANG Hong. Feature space optimization of object-oriented classification[J]. Journal of Remote Sensing(张秀英,冯学智,江洪.面向对象分类的特征空间优化.遥感学报),2009, 13 (4)： 664-669.

[44] TAO Chao, TAN Yi-Hua, CAI Hua-Jie, et al. Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery[J]. Acta Geodaetica et Cartographica Sinica(陶超,谭毅华,蔡华杰,杜博,田金文.面向对象的高分辨率遥感影像城区建筑物分级提取方法.测绘学报),2010, 39(1)： 39-45.

[45] SU Wei, LI Jing, CHEN Yun-Hao, et al. Object-oriented urban land-cover classification of multi-scale image segmentation method—a case study in Kuala Lumpur city center, Malaysia[J]. Journal of Remote Sensing(苏伟,李京,陈云浩,等.基于多尺度影像分割的面向对象城市土地覆被分类研究—以马来西亚吉隆坡市城市中心区为例.遥感学报),2007, 11(4)： 521-530.

[46] NIE Yong, ZHANG Yi-Li, LIU Lin-Shan, et al. Monitoring glacier change based on remote sensing in the Mt.Qomolangma national nature preserve, 1976-2006[J]. Acta Geographica Sinica(聂勇,张镱锂,刘林山,等.近30年珠穆朗玛峰国家自然保护区冰川变化的遥感监测.地理学报),2010, 65(1)： 13-28.

[47] DU Feng-Lan, TIAN Qing-Jiu, XIA Xue-Qi, et al. Object-oriented image classification analysis and evaluation[J]. Remote Sensing Technology and Application(杜凤兰,田庆久,夏学齐,等.面向对象的地物分类法分析与评价.遥感技术与应用),2004, 19(1)： 20-23.

[48] CHEN Yun-Hao, FENG Tong, SHI Pei-Jun, et al. Classification of remote sensing image based on object-oriented and class rules[J]. Geomatics and Information Science of Wuhan University(陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究.武汉大学学报：信息科学版),2006, 31(4)： 316-320.

宫兆宁, 林川, 赵文吉, 崔天翔. WorldView-2影像的湿地典型挺水植物群落含水量估算研究——以北京野鸭湖湿地为例[J]. 红外与毫米波学报, 2014, 33(5): 533. GONG Zhao-Ning, LIN Chuan, ZHAO Wen-Ji, CUI Tian-Xiang. Canopy water content estimation for typical emerged plant community using WorldView-2 imagery： A case study in Wild Duck Lake wetland, Beijing[J]. Journal of Infrared and Millimeter Waves, 2014, 33(5): 533.

WorldView-2影像的湿地典型挺水植物群落含水量估算研究——以北京野鸭湖湿地为例

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