多角度高光谱CHRIS数据森林叶面积指数反演研究
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摘要
森林是重要的再生资源,作为陆地生态系统的主体,森林是陆地上面积最大、分布最广、组成结构最复杂、物质资源最丰富的生态系统,也是自然界功能最完善的资源库,生物基因库及能源储存调节库。对改善生态环境,维护生态平衡具有不可替代的作用。
叶面积指数(leaf area index, LAI)又称为叶面积系数,是一块地上植被叶片的单面总面积与占地面积的比值。叶面积指数是植物生态研究中一个十分重要的植被结构参数,是表达植被冠层结构的最基本参数之一,其已成为一个重要的森林定量评价指标。本文利用多角度高光谱CHIRS数据对森林叶面积指数进行反演研究；通过对CHRIS传感器高光谱、多角度特点的分析,估算叶面积指数。主要研究内容与结论如下：
(1)对CHRIS数据进行深入研究,找到适合CHRIS数据的预处理方法。通过正射纠正,对CHRIS数据5个角度图像进行配准。
(2)通过归一化植被指数(normalized difference vegetation index, NDVI)、比值植被指数(ratio vegetation index, RVI)、修正简单植被指数(modified simple ratio index, MSR)对针叶林、针阔混交林进行LAI反演研究。在相同条件下,针叶林、针阔混交林的RVI与LAI的相关性最好。
(3)对CHRIS数据不同波段组合的NDVI、RVI、MSR与LAI的相关性进行分析,针叶林与针阔混交林对不同的波段的敏感性不同。针叶林三种植被指数与LAI相关性最好的为红光10波段(中心波长：709nm),最大R2为0.7098；针阔混交林三种植被指数与LAI相关性最好的为红光8波段(中心波长：697nm),最大R2为0.6385。
(4)对CHRIS数据不同角度图像间NDVI、RVI、MSR与LAI相关性进行分析,针叶林、针阔混交林0°图像植被指数与LAI的相关性最好。0°图像针叶林样地植被指数与LAI相关性最好,最大R2为0.7098,-36°图像次之,+36°图像植被指数与LAI没有明显的相关性。针阔混交林0°图像与LAI的最大R2为0.6385,-36°、+36°图像植被指数与LAI的相关性分别次之。
Forests are important regeneration resources. As the principal part of terrestrial ecosystems, forests are the largest, the most widely distributed, the most complex structure and the most abundant resources of ecosystem; also it is the most comprehensive resource library in nature, biological gene bank and energy storage regulation library. It has an irreplaceable role on improving environment and maintaining ecological balance.
Leaf area index (LAI) is the ratio between a total area of standing vegetation and single leaf area. Leaf area index is a significant vegetation structural parameter in the study of plant ecological and one of the most basic parameter of vegetation canopy structure, it also has become an important forest quantitative evaluation standard. In this paper, the multi-angle hyperspectral CHIRS data is used to inverse leaf area index. Through the analysis of hyperspectral and multi-angle sensor CHRIS, estimate leaf area index. Main content and conclusion include the followings:
(1) The preprocessing method for CHRIS data can be found through analysing the CHRIS data. By ortho correction, five CHRIS images have been matched.
(2) Use normalizing difference vegetation index (NDVI), simple vegetation index (RVI), modified simple ratio index (MSR) to inverse the conifer forest and the mixed coniferous broad leaved forest's LAI. It showed that RVI correlated best with LAI under the same conditions.
(3) Analyzing the relevance of LAI with NDVI, RVI, MSR that were in different bands of the CHRIS data, and found that the coniferous forest and the mixed coniferous broad leaved forest had different sensitivity to different band. The maximum correlation of LAI with NDVI, RVI, MSR of Coniferous forest happened in the red band 10 (middle wavelength:709nm), the R2 was 0.7098; meanwhile, that of mixed coniferous broad leaved forest happened in the red band 8 (middle wavelength:697nm), the maximum R2 was 0.6385.
(4) The correlation of LAI with NDVI, RVI, MSR from CHRIS image data with different angles has been studied, it revealed that 0°image of NDVI and LAI had the best correlation.0° image of NDVI of coniferous forest had the best correlation with LAI, the maximum R2 can reach 0.7098;-36°image took the second place, but the correlation in +36°image was not good. At the same time, the maximum R2 of 0°image of NDVI with LAI of mixed coniferous broad leaved forest was 0.6385, that of -36°,+36°image took the second place, respectively.
叶面积指数(leaf area index, LAI)又称为叶面积系数,是一块地上植被叶片的单面总面积与占地面积的比值。叶面积指数是植物生态研究中一个十分重要的植被结构参数,是表达植被冠层结构的最基本参数之一,其已成为一个重要的森林定量评价指标。本文利用多角度高光谱CHIRS数据对森林叶面积指数进行反演研究；通过对CHRIS传感器高光谱、多角度特点的分析,估算叶面积指数。主要研究内容与结论如下：
(1)对CHRIS数据进行深入研究,找到适合CHRIS数据的预处理方法。通过正射纠正,对CHRIS数据5个角度图像进行配准。
(2)通过归一化植被指数(normalized difference vegetation index, NDVI)、比值植被指数(ratio vegetation index, RVI)、修正简单植被指数(modified simple ratio index, MSR)对针叶林、针阔混交林进行LAI反演研究。在相同条件下,针叶林、针阔混交林的RVI与LAI的相关性最好。
(3)对CHRIS数据不同波段组合的NDVI、RVI、MSR与LAI的相关性进行分析,针叶林与针阔混交林对不同的波段的敏感性不同。针叶林三种植被指数与LAI相关性最好的为红光10波段(中心波长：709nm),最大R2为0.7098；针阔混交林三种植被指数与LAI相关性最好的为红光8波段(中心波长：697nm),最大R2为0.6385。
(4)对CHRIS数据不同角度图像间NDVI、RVI、MSR与LAI相关性进行分析,针叶林、针阔混交林0°图像植被指数与LAI的相关性最好。0°图像针叶林样地植被指数与LAI相关性最好,最大R2为0.7098,-36°图像次之,+36°图像植被指数与LAI没有明显的相关性。针阔混交林0°图像与LAI的最大R2为0.6385,-36°、+36°图像植被指数与LAI的相关性分别次之。
Forests are important regeneration resources. As the principal part of terrestrial ecosystems, forests are the largest, the most widely distributed, the most complex structure and the most abundant resources of ecosystem; also it is the most comprehensive resource library in nature, biological gene bank and energy storage regulation library. It has an irreplaceable role on improving environment and maintaining ecological balance.
Leaf area index (LAI) is the ratio between a total area of standing vegetation and single leaf area. Leaf area index is a significant vegetation structural parameter in the study of plant ecological and one of the most basic parameter of vegetation canopy structure, it also has become an important forest quantitative evaluation standard. In this paper, the multi-angle hyperspectral CHIRS data is used to inverse leaf area index. Through the analysis of hyperspectral and multi-angle sensor CHRIS, estimate leaf area index. Main content and conclusion include the followings:
(1) The preprocessing method for CHRIS data can be found through analysing the CHRIS data. By ortho correction, five CHRIS images have been matched.
(2) Use normalizing difference vegetation index (NDVI), simple vegetation index (RVI), modified simple ratio index (MSR) to inverse the conifer forest and the mixed coniferous broad leaved forest's LAI. It showed that RVI correlated best with LAI under the same conditions.
(3) Analyzing the relevance of LAI with NDVI, RVI, MSR that were in different bands of the CHRIS data, and found that the coniferous forest and the mixed coniferous broad leaved forest had different sensitivity to different band. The maximum correlation of LAI with NDVI, RVI, MSR of Coniferous forest happened in the red band 10 (middle wavelength:709nm), the R2 was 0.7098; meanwhile, that of mixed coniferous broad leaved forest happened in the red band 8 (middle wavelength:697nm), the maximum R2 was 0.6385.
(4) The correlation of LAI with NDVI, RVI, MSR from CHRIS image data with different angles has been studied, it revealed that 0°image of NDVI and LAI had the best correlation.0° image of NDVI of coniferous forest had the best correlation with LAI, the maximum R2 can reach 0.7098;-36°image took the second place, but the correlation in +36°image was not good. At the same time, the maximum R2 of 0°image of NDVI with LAI of mixed coniferous broad leaved forest was 0.6385, that of -36°,+36°image took the second place, respectively.
引文
[1]Barnsley, M.J., et al.,1994. Sampling the surface bidirectional reflectance distribution function (BRDF): Evaluation of current and future satellite sensors. Remote Sensing Reviews,8:271～311.
[2]Barducci A., Guzzi D., Marcoionni P., Pippi I.,2006. CHRIS-PROBA Performance Evaluation, Signal-to-Noise Ratio, Instrument Efficiency and Data Quality from Acquisitions over San Rossore Test site[C]. Proc. Of 3rd. ESA CHRIS/PROBA workshop, Frascati,21～23 March.
[3]Bothwell G.W., Hansen E.G., Vargo R.E. and Miller K.C.,2002. The multi-angle imaging spectroradiometer science. Data system, Its products, tools and performance[J], IEEE Transactions Geoscience Remote Sensing,40(7):1467～1476.
[4]Chen J.M.,1996a. Evalualtion of vegetation indices and a modified simple ratio for boreal applications, Canadian Journal of Remote Sensing,22(3):229～242.
[5]Chen J.M.,1996b. Optically-based Methods for Measuring Seasonal Variation of Leaf Area Index in Boreal Conifer stands, Agric. For. Meteorology,80:138163.
[6]Chen J.M., Black T.A.,1992. Defining leaf area index for non-fiat leaves, Plant Cell and Environment, 15:421～429.
[7]Chen J.M., Cihlar J.,1996. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM images, Remote Sensing of Environment,55:153～162.
[8]Chen J.M., Menges C.H. and Leblanc S.G.,2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment,97(4):447～457.
[9]Chen J.M., Pavlic G., Brown L., et al.,2001. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J], Remote Sens, Environ.,80:165～184.
[10]Chopping, M., et al.,2007. Large area mapping of southwestern forest crown cover, canopy height, and biomass using MISR, Remote Sens. Environ. Special Issue on Biodiversity and Ecology.
[11]Chopping M.J., et al.,2006. Mapping woody plant cover in desert grasslands using canopy reflectance modeling and MISR data. Geophys. Res. Lett,33.
[12]Chopping M., et al.,2006. Mapping shrub abundance in desert grasslands using geometric-optical modeling and multi-angle remote sensing with CHRIS/Proba. Remote Sensing of Environment,104(1): 62～73.
[13]David G.G., Dyk A., Tian Han, Hao Chen, Gates T., Niemann K.O.,2005. Multi-Temporal Evaluation with CHRIS of Coastal Forests[C]. Geoscience and Remote Sensing Symposium,2005. IGARSS '05. Proceedings.2005 IEEE International,5(7):3557～3560.
[14]Dawson, T.E.,1998. Fog in the California redwood forest:ecosystem inputs and use by plants. Oecologia,117(4):476～485.
[15]Dekker A.G., Vos R.J., Peters S.W.M.,2002. Analytical Algorithms for Lake Water TSM Estimation for Retrospective Analyses of TM and SPOT Sensor Data[J], International Journal of Remote Sensing, 23(1):15～35.
[16]Deuze J.L., Goloub P., Herman M., et al,2000. Estimate of the Aerosol Properties over the Ocean With POLDER[J],105(12):15329～15346.
[17]Deuze J.L., Herman M., Goloub P., et al.,1999. Characteristic of Aerosols over Ocean from POLDER/ADEOS-1[J], Geophysical Research Letters,26(10):1421～1424.
[18]Diner, D.J., et al.,1999. New Directions in Earth Observing:Scientific Applications of Multiangle Remote Sensing. Bulletin of the American Meteorological Society,80(11):2209～2228.
[19]Friedl, M.,2002. Global land cover mapping from MODIS algorithms and early results. Remote Sensing of Environment,83(1):287～302.
[20]Garcia J.C, Moreno J.,2004. Removol of noises in CHRIS/PROBA images:application to the SPARC campaign data[C], Proc. Of the 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[21]Goel, N.S.,1988. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews,4(1):1～222..
[22]Goloub P., Tanre D., Deuze J.L., et al.,1999. Validation of the First Algorithm Applied for Deriving the Aerosol Properties over the Ocean Using the POLDER/ADEOS Measurements[J]. IEEE Transactions on Geoscience and Remote Sensing,37(3):1586～1596.
[23]Grover K.D., Steven M.D., Rondeaux G., et al.,2000. Estimating albedo from limited spectral and angular data[J]. INT. J. Remote Sensing,21(1):155～165.
[24]Hagberg, J.O.,,L.M.H.Ulander, and J.Askne,1995. Repeat-pass SAR interferometry over forested terrain.Geoscience and Remote Sensing,IEEE Transactions on,33(2):331～340.
[25]Hall, F.G., J.R.,1995. Townshend, and E.T. Engman, Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment,51(1):138～156.
[26]Heiskanen,J.,2006. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data.Remote Sensing of Environment,103(1):97～114.
[27]Imhoff M.L., et al.,1995. A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass. Geoscience and Remote Sensing,IEEE Transactions on,33(2):341～352.
[28]Kayitakire F, Defourny P.,2004. Forest Type Discrimination Using Multi-angle Hyperspectral Data[C], Proc. Of the 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[29]Kimes, D.S., et al.,2006. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment,100(4):503～511.
[30]Kneubuhler M, Koetz B, Richter R, Michael S, Itten K.,2006. Geometric and radiometric pre-processing of CHRIS/PROBA data over mountainous terrain[C], Proc. Of 3rd CHRIS/Proba Workshop, Frascati,21～23 March.
[31]Le Toan, T., et al.,1992. Relating forest biomass to SAR data.Geoscience and Remote Sensing,IEEE Transactions on,30(2):403～411.
[32]Li X.W., Wang J.D., Xiang Y.Q., et. al.,1997. Measuring Leaf Angle Distribution and Leaf Area Index of Conifer Canopies Simultaneously on Wide-angle Image[J]. Journal of Remote Sensing,1:54～61.
[33]Mannheim S., Heim B., Segl K., Kaufmann H.,2004. Monitoring of Lake Water Quality Using Hyperspectral CHRIS-PROBA[C]. Proc. Of the 2nd CHRIS/Proba workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[34]Price, J.C.,1993. Estimating leaf area index from satellite data, IEEE Trans. Geosci Remote Sensing, 31:727～734.
[35]Robert. A. Scbowengerdt,1997. Remote Sensing Modals and Methods for Image Processing[M]. ACADEMIC press.
[36]Liang S.L., Alan H., Strahler, Charles, Walthall,1999. Retrieval of Land Surface Albedo from Satellite Observations:A Simulation Study[J]. Journal of Applied Metrorology,38:712～725.
[37]Voes J.M., Ullivan N.H., Clinton B.D., et.al.,1995. Vertical leaf area distribution, light transmittance, and application of the Beer-Lambert law in four mature hardwood stands in the southern Appalachian[J]. Canadian Journal of Forest Resource,25:1036～1043.
[38]Zhang,Y.,et al.,2002. Assessing the information content of multiangle satellite data for mapping biomes.I.Statistical analysis.Remote Sensing of Environment,80(3):418～434.
[39]Zhang,Y., et al.,2002.,Assessing the information content of multiangle satellite data for mapping biomes II.Theory.Remote Sensing of Environment,80:435～446.
[40]Zhao D.H., Huang L.M., Li J.L., et al.,2007. A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy, ISPRS Journal of Photogrammetry and Remote Sensing,62:25-33.
[41]车大为,陈圣波,吕乐婷等,2008.多角度遥感BRDF模型研究的现状与展望,吉林大学学报,38：229～231.
[42]陈洪滨,范学花,韩志刚,2006. POLDER多角度,多通道偏振探测器对地遥感观测研究进展.遥感技术与应用,21(2)：83～92.
[43]陈拉,2009.高光谱遥感数据在植被信息提取的应用研究,安徽农业科学,37(8)：2842～2843.
[44]董广香,张继贤,刘正军,2006. CHRIS/PROBA数据条带噪声去除方法比较[J],遥感信息,6：36～39.
[45]方秀琴,张万昌,刘三超,2004.黑河流域叶面积指数的遥感估算,国土资源遥感,1：27-30.
[46]冯晓明,2006.多角度MISR数据用于区域生态环境定量遥感研究.,中国科学院研究生院博士论文.
[47]冯晓明,赵英时,陈永康,2003. MISR宽波段反照率反演及其大气关系研究,国土资源遥感,4：22～25.
[48]冯伟,朱艳,姚霞等,2009.基于高光谱遥感的小麦叶干重和叶面积指数监测,植物生态学报,33(1)：34～44.
[49]高峰,朱启疆,1997.植被冠层多角度遥感研究进展[J],地理科学,17(4)：346～355.
[50]盖利亚,刘正军,张继贤,CHRIS/PROBA高光谱数据的预处理,测绘工程,17(1)：40～43.
[51]黄文江,王纪华,刘良云等,2005.基于多时相和多角度光谱信息的作物株型遥感识别初探,农业工程学报,21(6)：82～86.
[52]李淑敏,李红,孙丹峰,周连第,2009. PROSAIL冠层光谱模型遥感反演区域叶面积指数,光谱学与光谱分析,29(10)：2725～2729.
[53]李飞,周成虎,陈荣国,2008.基于光谱曲线形态的高光谱影像检索方法研究,光谱学与光谱分析,28(11)：2484～2486.
[54]李小文,王锦地,1995.植被光学遥感模型与植被结构参数化[M],科学出版社.
[55]梁尧钦,曾辉,2009.高光谱遥感在植被特征识别研究中的应用,世界林业研究,22(1)：41～47.
[56]林鹏飞.赵敏,张翼飞等,2008.基于SPOT5遥感影像的城市森林叶面积指数反演,测绘科学,33(2)：57～63.
[57]凌飞龙,李增元,陈尔学,何祺胜,2009.青海云杉林叶面积指数半球摄影测量方法研究,地球科学进展,24(7)：803～809.
[58]刘晓臣,范闻捷,田庆久,徐希儒,2008.不同叶面积指数反演方法比较研究,北京大学学报,44(5)：827～834.
[59]庞勇,李增元,2003.干涉雷达技术用于林分高估测.遥感学报,7(1)：8～13.
[60]任海,彭少麟,张祝平等,1996.鼎湖山季风常绿阔叶林冠结构与冠层辐射研究[J],生态学报,16(2)：174～179.
[61]申广荣,王人潮,2001.植被高光谱遥感的应用研究综述,上海交通大学学报,19(4)：315～319.
[62]孙鹏森,刘世荣,刘京涛,2006.利用不同分辨率影像的NDVI数据估算叶面积指数(LAI),生态学报,26(11)：3826～3834.
[63]谭炳香,2006.高光谱遥感森林类型识别及其郁闭度定量估测研究,国林业科学研究院博士论文.
[64]唐世浩,朱启疆,孙睿,2006.基于方向反射率的大尺度叶面积指数反演算法及验证,自然科学进展,16(3)：331～336.
[65]唐延林,王秀珍,王福民等,2004.农作物LAI和生物量的高光谱法测定[J],西北农林科技大学学报,32(11)：100～104.
[66]万华伟,王锦地,梁顺林,秦军,2009.联合MODIS与MISR遥感数据估算叶面积指数,光谱学与光学分析,29(11)：3106～3110.
[67]万华伟,王锦地,肖志强等,2007. MISR多角度遥感数据的组件库处理方法及数据分析,遥感信息,4：17～19.
[68]王殿中,2008.基于模型的森林空间结构参数反演研究,中国科学研究生院博士论文.
[69]王东伟,孟宪智,王锦地等,2009.叶面积指数遥感反演方法进展,23(4)：47～52.
[70]汪骏发,高晓萍,陈志峰等,2001.多角度遥感及其航空成像仪,红外与毫米波学报,20(5)：329～334.
[71]王雪军,付晓,徐茂松,2008.测试角与DEM精度对SPOT5正射影像的影响分析,林业科学研究,21：130～133.
[72]吴朝阳,牛铮,2008.基于辐射传输模型的高光谱植被指数与叶绿素浓度及叶面积指数的线性关系改进,植物学通报,6：714～720.
[73]吴伟斌,洪添胜,王锡平等,2007.叶面积指数地面测量方法的研究进展,华中农业大学学报,26(2)：270～275.
[74]向洪波,郭志华,赵占轻,王建力,2009.不同空间尺度森林叶面积指数的估算方法,林业科学,45(6)：139～144.
[75]杨燕,田庆久,2007.高光谱反演水稻叶面积指数的主成分分析法,国土资源遥感,3：47～50.
[76]杨燕,田庆久,2007.水稻LAI参数的Hyperion反演研究,遥感技术应用,22(3)：345～349.
[77]姚延娟,阎广建,王锦地,2005.多光谱多角度遥感数据综合反演叶面积指数方法研究[J],遥感学报,9(2)：117～122.
[78]张红旗,陈永瑞,牛栋,2004.红壤丘陵区针叶林有效叶面积指数遥感反演模型,江西农业大学学报,26(2)：159～163.
[79]张仁华,孙晓敏,朱治林,1998.叶面积指数的快速测定发放——植被定量遥感的地面标定技术[J],国土资源遥感,3,15～19.
[80]张万昌,钟山,胡少英,2008.黑河流域叶面积指数(LAI)空间尺度转换,生态学报,28(6)：2425～2503.
[81]赵传燕,沈卫华,彭焕华,2009.祁连山区青海云杉林冠层叶面积指数的反演方法,植物生态学报,33(5)：860～869.
[82]赵英时,2003.遥感应用分析原理与方法[M].
[83]周磊,辛晓平,李刚等,2009.高光谱遥感在草原监测中的应用,草业科学,26(4)：20～27.
[84]朱娟娟,2008.多角度遥感现状与发展[J],中国高新技术企业,10：72～72.
[2]Barducci A., Guzzi D., Marcoionni P., Pippi I.,2006. CHRIS-PROBA Performance Evaluation, Signal-to-Noise Ratio, Instrument Efficiency and Data Quality from Acquisitions over San Rossore Test site[C]. Proc. Of 3rd. ESA CHRIS/PROBA workshop, Frascati,21～23 March.
[3]Bothwell G.W., Hansen E.G., Vargo R.E. and Miller K.C.,2002. The multi-angle imaging spectroradiometer science. Data system, Its products, tools and performance[J], IEEE Transactions Geoscience Remote Sensing,40(7):1467～1476.
[4]Chen J.M.,1996a. Evalualtion of vegetation indices and a modified simple ratio for boreal applications, Canadian Journal of Remote Sensing,22(3):229～242.
[5]Chen J.M.,1996b. Optically-based Methods for Measuring Seasonal Variation of Leaf Area Index in Boreal Conifer stands, Agric. For. Meteorology,80:138163.
[6]Chen J.M., Black T.A.,1992. Defining leaf area index for non-fiat leaves, Plant Cell and Environment, 15:421～429.
[7]Chen J.M., Cihlar J.,1996. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM images, Remote Sensing of Environment,55:153～162.
[8]Chen J.M., Menges C.H. and Leblanc S.G.,2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment,97(4):447～457.
[9]Chen J.M., Pavlic G., Brown L., et al.,2001. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J], Remote Sens, Environ.,80:165～184.
[10]Chopping, M., et al.,2007. Large area mapping of southwestern forest crown cover, canopy height, and biomass using MISR, Remote Sens. Environ. Special Issue on Biodiversity and Ecology.
[11]Chopping M.J., et al.,2006. Mapping woody plant cover in desert grasslands using canopy reflectance modeling and MISR data. Geophys. Res. Lett,33.
[12]Chopping M., et al.,2006. Mapping shrub abundance in desert grasslands using geometric-optical modeling and multi-angle remote sensing with CHRIS/Proba. Remote Sensing of Environment,104(1): 62～73.
[13]David G.G., Dyk A., Tian Han, Hao Chen, Gates T., Niemann K.O.,2005. Multi-Temporal Evaluation with CHRIS of Coastal Forests[C]. Geoscience and Remote Sensing Symposium,2005. IGARSS '05. Proceedings.2005 IEEE International,5(7):3557～3560.
[14]Dawson, T.E.,1998. Fog in the California redwood forest:ecosystem inputs and use by plants. Oecologia,117(4):476～485.
[15]Dekker A.G., Vos R.J., Peters S.W.M.,2002. Analytical Algorithms for Lake Water TSM Estimation for Retrospective Analyses of TM and SPOT Sensor Data[J], International Journal of Remote Sensing, 23(1):15～35.
[16]Deuze J.L., Goloub P., Herman M., et al,2000. Estimate of the Aerosol Properties over the Ocean With POLDER[J],105(12):15329～15346.
[17]Deuze J.L., Herman M., Goloub P., et al.,1999. Characteristic of Aerosols over Ocean from POLDER/ADEOS-1[J], Geophysical Research Letters,26(10):1421～1424.
[18]Diner, D.J., et al.,1999. New Directions in Earth Observing:Scientific Applications of Multiangle Remote Sensing. Bulletin of the American Meteorological Society,80(11):2209～2228.
[19]Friedl, M.,2002. Global land cover mapping from MODIS algorithms and early results. Remote Sensing of Environment,83(1):287～302.
[20]Garcia J.C, Moreno J.,2004. Removol of noises in CHRIS/PROBA images:application to the SPARC campaign data[C], Proc. Of the 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[21]Goel, N.S.,1988. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews,4(1):1～222..
[22]Goloub P., Tanre D., Deuze J.L., et al.,1999. Validation of the First Algorithm Applied for Deriving the Aerosol Properties over the Ocean Using the POLDER/ADEOS Measurements[J]. IEEE Transactions on Geoscience and Remote Sensing,37(3):1586～1596.
[23]Grover K.D., Steven M.D., Rondeaux G., et al.,2000. Estimating albedo from limited spectral and angular data[J]. INT. J. Remote Sensing,21(1):155～165.
[24]Hagberg, J.O.,,L.M.H.Ulander, and J.Askne,1995. Repeat-pass SAR interferometry over forested terrain.Geoscience and Remote Sensing,IEEE Transactions on,33(2):331～340.
[25]Hall, F.G., J.R.,1995. Townshend, and E.T. Engman, Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment,51(1):138～156.
[26]Heiskanen,J.,2006. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data.Remote Sensing of Environment,103(1):97～114.
[27]Imhoff M.L., et al.,1995. A theoretical analysis of the effect of forest structure on synthetic aperture radar backscatter and the remote sensing of biomass. Geoscience and Remote Sensing,IEEE Transactions on,33(2):341～352.
[28]Kayitakire F, Defourny P.,2004. Forest Type Discrimination Using Multi-angle Hyperspectral Data[C], Proc. Of the 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[29]Kimes, D.S., et al.,2006. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment,100(4):503～511.
[30]Kneubuhler M, Koetz B, Richter R, Michael S, Itten K.,2006. Geometric and radiometric pre-processing of CHRIS/PROBA data over mountainous terrain[C], Proc. Of 3rd CHRIS/Proba Workshop, Frascati,21～23 March.
[31]Le Toan, T., et al.,1992. Relating forest biomass to SAR data.Geoscience and Remote Sensing,IEEE Transactions on,30(2):403～411.
[32]Li X.W., Wang J.D., Xiang Y.Q., et. al.,1997. Measuring Leaf Angle Distribution and Leaf Area Index of Conifer Canopies Simultaneously on Wide-angle Image[J]. Journal of Remote Sensing,1:54～61.
[33]Mannheim S., Heim B., Segl K., Kaufmann H.,2004. Monitoring of Lake Water Quality Using Hyperspectral CHRIS-PROBA[C]. Proc. Of the 2nd CHRIS/Proba workshop, ESA/ESRIN, Frascati, Italy,28～30 April.
[34]Price, J.C.,1993. Estimating leaf area index from satellite data, IEEE Trans. Geosci Remote Sensing, 31:727～734.
[35]Robert. A. Scbowengerdt,1997. Remote Sensing Modals and Methods for Image Processing[M]. ACADEMIC press.
[36]Liang S.L., Alan H., Strahler, Charles, Walthall,1999. Retrieval of Land Surface Albedo from Satellite Observations:A Simulation Study[J]. Journal of Applied Metrorology,38:712～725.
[37]Voes J.M., Ullivan N.H., Clinton B.D., et.al.,1995. Vertical leaf area distribution, light transmittance, and application of the Beer-Lambert law in four mature hardwood stands in the southern Appalachian[J]. Canadian Journal of Forest Resource,25:1036～1043.
[38]Zhang,Y.,et al.,2002. Assessing the information content of multiangle satellite data for mapping biomes.I.Statistical analysis.Remote Sensing of Environment,80(3):418～434.
[39]Zhang,Y., et al.,2002.,Assessing the information content of multiangle satellite data for mapping biomes II.Theory.Remote Sensing of Environment,80:435～446.
[40]Zhao D.H., Huang L.M., Li J.L., et al.,2007. A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy, ISPRS Journal of Photogrammetry and Remote Sensing,62:25-33.
[41]车大为,陈圣波,吕乐婷等,2008.多角度遥感BRDF模型研究的现状与展望,吉林大学学报,38：229～231.
[42]陈洪滨,范学花,韩志刚,2006. POLDER多角度,多通道偏振探测器对地遥感观测研究进展.遥感技术与应用,21(2)：83～92.
[43]陈拉,2009.高光谱遥感数据在植被信息提取的应用研究,安徽农业科学,37(8)：2842～2843.
[44]董广香,张继贤,刘正军,2006. CHRIS/PROBA数据条带噪声去除方法比较[J],遥感信息,6：36～39.
[45]方秀琴,张万昌,刘三超,2004.黑河流域叶面积指数的遥感估算,国土资源遥感,1：27-30.
[46]冯晓明,2006.多角度MISR数据用于区域生态环境定量遥感研究.,中国科学院研究生院博士论文.
[47]冯晓明,赵英时,陈永康,2003. MISR宽波段反照率反演及其大气关系研究,国土资源遥感,4：22～25.
[48]冯伟,朱艳,姚霞等,2009.基于高光谱遥感的小麦叶干重和叶面积指数监测,植物生态学报,33(1)：34～44.
[49]高峰,朱启疆,1997.植被冠层多角度遥感研究进展[J],地理科学,17(4)：346～355.
[50]盖利亚,刘正军,张继贤,CHRIS/PROBA高光谱数据的预处理,测绘工程,17(1)：40～43.
[51]黄文江,王纪华,刘良云等,2005.基于多时相和多角度光谱信息的作物株型遥感识别初探,农业工程学报,21(6)：82～86.
[52]李淑敏,李红,孙丹峰,周连第,2009. PROSAIL冠层光谱模型遥感反演区域叶面积指数,光谱学与光谱分析,29(10)：2725～2729.
[53]李飞,周成虎,陈荣国,2008.基于光谱曲线形态的高光谱影像检索方法研究,光谱学与光谱分析,28(11)：2484～2486.
[54]李小文,王锦地,1995.植被光学遥感模型与植被结构参数化[M],科学出版社.
[55]梁尧钦,曾辉,2009.高光谱遥感在植被特征识别研究中的应用,世界林业研究,22(1)：41～47.
[56]林鹏飞.赵敏,张翼飞等,2008.基于SPOT5遥感影像的城市森林叶面积指数反演,测绘科学,33(2)：57～63.
[57]凌飞龙,李增元,陈尔学,何祺胜,2009.青海云杉林叶面积指数半球摄影测量方法研究,地球科学进展,24(7)：803～809.
[58]刘晓臣,范闻捷,田庆久,徐希儒,2008.不同叶面积指数反演方法比较研究,北京大学学报,44(5)：827～834.
[59]庞勇,李增元,2003.干涉雷达技术用于林分高估测.遥感学报,7(1)：8～13.
[60]任海,彭少麟,张祝平等,1996.鼎湖山季风常绿阔叶林冠结构与冠层辐射研究[J],生态学报,16(2)：174～179.
[61]申广荣,王人潮,2001.植被高光谱遥感的应用研究综述,上海交通大学学报,19(4)：315～319.
[62]孙鹏森,刘世荣,刘京涛,2006.利用不同分辨率影像的NDVI数据估算叶面积指数(LAI),生态学报,26(11)：3826～3834.
[63]谭炳香,2006.高光谱遥感森林类型识别及其郁闭度定量估测研究,国林业科学研究院博士论文.
[64]唐世浩,朱启疆,孙睿,2006.基于方向反射率的大尺度叶面积指数反演算法及验证,自然科学进展,16(3)：331～336.
[65]唐延林,王秀珍,王福民等,2004.农作物LAI和生物量的高光谱法测定[J],西北农林科技大学学报,32(11)：100～104.
[66]万华伟,王锦地,梁顺林,秦军,2009.联合MODIS与MISR遥感数据估算叶面积指数,光谱学与光学分析,29(11)：3106～3110.
[67]万华伟,王锦地,肖志强等,2007. MISR多角度遥感数据的组件库处理方法及数据分析,遥感信息,4：17～19.
[68]王殿中,2008.基于模型的森林空间结构参数反演研究,中国科学研究生院博士论文.
[69]王东伟,孟宪智,王锦地等,2009.叶面积指数遥感反演方法进展,23(4)：47～52.
[70]汪骏发,高晓萍,陈志峰等,2001.多角度遥感及其航空成像仪,红外与毫米波学报,20(5)：329～334.
[71]王雪军,付晓,徐茂松,2008.测试角与DEM精度对SPOT5正射影像的影响分析,林业科学研究,21：130～133.
[72]吴朝阳,牛铮,2008.基于辐射传输模型的高光谱植被指数与叶绿素浓度及叶面积指数的线性关系改进,植物学通报,6：714～720.
[73]吴伟斌,洪添胜,王锡平等,2007.叶面积指数地面测量方法的研究进展,华中农业大学学报,26(2)：270～275.
[74]向洪波,郭志华,赵占轻,王建力,2009.不同空间尺度森林叶面积指数的估算方法,林业科学,45(6)：139～144.
[75]杨燕,田庆久,2007.高光谱反演水稻叶面积指数的主成分分析法,国土资源遥感,3：47～50.
[76]杨燕,田庆久,2007.水稻LAI参数的Hyperion反演研究,遥感技术应用,22(3)：345～349.
[77]姚延娟,阎广建,王锦地,2005.多光谱多角度遥感数据综合反演叶面积指数方法研究[J],遥感学报,9(2)：117～122.
[78]张红旗,陈永瑞,牛栋,2004.红壤丘陵区针叶林有效叶面积指数遥感反演模型,江西农业大学学报,26(2)：159～163.
[79]张仁华,孙晓敏,朱治林,1998.叶面积指数的快速测定发放——植被定量遥感的地面标定技术[J],国土资源遥感,3,15～19.
[80]张万昌,钟山,胡少英,2008.黑河流域叶面积指数(LAI)空间尺度转换,生态学报,28(6)：2425～2503.
[81]赵传燕,沈卫华,彭焕华,2009.祁连山区青海云杉林冠层叶面积指数的反演方法,植物生态学报,33(5)：860～869.
[82]赵英时,2003.遥感应用分析原理与方法[M].
[83]周磊,辛晓平,李刚等,2009.高光谱遥感在草原监测中的应用,草业科学,26(4)：20～27.
[84]朱娟娟,2008.多角度遥感现状与发展[J],中国高新技术企业,10：72～72.
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