基于机载LiDAR和高光谱融合的森林参数反演研究
|
摘要
森林资源是进行物质循环和能量交换的枢纽,具有调节气候、涵养水源、防风固沙、减少污染、保持生物多样性等多种功能,在维持生态平衡、人类生存与发展和社会进步等方面有着极为重要的作用。但是,由于人类长期过度采伐利用和破坏森林资源,致使地球生态环境遭受严重破坏。保护和发展森林资源使之可以永续利用已经得到世界上越来越多国家的广泛关注。先进的遥感技术已经逐步替代传统费时费力的地面调查工作,利用地物特有的光谱特性在遥感影像上的反映,对森林实行大面积的资源调查与监测。但是由于各方面条件的限制,目前研究还多限于星载较低空间分辨率且单一传感器的遥感数据,而对于水平和垂直结构都很复杂的北方森林而言,调查和监测工作很难做到细化。本文的研究工作正是基于机载高空间、高光谱分辨率数据和LiDAR数据相结合展开的,结合两种数据各自的特点,进行了复杂森林的树种识别、叶面积指数(LAI)及冠层叶绿素含量的森林参数反演研究。主要的内容、成果和结论为：
1)基于小脚印LiDAR获得的高密度点云数据,分离地面点与非地面点,得到表征冠层高度的冠层高度模型CHM,并结合样地实测树种的树高统计,对林间空隙掩膜,去除非林地区域,减小了非林地对树种光谱的干扰,提高了影像上树种光谱与参考光谱的识别与匹配,为分类前训练样本提取做好数据准备。
2)为减少噪声对光谱的影响,利用光谱微分技术将影像光谱和参考光谱进行一阶微分变换,选取代表地物特征的区间,计算两光谱的相关系数,提取相关系数高的像元光谱作为参考样本同类别的训练样本,实现了训练样本的自动提取。
3)对于高空间分辨率影像中的阴影像元,考虑到传统阴影像元信息补偿方法中存在的一些问题,提出通过计算太阳入射辐射方位来确定遮挡方向,进而对分类结果中阴影像元采取邻近填充的方法,既有科学依据,又简单易实现。
4)比较了仅高光谱数据和融合数据分别利用SAM和SVM分类器进行分类的结果,并得出LiDAR与CASI融合应用SVM分类器,并对分类后结果进行阴影填充的树种分类结果总体精度最高,达到86.68%,说明本文提出的对LiDAR与CASI融合数据的树种分类方法是可行的。
5)基于统计模型反演LAI。由于仅利用植被指数建立与LAI的相关关系会有一定的片面性,本文除提取高光谱植被指数外,还基于LiDAR的回波数量、回波强度等信息,提取了表征森林垂直结构信息的参数,共同作为统计模型的输入自变量；根据不同的森林类型,将实测的有效LAI转为真实的LAI作为因变量,通过逐步回归进行变量筛选,建模反演LAI并进行精度验证。LAI模拟值与真实值之间的R2为0.85, RMSE为0.456,表明两者相关性较高,用该模型进行的LAI反演精度是可靠的。
6)物理模型反演森林冠层生化参数涉及模型的尺度转换问题。对于第三章分类结果掩膜后的林地区域,根据树种类型,叶片模型分别选择阔叶PROSPECT和针叶LIBERTY、冠层选择SAIL辐射传输模型,并通过敏感性分析确定叶片模型输入参数的变动范围,最后模拟得到冠层的反射率。
7)建立输入参数与输出反射率之间的查找表,通过影像与模拟冠层反射率的匹配查找,得到相应的叶片尺度叶绿素含量,经验证,反演叶绿素含量与实测数据R2为0.8379,满足精度要求；再利用第四章反演得到的结构参数LAI,将叶片尺度叶绿素上推至冠层,实现了森林冠层叶绿素含量的反演。
Forest is key resource for material circulation and energy exchange. It has functions of climate regulation, water conservation, windbreak and sand-fixation, pollution reduction and biodiversity conservation. It also plays a very important role in maintaining ecological balance, human survival, economic development and social progress. However, the long-term over-harvesting and destruction on forest resources have resulted in serious damage to global ecological environment. Protect and develop forest resources to sustainable use have been concerned by a number of countries in world wide. The traditional time-consuming work of large-scale ground surveys has been gradually replace by remote sensing technology which using the special spectral characteristics of ground objects reflected in remote sensing images for forest resource survey and monitoring. However, due to various constraints, the present study mainly focus on the application of low spatial resolution satellite remote sensing data which with single sensor. It is a hard work to survey and monitoring the Boreal forest for its complex horizontal and vertical structure. This study is based on airborne high spatial resolution data, high spectral resolution data and LiDAR data, combined with the characteristics of each of the two data to identify the forest tree species, and inversed the leaf area index (LAI) and canopy chlorophyll content. The main contents and results include:
1) Separated the ground points and non-ground points from small footprint LiDAR with high-density point data, and created a canopy height model (CHM). Then removed the gap between trees by combined the statistics of tree height measured in field plots, it reduced the interference of non-forest spectra and improved matching of the image spectra and reference spectra of tree species, took preparation for the training samples extraction of classification.
2) To reduce the impact of noise on the spectrum, using spectral derivative technique to processed both imaging spectra and reference spectra by first spectral derivative transform, then selected the range of features on behalf of characteristics and calculated the correlation coefficient between two spectra, extracted the high correlation spectroscopy pixels as reference samples to achieve the automatic extraction of training samples.
3) For the pixels of shadow of high spatial resolution images, the traditional methods of shadow information compensation still have some problems. In this study we identified the block direction by calculating the solar radiation orientation and then filled the pixel of shadow from its neighboring pixel. This method is both scientific and simple in the work.
4) Compared the SAM and SVM classification accuracies using hyperspectral data only and the integration data, and results showed that using SVM to classify the integration data of LiDAR and CASI, and filled the shadow pixels after classification had the highest overall accuracy of classification which reached 86.68%. It indicated that the tree species classification method in this study is feasible for the integration data of LiDAR and CASI.
5) The inversion of LAI based on statistical model. Since established the correlation between vegetation index and LAI still have onesidedness. In this study, the vegetation index and the vertical structure parameters were both extracted based on the echo numbers and intensities of LiDAR. These parameters were input as statistical model variables. Then the measured effective LAIs were converted into real LAIs as the dependent variable according to the different forest types and stepwise regression was used for variables selection, and the inversion model was created and validated simultaneously.
6) The physical model of forest canopy biochemical parameters inversion related the problem of scaling. For the masked forest area with the classified result of Chapter III, we choose PROSPECT, LIBERTY model as broadleaf and coniferous radiative transfer model, respectively. The SAIL model was used as canopy radiative transfer model. The scope of changes was determined by analysing the sensitivity, then, output the simulated canopy reflectance.
7) The lookup table was established between input parameters and output reflectances. The leaf chlorophyll contents were derived by matching the coincident images and simulated canopy reflectance. The R2 between retrieved chlorophyll and measured data was 0.8379, which satisfied the requirements accuracy. And then the inversion of forest canopy chlorophyll contents were achieved by scaled the leaf chlorophyll contents up to canopy.
1)基于小脚印LiDAR获得的高密度点云数据,分离地面点与非地面点,得到表征冠层高度的冠层高度模型CHM,并结合样地实测树种的树高统计,对林间空隙掩膜,去除非林地区域,减小了非林地对树种光谱的干扰,提高了影像上树种光谱与参考光谱的识别与匹配,为分类前训练样本提取做好数据准备。
2)为减少噪声对光谱的影响,利用光谱微分技术将影像光谱和参考光谱进行一阶微分变换,选取代表地物特征的区间,计算两光谱的相关系数,提取相关系数高的像元光谱作为参考样本同类别的训练样本,实现了训练样本的自动提取。
3)对于高空间分辨率影像中的阴影像元,考虑到传统阴影像元信息补偿方法中存在的一些问题,提出通过计算太阳入射辐射方位来确定遮挡方向,进而对分类结果中阴影像元采取邻近填充的方法,既有科学依据,又简单易实现。
4)比较了仅高光谱数据和融合数据分别利用SAM和SVM分类器进行分类的结果,并得出LiDAR与CASI融合应用SVM分类器,并对分类后结果进行阴影填充的树种分类结果总体精度最高,达到86.68%,说明本文提出的对LiDAR与CASI融合数据的树种分类方法是可行的。
5)基于统计模型反演LAI。由于仅利用植被指数建立与LAI的相关关系会有一定的片面性,本文除提取高光谱植被指数外,还基于LiDAR的回波数量、回波强度等信息,提取了表征森林垂直结构信息的参数,共同作为统计模型的输入自变量；根据不同的森林类型,将实测的有效LAI转为真实的LAI作为因变量,通过逐步回归进行变量筛选,建模反演LAI并进行精度验证。LAI模拟值与真实值之间的R2为0.85, RMSE为0.456,表明两者相关性较高,用该模型进行的LAI反演精度是可靠的。
6)物理模型反演森林冠层生化参数涉及模型的尺度转换问题。对于第三章分类结果掩膜后的林地区域,根据树种类型,叶片模型分别选择阔叶PROSPECT和针叶LIBERTY、冠层选择SAIL辐射传输模型,并通过敏感性分析确定叶片模型输入参数的变动范围,最后模拟得到冠层的反射率。
7)建立输入参数与输出反射率之间的查找表,通过影像与模拟冠层反射率的匹配查找,得到相应的叶片尺度叶绿素含量,经验证,反演叶绿素含量与实测数据R2为0.8379,满足精度要求；再利用第四章反演得到的结构参数LAI,将叶片尺度叶绿素上推至冠层,实现了森林冠层叶绿素含量的反演。
Forest is key resource for material circulation and energy exchange. It has functions of climate regulation, water conservation, windbreak and sand-fixation, pollution reduction and biodiversity conservation. It also plays a very important role in maintaining ecological balance, human survival, economic development and social progress. However, the long-term over-harvesting and destruction on forest resources have resulted in serious damage to global ecological environment. Protect and develop forest resources to sustainable use have been concerned by a number of countries in world wide. The traditional time-consuming work of large-scale ground surveys has been gradually replace by remote sensing technology which using the special spectral characteristics of ground objects reflected in remote sensing images for forest resource survey and monitoring. However, due to various constraints, the present study mainly focus on the application of low spatial resolution satellite remote sensing data which with single sensor. It is a hard work to survey and monitoring the Boreal forest for its complex horizontal and vertical structure. This study is based on airborne high spatial resolution data, high spectral resolution data and LiDAR data, combined with the characteristics of each of the two data to identify the forest tree species, and inversed the leaf area index (LAI) and canopy chlorophyll content. The main contents and results include:
1) Separated the ground points and non-ground points from small footprint LiDAR with high-density point data, and created a canopy height model (CHM). Then removed the gap between trees by combined the statistics of tree height measured in field plots, it reduced the interference of non-forest spectra and improved matching of the image spectra and reference spectra of tree species, took preparation for the training samples extraction of classification.
2) To reduce the impact of noise on the spectrum, using spectral derivative technique to processed both imaging spectra and reference spectra by first spectral derivative transform, then selected the range of features on behalf of characteristics and calculated the correlation coefficient between two spectra, extracted the high correlation spectroscopy pixels as reference samples to achieve the automatic extraction of training samples.
3) For the pixels of shadow of high spatial resolution images, the traditional methods of shadow information compensation still have some problems. In this study we identified the block direction by calculating the solar radiation orientation and then filled the pixel of shadow from its neighboring pixel. This method is both scientific and simple in the work.
4) Compared the SAM and SVM classification accuracies using hyperspectral data only and the integration data, and results showed that using SVM to classify the integration data of LiDAR and CASI, and filled the shadow pixels after classification had the highest overall accuracy of classification which reached 86.68%. It indicated that the tree species classification method in this study is feasible for the integration data of LiDAR and CASI.
5) The inversion of LAI based on statistical model. Since established the correlation between vegetation index and LAI still have onesidedness. In this study, the vegetation index and the vertical structure parameters were both extracted based on the echo numbers and intensities of LiDAR. These parameters were input as statistical model variables. Then the measured effective LAIs were converted into real LAIs as the dependent variable according to the different forest types and stepwise regression was used for variables selection, and the inversion model was created and validated simultaneously.
6) The physical model of forest canopy biochemical parameters inversion related the problem of scaling. For the masked forest area with the classified result of Chapter III, we choose PROSPECT, LIBERTY model as broadleaf and coniferous radiative transfer model, respectively. The SAIL model was used as canopy radiative transfer model. The scope of changes was determined by analysing the sensitivity, then, output the simulated canopy reflectance.
7) The lookup table was established between input parameters and output reflectances. The leaf chlorophyll contents were derived by matching the coincident images and simulated canopy reflectance. The R2 between retrieved chlorophyll and measured data was 0.8379, which satisfied the requirements accuracy. And then the inversion of forest canopy chlorophyll contents were achieved by scaled the leaf chlorophyll contents up to canopy.
引文
[1]陈述彭,童庆禧,郭华东.遥感信息机理研究.北京：科学出版社,1998.
[2]Lillesand T M, Kiefer R W. Remote Sensing and Image Interpretation.3rd. New York: John Wiley & Sons, Inc.,1994.1-750.
[3]Hunt G R. Electromagnetic Radiation:The Communication Link in Remote Sensing.New York: John Wiley and Sons,1980.
[4]Green R 0, Eastwood M L, Sarture C M, et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer(Aviris). Remote Sensing of Environment. 1998.65(3):227-248.
[5]浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000.
[6]Price J C. An Approach for Analysis of Reflectance Spectra. Remote Sensing of Environment.1998.64(3):316-330.
[7]Roberts D A, Smith M O, B. A J. Green Vegetation, Nonphotosynthetic Vegetation, and Soils in Aviris Data. Remote Sensing of Environment.1993.44:255-269.
[8]Treitz P, Howarth P. High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale. Remote Sensing of Environment. 2000.72(3):268-289.
[9]Franklin S E, McDermid G J. Empirical Relations between Digital Spot Hrv and Casi Spectral Response and Lodgepole Pine (Pinus Contorta) Forest Stand Parameters. International Journal of Remote Sensing.1993.14(12):2331-2348.
[10]范文义.荒漠化程度评价高光谱遥感信息模型.林业科学.2002.38(2)：61-67.
[11]Kokaly R F, Clark R N. Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression. Remote Sensing of Environment.1999.67(3):267-287.
[12]张凤丽,万余庆.利用高光谱数据进行植被生化成分反演方法研究.地球信息科学.2001(4)：71-75.
[13]浦瑞良,宫鹏.森林生物化学与casi高光谱分辨率遥感数据的相关分析.遥感学报.1997.1(2)：115-123.
[14]Huang Z, Turner B J, Dury S J, et al. Estimating Foliage Nitrogen Concentration from Hymap Data Using Continuum Removal Analysis. Remote Sensing of Environment. 2004.93(1-2):18-29.
[15]Zarco-Tejada P J, Miller J R, Mohammed G H, et al. Chlorophyll Fluorescence Effects on Vegetation Apparent Reflectance:Ii. Laboratory and Airborne Canopy-Level Measurements with Hyperspectral Data. Remote Sensing of Environment.2000.74(3):596-608.
[16]White J C, Coops N C, Hilker T, et al. Detecting Mountain Pine Beetle Red Attack Damage with Eo-1 Hyperion Moisture Indices International Journal of Remote Sensing. 2007.28:2111-2122.
[17]Wulder M A, Dymond C C, White J C, et al. Surveying Mountain Pine Beetle Damage of Forests:A Review of Remote Sensing Opportunities. Forest Ecology and Management. 2006.221(1-3):27-41.
[18]Wulder M A, White J C, Coops N C, et al. Multi-Temporal Analysis of High Spatial Resolution Imagery for Disturbance Monitoring. Remote Sensing of Environment. 2008.112(6):2729-2740.
[19]Eklundh L, Johansson T, Solberg S. Mapping Insect Defoliation in Scots Pine with Modis Time-Series Data. Remote Sensing of Environment.2009.113(7):1566-1573.
[20]谢红接,李剑锋,刘德长.高光谱数据处理及其在广西苗儿山地区的地质应用研究.铀矿地质.1999.15(1)：47-54.
[21]张宗贵,王润生,郭小方等.基于地物光谱特征的成像光谱遥感矿物识别方法.地学前缘.2003.10(2)：437-443.
[22]van der Meero F, Bakker W. Cross Correlogram Spectral Matching:Application to Surface Mineralogical Mapping by Using Aviris Data from Cuprite, Nevada. Remote Sensing of Environment.1997.61(3):371-382.
[23]Crosta A P, Sabine C, Taranik J V. Hydrothermal Alteration Mapping at Bodie, California, Using Aviris Hyperspectral Data. Remote Sensing of Environment.1998.65(3):309-319.
[24]Gao B C, Goetz A F H, Heidebrecht K B. Derivation of Scaled Surface Reflectances from Aviris Data. Remote Sensing of Environment.1993.44(2-3):165-178.
[25]赵凤生,丁强,孙同明等.利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法,气象学报.2002.60(5)：594-601.
[26]Arking A, Childs J D. Retrieval of Cloud Cover Parameters from Multispectral Satellite Images. Journal of Applied Meteorology.1985.24(4):322-334.
[27]孙林,柳钦火,陈良富等.环境与减灾小卫星高光谱成像仪陆地气溶胶光学厚度反演.遥感学报.2006.10(5)：770-776.
[28]潘德炉,李淑菁.卫星海洋水色遥感信息特征量的研究.遥感学报.1998.2(1)：26-31.
[29]李红波,舒嵘,薛永祺.Phi超光谱成像系统及其海洋遥感应用前景分析.红外与毫米波学报.2002.21(6)：429-433.
[30]Nolin A W, Dozier J. A Hyperspectral Method for Remotely Sensing the Grain Size of Snow. Remote Sensing of Environment.2000.74(2):207-216.
[31]万余庆,张凤丽,閆永忠.用细分光谱仪数据分析水体泥土含量的方法研究.国土资源遥感.2002.(2)：51-55.
[32]Flowler R A. The Lowdown on Lidar. Earth Observation.2000.9(3):27-30.
[33]Petzold B, Reiss P, Stossel W. Laser Scanning--Surveying and Mapping Agencies Are Using a New Technique for the Derivation of Digital Terrain Models. ISPRS Journal of Photogrammetry and Remote Sensing.1999.54(2-3):95-104.
[34]Hu Y.Automated Extraction of Digital Terrain Models, Roads and Buildings Using Airborne Lidar Data.University of Calgary Doctor Degree.2004:222.
[35]Garvin J, Bufton J, Blair J, et al. Observations of the Earth's Topography from the Shuttle Laser Altimeter (Sla):Laser-Pulse Echo-Recovery Measurements of Terrestrial Surfaces. Physics and Chemistry of The Earth.1998.23(9-10):1053-1068.
[36]Smith D E, Zuber M T, Frey H V, et al. Topography of the Northern Hemisphere of Mars from the Mars Orbiter Laser Altimeter. Science.1998.279(5357):1686-1692.
[37]Garvin J B, Zuber M T, Bufton J L.Lunar Observer Laser Altimeter: Geoscience Applications.Lunar and Planetary Science Conference,Unite States,1988:379.
[38]李树楷,薛永祺.高效三维遥感集成技术系统.北京：科学出版社,2000.
[39]张小红.机载激光雷达测量技术理论与方法.武汉：武汉大学出版社,2007.27.
[40]杜国庆,史照良,龚越新等.Lidar技术在江苏沿海滩涂测绘中的应用研究.城市勘测.2007.(5)：23-26.
[41]Timothy D J, T M. Extracting Photogrammetric Ground Control from Lidar Dems for Change Detection. The photogrammetric record.2006.12(116):312-328.
[42]王俊刚,李新科.机载激光雷达技术在电网工程建设中的应用.广东电力.2009.22(9)：46-49.
[43]李艳玲,杜殿斌,刘丽茹.激光雷达技术在城市三维建筑模型中的应用.测绘.2010.33(1)：42-44.
[44]Bornik A, Karner K, Bauer J, et al. High-Quality Texture Reconstruction from Multiple Views. Visualization and Computer Animation.2001.12(5):263-276.
[45]Debevec P E, Taylor C J, Malik, J. Modeling and Rendering Architecture from Photographs a Hybrid Geometry- and Image-Based Approach. Computer Graphics. 1996.30:11-20.
[46]Zhao H J, Shibasaki R. Reconstruction Textured Urban 3d Model by Fusing Ground_Based Laser Range Image and Ccd Image. ieice transactions on information and systems.2000.83(7):1429-1440.
[47]王晓南,郑顺义.基于激光扫描和高分辨率影像的文物三维重建.测绘工程.2009.18(6)：53-55.
[48]Nilsson M. Estimation of Tree Heights and Stand Volume Using an Airborne Lidar System. Remote Sensing of Environment.1996.56(1):1-7.
[49]Popescu S C, Wynne R H, Nelson R F. Estimating Plot-Level Tree Heights with Lidar: Local Filtering with a Canopy-Height Based Variable Window Size. Computers and Electronics in Agriculture.2002.37(1-3):71-95.
[50]孙国清,Ranson K J,张钟军.利用激光雷达和多角度频谱成像仪数据估测森林垂直参数.遥感学报.2006.10(4)：523-530.
[51]庞勇,赵峰,李增元等.机载激光雷达平均树高提取研究.遥感学报.2008.12(1)：152-158.
[52]Lefsky M A, Cohen W B, Spies T A. An Evaluation of Alternate Remote Sensing Products for Forest Inventory, Monitoring, and Mapping of Douglas-Fir Forests in Western Oregon. Canadian Journal of Forest Research.2001.31(1):78-87.
[53]Conese C, Maracchi G, Miglietta F, et al. Forest Classification by Principal Component Analyses of Tm Data International Journal of Remote Sensing.1988.9:1597-1612.
[54]Bolstad P V, Lillesand T M. Improved Classification of Forest Vegetation in Northern Wisconsin through a Rule-Based Combination of Soils, Terrain, and Landsat Thematic Mapper Data Forest Science.1992.38(1):5-20.
[55]Martin M E, Newman S D, Aber J D, et al. Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data. Remote Sensing of Environment. 1998.65(3):249-254.
[56]Clark M L, Roberts D A, Clark D B. Hyperspectral Discrimination of Tropical Rain Forest Tree Species at Leaf to Crown Scales. Remote Sensing of Environment.2005.96(3-4):375-398.
[57]Strahle A. The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data. Remote Sensing of Environment.1980.10:135-163.
[58]Kruse F A, Lefkoff A B, W B J. The Spectral Image Processing System(Sips)-Interactive Visualization and Analysis of Imaging Spectrometer Data. Remote Sensing of Environment.1993.44:145-163.
[59]陈尔学,李增元,谭炳香等.高光谱数据森林类型统计模式识别方法比较评价.林业科学.2007.43(1)：84-89.
[60]Goodenough D G, Dyk A, Niemann K 0, et al. Processing Hyperion and Ali for Forest Classification. IEEE Transactions on Geoscience and Remote Sensing.2003.41(6):1321-1331.
[61]李明诗,彭世揆,周林等.基于ASTER数据的决策树自动构建及分类研究.国土资源遥感.2006.(3)：33-36.
[62]王志辉,丁丽霞.基于叶片高光谱特性分析的树种识别.光谱学与光谱分析.2010.30(7)：1825-1829.
[63]Andersen H-E, McGaughey R J, Reutebuch S E. Estimating Forest Canopy Fuel Parameters Using Lidar Data. Remote Sensing of Environment.2005.94(4):441-449.
[64]Maltamo M, EERIKAINEN K, PITKANEN J, et al. Estimation of Timber Volume and Stem Density Based on Scanning Laser Altimetry and Expected Tree Size Distribution Functions. Remote Sensing of Environment.2004.90(3):319-330.
[65]Holmgren J, Persson A. Identifying Species of Individual Trees Using Airborne Laser Scanner. Remote Sensing of Environment.2004.90(4):415-423.
[66]Reitberger J, Krzystek P, Stilla U. Analysis of Full Waveform Lidar Data for the Classification of Deciduous and Coniferous Trees. International Journal of Remote Sensing.2008.29(5):1407-1431.
[67]Brandtberg T. Classifying Individual Tree Species under Leaf-Off and Leaf-on Conditions Using Airborne Lidar. ISPRS Journal of Photogrammetry and Remote Sensing. 2007.61(5):325-340.
[68]Asner G P, Knapp D E, Kennedy-Bowdoin T, et al. Invasive Species Detection in Hawaiian Rainforests Using Airborne Imaging Spectroscopy and Lidar. Remote Sensing of Environment.2008.112(5):1942-1955.
[69]Lucas R M, Lee A C, Bunting P J. Retrieving Forest Biomass through Integration of Casi and Lidar Data. International Journal of Remote Sensing.2008.29(5):1553-1577.
[70]Voss M, Sugumaran R. Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, Lidar, and an Object-Oriented Approach. Sensors. 2008.8:3020-3036.
[71]Sugumaran R, Voss M.Object-Oriented Classification of Lidar-Fused Hyperspectral Imagery for Tree Species Identification in an Urban Environment.Urban Remote Sensing Joint Event,Paris,2007.1-6.
[72]李奇,马洪超,邬建伟等.机载小光斑lidar的森林参数评估.林业资源管理.2008.(1)：74-81.
[73]何祺胜,陈尔学,曹春香等.基于lidar数据的森林参数反演方法研究.地球科学进展.2009.24(7)：748-755.
[74]刘清旺,李增元,陈尔学等.机载lidar点云数据估测单株木生物量.高技术通讯.2010.20(7).
[75]Liu J, Chen J M, Cihlar J, et al. A Process-Based Boreal Ecosystem Productivity Simulator Using Remote Sensing Inputs.New York, NY, ETATS-UNIS:Elsevier,1997.
[76]Gong P, Pu R L, Miller J, R. Coniferous Forest Leaf Area Index Estimation Along the Oregon Transect Using Compact Airborne Spectrographic Imager Data. Photogrammetric engineering and remote sensing.1995.61(9):1107-1117
[77]Wang Q, Adiku S, Tenhunen J, et al. On the Relationship of Ndvi with Leaf Area Index in a Deciduous Forest Site. Remote Sensing of Environment.2005.94(2):244-255.
[78]Birky A K. Ndvi and a Simple Model of Deciduous Forest Seasonal Dynamics. Ecological Modelling.2001.143(1-2):43-58.
[79]Propastin P A. Spatial Non-Stationarity and Scale-Dependency of Prediction Accuracy in the Remote Estimation of Lai over a Tropical Rainforest in Sulawesi, Indonesia. Remote Sensing of Environment.2009.113(10):2234-2242.
[80]Li X W, Strahler A H. Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy. IEEE Transactions on Geoscience and Remote Sensing.1986.24(6):906-919.
[81]Myneni R B, Nemani R R, Running S W. Estimation of Global Leaf Area Index and Absorbed Par Using Radiative Transfer Models. IEEE Transactions on Geoscience and Remote Sensing.1997.35:1380-1393.
[82]Shabanov N V, Wang Y, Buermann W, et al. Effect of Foliage Spatial Heterogeneity in the Modis Lai and Fpar Algorithm over Broadleaf Forests. Remote Sensing of Environment. 2003.85(4):410-423.
[83]姚延娟,陈良富,柳钦火等.基于波谱知识库的MODIS叶面积指数反演及验证.遥感学报.2006.10(6)：869-878.
[84]Deng F, Chen J M, Plummer S, et al. Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2006.44(8):2219-2229.
[85]Schlerf M, Atzberger C, Hill J. Remote Sensing of Forest Biophysical Variables Using Hymap Imaging Spectrometer Data. Remote Sensing of Environment.2005.95(2):177-194.
[86]Gong P, Pu R L, Biging G, et al. Estimation of Forest Leaf Area Index Using Vegetation Indices Derived from Hyperion Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing.2003.41(6):1355-1362.
[87]浦瑞良,宫鹏,Miller J R.用CASI遥感数据估测横跨美国俄勒冈州针叶林叶面积指数.南京林业大学学报.1993.17(1)：41-48.
[88]Pu R L, Gong P, Biging G, et al. Retrieval of Surface Reflectance and Lai Mapping with Data from Ali, Hyperion and Aviris. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2002).2002.1411-1413.
[89]宋开山,张柏,王宗明等.玉米和大豆LAI高光谱遥感估算模型研究.农业信息科学.2005.21(1)：318-322.
[90]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.植物生态学报.2004.28(1)：47-52.
[91]赵丽芳,谭炳香,杨华等.高光谱遥感森林叶面积指数估测研究现状.世界林业研究.2007.20(2)：50-54.
[92]刘晓臣,范闻捷,田庆久等.不同叶面积指数反演方法比较研究.北京大学学报：自然科学版.2008.44(5)：828—834.
[93]武红敢,乔彦友.马尾松林叶面积指数动态变化的遥感监测研究.植物生态学报.1997.21(5)：485-488.
[94]徐全芝,张万昌,刘三超等.黑河流域叶面积指数的遥感反演.干旱区研究.2003.20(4)：281-285.
[95]Jensen J L R, Humes K S, Vierling L A, et al. Discrete Return Lidar-Based Prediction of Leaf Area Index in Two Conifer Forests. Remote Sensing of the Environment. 2008.112(10):3947-3957.
[96]Farid A, Goodrich D C, Bryant R, et al. Using Airborne Lidar to Predict Leaf Area Index in Cottonwood Trees and Refine Riparian Water-Use Estimates. Journal of Arid Environments.2008.72:1-15.
[97]Barilotti A, Turco S, AlbertiAlberti A G Lai Determination in Forestry Ecosystem by Lidar Data Analysis. Workshop on 3D Remote Sensing in Forestry. BOKU Vienna,2006.
[98]Madeira A C, Mentions A, Ferreira M E, et al. Relationship between Spectroradiometric and Chlorophyll Measurements in Green Beans. Communications in Soil Science and Plant Analysis.2000.31(5):631-643.
[99]Jago R A, Cutler M E J, Curran P J. Estimating Canopy Chlorophyll Concentration from Field and Airborne Spectra. Remote Sensing of Environment.1999.68(3):217-224.
[100]Maccioni A, Agati G, Mazzinghi P. New Vegetation Indices for Remote Measurement of Chlorophylls Based on Leaf Directional Reflectance Spectra. Journal of Photochemistry and Photobiology B:Biology.2001.61(1-2):52-61.
[101]Broge N H, Leblanc E. Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density. Remote Sensing of Environment.2001.76(2):156-172.
[102]Houborg R, Boegh E. Mapping Leaf Chlorophyll and Leaf Area Index Using Inverse and Forward Canopy Reflectance Modeling and Spot Reflectance Data. Remote Sensing of Environment.2008.112:186-202.
[103]Verrelst J, Schaepman M E, Malenovsk Z, et al. Effects of Woody Elements on Simulated Canopy Reflectance: Implications for Forest Chlorophyll Content Retrieval. Remote Sensing of Environment.114(3):647-656.
[104]Malenovsky Z, Ufer C, Lhotakova Z, et al. A New Hyperspectral Index for Chlorophyll Estimation of a Forest Canopy:Area under Curve Normalised to Maximal Band Depth between 650-725 Nm. EARSeL eProceedings.2006.5(2):161-172.
[105]Blackburn G A, Ferwerda J G. Retrieval of Chlorophyll Concentration from Leaf Reflectance Spectra Using Wavelet Analysis. Remote Sensing of Environment. 2008.112(4):1614-1632.
[106]关丽,刘湘南.两种用于作物冠层叶绿素含量提取的改进光谱指数.地球科学进展.2009.24(5)：548-554.
[107]焦全军,张霞,张兵等.基于叶片光谱的森林叶绿素浓度反演研究.国土资源遥感.2006.(2)：26-30.
[108]Jacquemoud S, Baret F. Prospect: A Model of Leaf Optical Properties Spectra. Remote Sensing of Environment.1990.34(2):75-91.
[109]Dawson T P, Curran P J, Plummer S E. Liberty--Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra. Remote Sensing of Environment. 1998.65(1):50-60.
[110]Allen W A. Transmission of Isotropic Light across a Dielectric Surface in Two and Three Dimensions. Journal of the Optical Society of America.1973.63(6):664-666.
[111]Govaerts Y M, Stephane J, Michel M V, et al. Three-Dimensional Radiation Transfer Modeling in a Dicotyledon Leaf. Applied Optics.1996.35(33):6585-6598
[112]Maier S W, Ludeker W, Gunther K P. Slop:A Revised Version of the Stochastic Model for Leaf Optical Properties. Remote Sensing of Environment.1999.68(3):273-280.
[113]Feret J B, Francois C, Asner G.P., et al. Prospect-4 and 5:Advances in the Leaf Optical Properties Model Separating Photosynthetic Pigments. Remote Sensing of Environment. 2008.112 (6):3030-3043.
[114]Kuusk A. Monitoring of Vegetation Parameters on Large Areas by the Inversion of a Canopy Reflectance Model. International Journal of Remote Sensing.1998.19:2893-2905.
[115]Demarez V, Gastellu-Etchegorry J P. A Modeling Approach for Studying Forest Chlorophyll Content. Remote Sensing of Environment.2000.71:226-238.
[116]Jacquemoud S, Ustin S L, Verdebout J, et al. Estimating Leaf Biochemistry Using the Prospect Leaf Optical Properties Model. Remote Sensing of Environment. 1996.56(3):194-202.
[117]Zarco-Tejada P J, Miller J R, Harron J, et al. Needle Chlorophyll Content Estimation through Model Inversion Using Hyperspectral Data from Boreal Conifer Forest Canopies. Remote Sensing of Environment.2004.89:189-199.
[118]薛云,陈水森,夏丽华等.几个典型的叶片／冠层模型.西部林业科学.2005.34(1)：70-73.
[119]杨曦光,范文义,于颖.基于prospect+Sail模型的森林冠层叶绿素含量反演.光谱学与光谱分析.2010.30(11)：3022-3026.
[120]梁守真,施平,马万栋等.植被叶片光谱及红边特征与叶片生化组分关系的分析.中国生态农业学报.2010.18(4)：804-809.
[121]颜春燕,刘强,牛铮等.植被生化组分的遥感反演方法研究.遥感学报.2004.8(4)：300-308.
[122]Barnes J D, Balaguer L, Manrique E, et al. A Reappraisal of the Use of Dmso for the Extraction and Determination of Chlorophylls a and B in Lichens and Higher Plants. Environmental and Experimental Botany.1992.32(2):85-100.
[123]Seely GR, Duncan MJ, WE V. Preparative and Analytical Extraction of Pigments from Brown Algae with Dimethyl Sulfoxide. Marine Biology.1972.12(2):184-188.
[124]Minolta Camera Co Ltd.Japan: Osaka:Minolta,1989.
[125]杨曦光.高光谱数据提取森林冠层叶绿素及氮含量的研究.东北林业大学硕士论文.2010：23.
[126]Lindenberger J. Laser-Profilmessungen Zur Topographischen Gelandeaufnahme. Stuttgart: Universitat Stuttgart, Verlag der Bayerischen Akademie der Wissenschaften.
[127]Petzold B, Reiss P, Stossel W. Laser Scanning-Surveying and Mapping Agencies Are Using a New Technique for the Deviation of Digital Terrain Models.1999.54(2/3):95-104.
[128]Kraus K, Pfeifer N. Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data. ISPRS Journal of Photogrammetry and Remote Sensing. 1998.53(4):193-203.
[129]Vosselman G. Slope Based Filtering of Laser Altimetry Data International Archives of Photogrammetry and Remote Sensing.2000.33(B3):935-942.
[130]Ben-Dor E, Kruse F A, Lefkoff A B, et al. Comparison of Three Calibration Techniques for Utilization of Ger 63-Channel Aircraft Scanner Data of Makhtesh Ramon, Negev, Israel. Photogrammetric engineering and remote sensing.1994.60:1339-1354.
[131]Milton E J, Webb J P. Ground Radiometry and Airborne Multispectral Survey of Bare Soils. International Journal of Remote Sensing.1987.8:3-14.
[132]Kneizys F X, Shettle E P, Gallery W O, et al. Atmospheric Transmittance/Radiance: Computer Code Lowtran 5. Environmental Research Paper No697 Optical Space Division, Air Force Geophysics Laboratory, Massachusetts Air Force Systems Command. 1980.
[133]牛铮,王长耀等.碳循环遥感基础与应用.北京：科学出版社,2008.126-127,190-191,186.
[134]Smith K L, Steven M D, Colls J J. Use of Hyperspectral Derivative Ratios in the Red- Edge Region to Identify Plant Stress Responses to Gas Leaks. Remote Sensing of Environment.2004.92:207-217.
[135]Vapnik V N, Hoboken. Statistical Learning Theory.NJ:Wiley,1998.
[136]Camps-Valls G, Bruzzone L. Kernel-Based Methods for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2005.43(6):1351-1362.
[137]Melgani F, Bruzzone L. Classification of Hyperspectral Remote Sensing Images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing. 2004.42(8):1778-1790.
[138]赵英时.遥感应用分析原理与方法.北京：科学出版社,2003.102.
[139]徐希孺.遥感物理.北京：北京大学出版社,2005.
[140]Voicu L, Myler H R, Weeks A R. Practical Considerations on Color Image Enhancement Using Homomorphic Filtering. Journal of Electronic Imaging.1997.6(l):108-113.
[141]Zamudio J A, W. A W. Analysis of Aviris Data for Spectral Discrimination of Geologic Materials in the Dolly Varden Mountains. The Second AVIRIS Conference, Pasadena. 1990.
[142]金慧然,陶欣,范闻捷等.应用北京一号卫星数据监测高分辨率叶面积指数的空间分布.自然科学进展.2007.9(17)：1229-1234.
[143]孙鹏森,刘世荣,刘京涛等.利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI)——以岷江上游为例.生态学报.2006.11(26)：3826-3834.
[144]Chen J M, Black T A. Foliage Area and Architecture of Plant Canopies from Sunfleck Size Distributions. Agricultural and Forest Meteorology.1992.60(3-4):249-266.
[145]Tucker C. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment.1979.8:127-150.
[146]Justice C O, Vermote E, G T J R. The Moderate Resolution Imaging S Pectroradiometer (Modis):Land Remote Sensing for Global Change Research. IEEE Transactions on Geoscience and Remote Sensing.1998.36(4):1228-1249.
[147]Baret F, Guyot G. Potential and Limits of Vegetation Index for Lai and Apar Assessment. Remote Sensing of Environment.1991.35:161-173.
[148]Huete A R. A Soil-Adjusted Vegetation Index(Savi). Remote Sensing of Environment. 1988.25:295-309.
[149]Kim M S, Daughtry C S T, Chappelle E W, et al.The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynethetically Active Radiation(Apar).Proceeding of ISPRS,France,1994.299-306.
[150]Daughtry C S T, Walthall C L, Kim M S, et al. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment. 2000.74:229-239.
[151]Lefsky M A, Cohen W B, Parker G G, et al. Lidar Remote Sensing for Ecosystem Studies. BioScience.2002.52(1):19-30.
[152]Chen J M. Optically-Based Methods for Measuring Seasonal Variation of Leaf Area Index in Boreal Conifer Stands. Agricultural and Forest Meteorology. 1996.80(2-4):135-163.
[153]Chen J M, Black T A. Measuring Leaf Area Index of Plant Canopies with Branch Architecture. Agricultural and Forest Meteorology.1991.57(1-3):1-12.
[154]Chen J M, Cihlar J. Quantifying the Effect of Canopy Architecture on Optical Measurements of Leaf Area Index Using Two Gap Size Analysis Methods. IEEE Transactions on Geoscience and Remote Sensing.1995.33(3):777-787.
[155]Verhoef W. Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling:The Sail Model. Remote Sensing of Environment.1984.16(2):125-141.
[156]Koetza B, Baret F, Poilve H, et al. Use of Coupled Canopy Structure Dynamic and Radiative Transfer Models to Estimate Biophysical Canopy Characteristics. Remote Sensing of Environment.2005.95(1):115-124.
[157]Liang S, Zhong B, Fang H. Improved Estimation of Aerosol Optical Depth from Modis Imagery over Land Surfaces. Remote Sensing of Environment.2006.104(4):416-425.
[2]Lillesand T M, Kiefer R W. Remote Sensing and Image Interpretation.3rd. New York: John Wiley & Sons, Inc.,1994.1-750.
[3]Hunt G R. Electromagnetic Radiation:The Communication Link in Remote Sensing.New York: John Wiley and Sons,1980.
[4]Green R 0, Eastwood M L, Sarture C M, et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer(Aviris). Remote Sensing of Environment. 1998.65(3):227-248.
[5]浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000.
[6]Price J C. An Approach for Analysis of Reflectance Spectra. Remote Sensing of Environment.1998.64(3):316-330.
[7]Roberts D A, Smith M O, B. A J. Green Vegetation, Nonphotosynthetic Vegetation, and Soils in Aviris Data. Remote Sensing of Environment.1993.44:255-269.
[8]Treitz P, Howarth P. High Spatial Resolution Remote Sensing Data for Forest Ecosystem Classification: An Examination of Spatial Scale. Remote Sensing of Environment. 2000.72(3):268-289.
[9]Franklin S E, McDermid G J. Empirical Relations between Digital Spot Hrv and Casi Spectral Response and Lodgepole Pine (Pinus Contorta) Forest Stand Parameters. International Journal of Remote Sensing.1993.14(12):2331-2348.
[10]范文义.荒漠化程度评价高光谱遥感信息模型.林业科学.2002.38(2)：61-67.
[11]Kokaly R F, Clark R N. Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression. Remote Sensing of Environment.1999.67(3):267-287.
[12]张凤丽,万余庆.利用高光谱数据进行植被生化成分反演方法研究.地球信息科学.2001(4)：71-75.
[13]浦瑞良,宫鹏.森林生物化学与casi高光谱分辨率遥感数据的相关分析.遥感学报.1997.1(2)：115-123.
[14]Huang Z, Turner B J, Dury S J, et al. Estimating Foliage Nitrogen Concentration from Hymap Data Using Continuum Removal Analysis. Remote Sensing of Environment. 2004.93(1-2):18-29.
[15]Zarco-Tejada P J, Miller J R, Mohammed G H, et al. Chlorophyll Fluorescence Effects on Vegetation Apparent Reflectance:Ii. Laboratory and Airborne Canopy-Level Measurements with Hyperspectral Data. Remote Sensing of Environment.2000.74(3):596-608.
[16]White J C, Coops N C, Hilker T, et al. Detecting Mountain Pine Beetle Red Attack Damage with Eo-1 Hyperion Moisture Indices International Journal of Remote Sensing. 2007.28:2111-2122.
[17]Wulder M A, Dymond C C, White J C, et al. Surveying Mountain Pine Beetle Damage of Forests:A Review of Remote Sensing Opportunities. Forest Ecology and Management. 2006.221(1-3):27-41.
[18]Wulder M A, White J C, Coops N C, et al. Multi-Temporal Analysis of High Spatial Resolution Imagery for Disturbance Monitoring. Remote Sensing of Environment. 2008.112(6):2729-2740.
[19]Eklundh L, Johansson T, Solberg S. Mapping Insect Defoliation in Scots Pine with Modis Time-Series Data. Remote Sensing of Environment.2009.113(7):1566-1573.
[20]谢红接,李剑锋,刘德长.高光谱数据处理及其在广西苗儿山地区的地质应用研究.铀矿地质.1999.15(1)：47-54.
[21]张宗贵,王润生,郭小方等.基于地物光谱特征的成像光谱遥感矿物识别方法.地学前缘.2003.10(2)：437-443.
[22]van der Meero F, Bakker W. Cross Correlogram Spectral Matching:Application to Surface Mineralogical Mapping by Using Aviris Data from Cuprite, Nevada. Remote Sensing of Environment.1997.61(3):371-382.
[23]Crosta A P, Sabine C, Taranik J V. Hydrothermal Alteration Mapping at Bodie, California, Using Aviris Hyperspectral Data. Remote Sensing of Environment.1998.65(3):309-319.
[24]Gao B C, Goetz A F H, Heidebrecht K B. Derivation of Scaled Surface Reflectances from Aviris Data. Remote Sensing of Environment.1993.44(2-3):165-178.
[25]赵凤生,丁强,孙同明等.利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法,气象学报.2002.60(5)：594-601.
[26]Arking A, Childs J D. Retrieval of Cloud Cover Parameters from Multispectral Satellite Images. Journal of Applied Meteorology.1985.24(4):322-334.
[27]孙林,柳钦火,陈良富等.环境与减灾小卫星高光谱成像仪陆地气溶胶光学厚度反演.遥感学报.2006.10(5)：770-776.
[28]潘德炉,李淑菁.卫星海洋水色遥感信息特征量的研究.遥感学报.1998.2(1)：26-31.
[29]李红波,舒嵘,薛永祺.Phi超光谱成像系统及其海洋遥感应用前景分析.红外与毫米波学报.2002.21(6)：429-433.
[30]Nolin A W, Dozier J. A Hyperspectral Method for Remotely Sensing the Grain Size of Snow. Remote Sensing of Environment.2000.74(2):207-216.
[31]万余庆,张凤丽,閆永忠.用细分光谱仪数据分析水体泥土含量的方法研究.国土资源遥感.2002.(2)：51-55.
[32]Flowler R A. The Lowdown on Lidar. Earth Observation.2000.9(3):27-30.
[33]Petzold B, Reiss P, Stossel W. Laser Scanning--Surveying and Mapping Agencies Are Using a New Technique for the Derivation of Digital Terrain Models. ISPRS Journal of Photogrammetry and Remote Sensing.1999.54(2-3):95-104.
[34]Hu Y.Automated Extraction of Digital Terrain Models, Roads and Buildings Using Airborne Lidar Data.University of Calgary Doctor Degree.2004:222.
[35]Garvin J, Bufton J, Blair J, et al. Observations of the Earth's Topography from the Shuttle Laser Altimeter (Sla):Laser-Pulse Echo-Recovery Measurements of Terrestrial Surfaces. Physics and Chemistry of The Earth.1998.23(9-10):1053-1068.
[36]Smith D E, Zuber M T, Frey H V, et al. Topography of the Northern Hemisphere of Mars from the Mars Orbiter Laser Altimeter. Science.1998.279(5357):1686-1692.
[37]Garvin J B, Zuber M T, Bufton J L.Lunar Observer Laser Altimeter: Geoscience Applications.Lunar and Planetary Science Conference,Unite States,1988:379.
[38]李树楷,薛永祺.高效三维遥感集成技术系统.北京：科学出版社,2000.
[39]张小红.机载激光雷达测量技术理论与方法.武汉：武汉大学出版社,2007.27.
[40]杜国庆,史照良,龚越新等.Lidar技术在江苏沿海滩涂测绘中的应用研究.城市勘测.2007.(5)：23-26.
[41]Timothy D J, T M. Extracting Photogrammetric Ground Control from Lidar Dems for Change Detection. The photogrammetric record.2006.12(116):312-328.
[42]王俊刚,李新科.机载激光雷达技术在电网工程建设中的应用.广东电力.2009.22(9)：46-49.
[43]李艳玲,杜殿斌,刘丽茹.激光雷达技术在城市三维建筑模型中的应用.测绘.2010.33(1)：42-44.
[44]Bornik A, Karner K, Bauer J, et al. High-Quality Texture Reconstruction from Multiple Views. Visualization and Computer Animation.2001.12(5):263-276.
[45]Debevec P E, Taylor C J, Malik, J. Modeling and Rendering Architecture from Photographs a Hybrid Geometry- and Image-Based Approach. Computer Graphics. 1996.30:11-20.
[46]Zhao H J, Shibasaki R. Reconstruction Textured Urban 3d Model by Fusing Ground_Based Laser Range Image and Ccd Image. ieice transactions on information and systems.2000.83(7):1429-1440.
[47]王晓南,郑顺义.基于激光扫描和高分辨率影像的文物三维重建.测绘工程.2009.18(6)：53-55.
[48]Nilsson M. Estimation of Tree Heights and Stand Volume Using an Airborne Lidar System. Remote Sensing of Environment.1996.56(1):1-7.
[49]Popescu S C, Wynne R H, Nelson R F. Estimating Plot-Level Tree Heights with Lidar: Local Filtering with a Canopy-Height Based Variable Window Size. Computers and Electronics in Agriculture.2002.37(1-3):71-95.
[50]孙国清,Ranson K J,张钟军.利用激光雷达和多角度频谱成像仪数据估测森林垂直参数.遥感学报.2006.10(4)：523-530.
[51]庞勇,赵峰,李增元等.机载激光雷达平均树高提取研究.遥感学报.2008.12(1)：152-158.
[52]Lefsky M A, Cohen W B, Spies T A. An Evaluation of Alternate Remote Sensing Products for Forest Inventory, Monitoring, and Mapping of Douglas-Fir Forests in Western Oregon. Canadian Journal of Forest Research.2001.31(1):78-87.
[53]Conese C, Maracchi G, Miglietta F, et al. Forest Classification by Principal Component Analyses of Tm Data International Journal of Remote Sensing.1988.9:1597-1612.
[54]Bolstad P V, Lillesand T M. Improved Classification of Forest Vegetation in Northern Wisconsin through a Rule-Based Combination of Soils, Terrain, and Landsat Thematic Mapper Data Forest Science.1992.38(1):5-20.
[55]Martin M E, Newman S D, Aber J D, et al. Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data. Remote Sensing of Environment. 1998.65(3):249-254.
[56]Clark M L, Roberts D A, Clark D B. Hyperspectral Discrimination of Tropical Rain Forest Tree Species at Leaf to Crown Scales. Remote Sensing of Environment.2005.96(3-4):375-398.
[57]Strahle A. The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data. Remote Sensing of Environment.1980.10:135-163.
[58]Kruse F A, Lefkoff A B, W B J. The Spectral Image Processing System(Sips)-Interactive Visualization and Analysis of Imaging Spectrometer Data. Remote Sensing of Environment.1993.44:145-163.
[59]陈尔学,李增元,谭炳香等.高光谱数据森林类型统计模式识别方法比较评价.林业科学.2007.43(1)：84-89.
[60]Goodenough D G, Dyk A, Niemann K 0, et al. Processing Hyperion and Ali for Forest Classification. IEEE Transactions on Geoscience and Remote Sensing.2003.41(6):1321-1331.
[61]李明诗,彭世揆,周林等.基于ASTER数据的决策树自动构建及分类研究.国土资源遥感.2006.(3)：33-36.
[62]王志辉,丁丽霞.基于叶片高光谱特性分析的树种识别.光谱学与光谱分析.2010.30(7)：1825-1829.
[63]Andersen H-E, McGaughey R J, Reutebuch S E. Estimating Forest Canopy Fuel Parameters Using Lidar Data. Remote Sensing of Environment.2005.94(4):441-449.
[64]Maltamo M, EERIKAINEN K, PITKANEN J, et al. Estimation of Timber Volume and Stem Density Based on Scanning Laser Altimetry and Expected Tree Size Distribution Functions. Remote Sensing of Environment.2004.90(3):319-330.
[65]Holmgren J, Persson A. Identifying Species of Individual Trees Using Airborne Laser Scanner. Remote Sensing of Environment.2004.90(4):415-423.
[66]Reitberger J, Krzystek P, Stilla U. Analysis of Full Waveform Lidar Data for the Classification of Deciduous and Coniferous Trees. International Journal of Remote Sensing.2008.29(5):1407-1431.
[67]Brandtberg T. Classifying Individual Tree Species under Leaf-Off and Leaf-on Conditions Using Airborne Lidar. ISPRS Journal of Photogrammetry and Remote Sensing. 2007.61(5):325-340.
[68]Asner G P, Knapp D E, Kennedy-Bowdoin T, et al. Invasive Species Detection in Hawaiian Rainforests Using Airborne Imaging Spectroscopy and Lidar. Remote Sensing of Environment.2008.112(5):1942-1955.
[69]Lucas R M, Lee A C, Bunting P J. Retrieving Forest Biomass through Integration of Casi and Lidar Data. International Journal of Remote Sensing.2008.29(5):1553-1577.
[70]Voss M, Sugumaran R. Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, Lidar, and an Object-Oriented Approach. Sensors. 2008.8:3020-3036.
[71]Sugumaran R, Voss M.Object-Oriented Classification of Lidar-Fused Hyperspectral Imagery for Tree Species Identification in an Urban Environment.Urban Remote Sensing Joint Event,Paris,2007.1-6.
[72]李奇,马洪超,邬建伟等.机载小光斑lidar的森林参数评估.林业资源管理.2008.(1)：74-81.
[73]何祺胜,陈尔学,曹春香等.基于lidar数据的森林参数反演方法研究.地球科学进展.2009.24(7)：748-755.
[74]刘清旺,李增元,陈尔学等.机载lidar点云数据估测单株木生物量.高技术通讯.2010.20(7).
[75]Liu J, Chen J M, Cihlar J, et al. A Process-Based Boreal Ecosystem Productivity Simulator Using Remote Sensing Inputs.New York, NY, ETATS-UNIS:Elsevier,1997.
[76]Gong P, Pu R L, Miller J, R. Coniferous Forest Leaf Area Index Estimation Along the Oregon Transect Using Compact Airborne Spectrographic Imager Data. Photogrammetric engineering and remote sensing.1995.61(9):1107-1117
[77]Wang Q, Adiku S, Tenhunen J, et al. On the Relationship of Ndvi with Leaf Area Index in a Deciduous Forest Site. Remote Sensing of Environment.2005.94(2):244-255.
[78]Birky A K. Ndvi and a Simple Model of Deciduous Forest Seasonal Dynamics. Ecological Modelling.2001.143(1-2):43-58.
[79]Propastin P A. Spatial Non-Stationarity and Scale-Dependency of Prediction Accuracy in the Remote Estimation of Lai over a Tropical Rainforest in Sulawesi, Indonesia. Remote Sensing of Environment.2009.113(10):2234-2242.
[80]Li X W, Strahler A H. Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy. IEEE Transactions on Geoscience and Remote Sensing.1986.24(6):906-919.
[81]Myneni R B, Nemani R R, Running S W. Estimation of Global Leaf Area Index and Absorbed Par Using Radiative Transfer Models. IEEE Transactions on Geoscience and Remote Sensing.1997.35:1380-1393.
[82]Shabanov N V, Wang Y, Buermann W, et al. Effect of Foliage Spatial Heterogeneity in the Modis Lai and Fpar Algorithm over Broadleaf Forests. Remote Sensing of Environment. 2003.85(4):410-423.
[83]姚延娟,陈良富,柳钦火等.基于波谱知识库的MODIS叶面积指数反演及验证.遥感学报.2006.10(6)：869-878.
[84]Deng F, Chen J M, Plummer S, et al. Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2006.44(8):2219-2229.
[85]Schlerf M, Atzberger C, Hill J. Remote Sensing of Forest Biophysical Variables Using Hymap Imaging Spectrometer Data. Remote Sensing of Environment.2005.95(2):177-194.
[86]Gong P, Pu R L, Biging G, et al. Estimation of Forest Leaf Area Index Using Vegetation Indices Derived from Hyperion Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing.2003.41(6):1355-1362.
[87]浦瑞良,宫鹏,Miller J R.用CASI遥感数据估测横跨美国俄勒冈州针叶林叶面积指数.南京林业大学学报.1993.17(1)：41-48.
[88]Pu R L, Gong P, Biging G, et al. Retrieval of Surface Reflectance and Lai Mapping with Data from Ali, Hyperion and Aviris. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2002).2002.1411-1413.
[89]宋开山,张柏,王宗明等.玉米和大豆LAI高光谱遥感估算模型研究.农业信息科学.2005.21(1)：318-322.
[90]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.植物生态学报.2004.28(1)：47-52.
[91]赵丽芳,谭炳香,杨华等.高光谱遥感森林叶面积指数估测研究现状.世界林业研究.2007.20(2)：50-54.
[92]刘晓臣,范闻捷,田庆久等.不同叶面积指数反演方法比较研究.北京大学学报：自然科学版.2008.44(5)：828—834.
[93]武红敢,乔彦友.马尾松林叶面积指数动态变化的遥感监测研究.植物生态学报.1997.21(5)：485-488.
[94]徐全芝,张万昌,刘三超等.黑河流域叶面积指数的遥感反演.干旱区研究.2003.20(4)：281-285.
[95]Jensen J L R, Humes K S, Vierling L A, et al. Discrete Return Lidar-Based Prediction of Leaf Area Index in Two Conifer Forests. Remote Sensing of the Environment. 2008.112(10):3947-3957.
[96]Farid A, Goodrich D C, Bryant R, et al. Using Airborne Lidar to Predict Leaf Area Index in Cottonwood Trees and Refine Riparian Water-Use Estimates. Journal of Arid Environments.2008.72:1-15.
[97]Barilotti A, Turco S, AlbertiAlberti A G Lai Determination in Forestry Ecosystem by Lidar Data Analysis. Workshop on 3D Remote Sensing in Forestry. BOKU Vienna,2006.
[98]Madeira A C, Mentions A, Ferreira M E, et al. Relationship between Spectroradiometric and Chlorophyll Measurements in Green Beans. Communications in Soil Science and Plant Analysis.2000.31(5):631-643.
[99]Jago R A, Cutler M E J, Curran P J. Estimating Canopy Chlorophyll Concentration from Field and Airborne Spectra. Remote Sensing of Environment.1999.68(3):217-224.
[100]Maccioni A, Agati G, Mazzinghi P. New Vegetation Indices for Remote Measurement of Chlorophylls Based on Leaf Directional Reflectance Spectra. Journal of Photochemistry and Photobiology B:Biology.2001.61(1-2):52-61.
[101]Broge N H, Leblanc E. Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density. Remote Sensing of Environment.2001.76(2):156-172.
[102]Houborg R, Boegh E. Mapping Leaf Chlorophyll and Leaf Area Index Using Inverse and Forward Canopy Reflectance Modeling and Spot Reflectance Data. Remote Sensing of Environment.2008.112:186-202.
[103]Verrelst J, Schaepman M E, Malenovsk Z, et al. Effects of Woody Elements on Simulated Canopy Reflectance: Implications for Forest Chlorophyll Content Retrieval. Remote Sensing of Environment.114(3):647-656.
[104]Malenovsky Z, Ufer C, Lhotakova Z, et al. A New Hyperspectral Index for Chlorophyll Estimation of a Forest Canopy:Area under Curve Normalised to Maximal Band Depth between 650-725 Nm. EARSeL eProceedings.2006.5(2):161-172.
[105]Blackburn G A, Ferwerda J G. Retrieval of Chlorophyll Concentration from Leaf Reflectance Spectra Using Wavelet Analysis. Remote Sensing of Environment. 2008.112(4):1614-1632.
[106]关丽,刘湘南.两种用于作物冠层叶绿素含量提取的改进光谱指数.地球科学进展.2009.24(5)：548-554.
[107]焦全军,张霞,张兵等.基于叶片光谱的森林叶绿素浓度反演研究.国土资源遥感.2006.(2)：26-30.
[108]Jacquemoud S, Baret F. Prospect: A Model of Leaf Optical Properties Spectra. Remote Sensing of Environment.1990.34(2):75-91.
[109]Dawson T P, Curran P J, Plummer S E. Liberty--Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra. Remote Sensing of Environment. 1998.65(1):50-60.
[110]Allen W A. Transmission of Isotropic Light across a Dielectric Surface in Two and Three Dimensions. Journal of the Optical Society of America.1973.63(6):664-666.
[111]Govaerts Y M, Stephane J, Michel M V, et al. Three-Dimensional Radiation Transfer Modeling in a Dicotyledon Leaf. Applied Optics.1996.35(33):6585-6598
[112]Maier S W, Ludeker W, Gunther K P. Slop:A Revised Version of the Stochastic Model for Leaf Optical Properties. Remote Sensing of Environment.1999.68(3):273-280.
[113]Feret J B, Francois C, Asner G.P., et al. Prospect-4 and 5:Advances in the Leaf Optical Properties Model Separating Photosynthetic Pigments. Remote Sensing of Environment. 2008.112 (6):3030-3043.
[114]Kuusk A. Monitoring of Vegetation Parameters on Large Areas by the Inversion of a Canopy Reflectance Model. International Journal of Remote Sensing.1998.19:2893-2905.
[115]Demarez V, Gastellu-Etchegorry J P. A Modeling Approach for Studying Forest Chlorophyll Content. Remote Sensing of Environment.2000.71:226-238.
[116]Jacquemoud S, Ustin S L, Verdebout J, et al. Estimating Leaf Biochemistry Using the Prospect Leaf Optical Properties Model. Remote Sensing of Environment. 1996.56(3):194-202.
[117]Zarco-Tejada P J, Miller J R, Harron J, et al. Needle Chlorophyll Content Estimation through Model Inversion Using Hyperspectral Data from Boreal Conifer Forest Canopies. Remote Sensing of Environment.2004.89:189-199.
[118]薛云,陈水森,夏丽华等.几个典型的叶片／冠层模型.西部林业科学.2005.34(1)：70-73.
[119]杨曦光,范文义,于颖.基于prospect+Sail模型的森林冠层叶绿素含量反演.光谱学与光谱分析.2010.30(11)：3022-3026.
[120]梁守真,施平,马万栋等.植被叶片光谱及红边特征与叶片生化组分关系的分析.中国生态农业学报.2010.18(4)：804-809.
[121]颜春燕,刘强,牛铮等.植被生化组分的遥感反演方法研究.遥感学报.2004.8(4)：300-308.
[122]Barnes J D, Balaguer L, Manrique E, et al. A Reappraisal of the Use of Dmso for the Extraction and Determination of Chlorophylls a and B in Lichens and Higher Plants. Environmental and Experimental Botany.1992.32(2):85-100.
[123]Seely GR, Duncan MJ, WE V. Preparative and Analytical Extraction of Pigments from Brown Algae with Dimethyl Sulfoxide. Marine Biology.1972.12(2):184-188.
[124]Minolta Camera Co Ltd.Japan: Osaka:Minolta,1989.
[125]杨曦光.高光谱数据提取森林冠层叶绿素及氮含量的研究.东北林业大学硕士论文.2010：23.
[126]Lindenberger J. Laser-Profilmessungen Zur Topographischen Gelandeaufnahme. Stuttgart: Universitat Stuttgart, Verlag der Bayerischen Akademie der Wissenschaften.
[127]Petzold B, Reiss P, Stossel W. Laser Scanning-Surveying and Mapping Agencies Are Using a New Technique for the Deviation of Digital Terrain Models.1999.54(2/3):95-104.
[128]Kraus K, Pfeifer N. Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data. ISPRS Journal of Photogrammetry and Remote Sensing. 1998.53(4):193-203.
[129]Vosselman G. Slope Based Filtering of Laser Altimetry Data International Archives of Photogrammetry and Remote Sensing.2000.33(B3):935-942.
[130]Ben-Dor E, Kruse F A, Lefkoff A B, et al. Comparison of Three Calibration Techniques for Utilization of Ger 63-Channel Aircraft Scanner Data of Makhtesh Ramon, Negev, Israel. Photogrammetric engineering and remote sensing.1994.60:1339-1354.
[131]Milton E J, Webb J P. Ground Radiometry and Airborne Multispectral Survey of Bare Soils. International Journal of Remote Sensing.1987.8:3-14.
[132]Kneizys F X, Shettle E P, Gallery W O, et al. Atmospheric Transmittance/Radiance: Computer Code Lowtran 5. Environmental Research Paper No697 Optical Space Division, Air Force Geophysics Laboratory, Massachusetts Air Force Systems Command. 1980.
[133]牛铮,王长耀等.碳循环遥感基础与应用.北京：科学出版社,2008.126-127,190-191,186.
[134]Smith K L, Steven M D, Colls J J. Use of Hyperspectral Derivative Ratios in the Red- Edge Region to Identify Plant Stress Responses to Gas Leaks. Remote Sensing of Environment.2004.92:207-217.
[135]Vapnik V N, Hoboken. Statistical Learning Theory.NJ:Wiley,1998.
[136]Camps-Valls G, Bruzzone L. Kernel-Based Methods for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2005.43(6):1351-1362.
[137]Melgani F, Bruzzone L. Classification of Hyperspectral Remote Sensing Images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing. 2004.42(8):1778-1790.
[138]赵英时.遥感应用分析原理与方法.北京：科学出版社,2003.102.
[139]徐希孺.遥感物理.北京：北京大学出版社,2005.
[140]Voicu L, Myler H R, Weeks A R. Practical Considerations on Color Image Enhancement Using Homomorphic Filtering. Journal of Electronic Imaging.1997.6(l):108-113.
[141]Zamudio J A, W. A W. Analysis of Aviris Data for Spectral Discrimination of Geologic Materials in the Dolly Varden Mountains. The Second AVIRIS Conference, Pasadena. 1990.
[142]金慧然,陶欣,范闻捷等.应用北京一号卫星数据监测高分辨率叶面积指数的空间分布.自然科学进展.2007.9(17)：1229-1234.
[143]孙鹏森,刘世荣,刘京涛等.利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI)——以岷江上游为例.生态学报.2006.11(26)：3826-3834.
[144]Chen J M, Black T A. Foliage Area and Architecture of Plant Canopies from Sunfleck Size Distributions. Agricultural and Forest Meteorology.1992.60(3-4):249-266.
[145]Tucker C. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment.1979.8:127-150.
[146]Justice C O, Vermote E, G T J R. The Moderate Resolution Imaging S Pectroradiometer (Modis):Land Remote Sensing for Global Change Research. IEEE Transactions on Geoscience and Remote Sensing.1998.36(4):1228-1249.
[147]Baret F, Guyot G. Potential and Limits of Vegetation Index for Lai and Apar Assessment. Remote Sensing of Environment.1991.35:161-173.
[148]Huete A R. A Soil-Adjusted Vegetation Index(Savi). Remote Sensing of Environment. 1988.25:295-309.
[149]Kim M S, Daughtry C S T, Chappelle E W, et al.The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynethetically Active Radiation(Apar).Proceeding of ISPRS,France,1994.299-306.
[150]Daughtry C S T, Walthall C L, Kim M S, et al. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment. 2000.74:229-239.
[151]Lefsky M A, Cohen W B, Parker G G, et al. Lidar Remote Sensing for Ecosystem Studies. BioScience.2002.52(1):19-30.
[152]Chen J M. Optically-Based Methods for Measuring Seasonal Variation of Leaf Area Index in Boreal Conifer Stands. Agricultural and Forest Meteorology. 1996.80(2-4):135-163.
[153]Chen J M, Black T A. Measuring Leaf Area Index of Plant Canopies with Branch Architecture. Agricultural and Forest Meteorology.1991.57(1-3):1-12.
[154]Chen J M, Cihlar J. Quantifying the Effect of Canopy Architecture on Optical Measurements of Leaf Area Index Using Two Gap Size Analysis Methods. IEEE Transactions on Geoscience and Remote Sensing.1995.33(3):777-787.
[155]Verhoef W. Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling:The Sail Model. Remote Sensing of Environment.1984.16(2):125-141.
[156]Koetza B, Baret F, Poilve H, et al. Use of Coupled Canopy Structure Dynamic and Radiative Transfer Models to Estimate Biophysical Canopy Characteristics. Remote Sensing of Environment.2005.95(1):115-124.
[157]Liang S, Zhong B, Fang H. Improved Estimation of Aerosol Optical Depth from Modis Imagery over Land Surfaces. Remote Sensing of Environment.2006.104(4):416-425.