Please wait a minute...
img

官方微信

遥感技术与应用  2008, Vol. 23 Issue (3): 264-271    DOI: 10.11873/j.issn.1004-0323.2008.3.264
研究与应用     
用高光谱数据反演健康与病害落叶松冠层光合色素含量的模型研究——基于2005年吉林省敦化、和龙两市落叶松冠层采样测量数据
石韧1,刘礼2,高娜3
(1.中国科学院遥感应用研究所遥感科学国家重点实验室,北京100101;
2.南京林业大学,江苏 南京210037;3.兰州大学,甘肃 兰州730000)
Retrieval of Photosynthetic Pigment Contents in Healthy andDiseased Larch Canopies by Hyperspectral Data
SHI Ren1,LIU Li2,GAO Na3
 (1.State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute ofRemote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University,Institute of Remote Sensing Applications,CAS,Beijing,100101;2.Nanjing Forest University,Nanjing,210037;3.Lanzhou University,Lanzhou,730000)
 全文: PDF(1423 KB)  
摘要:

健康落叶松与遭受病害落叶松的冠层光谱反射率曲线具有明显差异,利用反映这种差异的光谱特征参数建立回归模型,可为反演落叶松冠层光合色素含量进而诊断落叶松健康状况提供方法和途径。以吉林省延边州敦化、和龙两市林场中健康的和遭受落叶松早落病侵害的人工落叶松林为调查对象,在对野外采集的14个落叶松冠层样本进行光谱测量及光合色素含量测量的基础上,选取8个对落叶松冠层光合色素含量变化反映敏感的光谱参数参与建立其光合色素含量的一元线性回归和多元逐步回归模型。研究结果表明,不同健康程度的落叶松冠层光谱曲线在其可见光及近红外波段有3个比较明显的特征差异处,分别位于光谱曲线的“绿峰”、“红谷”和“红边”位置。利用反映这些差异的8个光谱特征参数建立落叶松冠层光合色素含量的回归模型,除 “红边”这一参数回归效果不令人满意外,其余7个参数均得到了较好的回归效果,其中利用峰谷波长差Dgr建立的关于总叶绿素和叶绿素b含量的一元回归模型R2值分别达到0.842 8和0.749 8,利用NDGI建立的关于叶绿素a和类胡萝卜素含量的一元回归模型R2值分别达到0.875 8和0.789 7;多元逐步回归模型的回归效果与一元回归模型相比,各判定系数R2值均有所提高,总叶绿素、叶绿素a、b和类胡萝卜素含量的回归模型R2值分别达到0.885、0.910、0.839和0.862。

关键词: 高光谱落叶松冠层光合色素含量健康回归模型    
Abstract:

The difference between the spectral reflectance curves of healthy and diseased larch canopies is obvious.In this work,we chose the larch plantations as the study object.Firstly,we selected 14 larch canopy samples and measured their spectral reflectance and photosynthetic pigment contents.Then we selected 8 spectral parameters to set up a series of linear regression models.The results show that 3 remarkable different regions occur in the visible and near infrared bands.The correlation between the spectral parameters and the photosynthetic pigment contents are good except the parameter REP.The R2 values of the single-variable linear regression models for estimation of the contents of total chlorophyll,chlorophyll a,chlorophyll band carotenoid are 0.8428,0.8758,0.7498 and 0.7897 respectively.The multivariable regression models were also set up and their performance was better than the single variable regression models.The R2 values of the multivariable regression models for estimation of the contents of total chlorophyll,chlorophyll a,chlorophyll b and carotenoid are 0.885, 0.910, 0.839 and 0.862 respectively.This work provides an approach for the retrieval of the forest canopy photosynthetic pigment contents by using hyperspectral data.

Key words: Hyperspectrum    Larch canopy    Photosynthetic pigment content    Health    Regression models
收稿日期: 2007-11-01 出版日期: 2011-10-25
:  TP 79  
基金资助:

中国科学院遥感应用研究所遥感科学国家重点实验室开放研究基金。

作者简介: 石韧:女,副研究员,主要从事环境遥感应用研究。E-mail: rshi@irsa.ac.cn。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

石韧,刘礼,高娜. 用高光谱数据反演健康与病害落叶松冠层光合色素含量的模型研究——基于2005年吉林省敦化、和龙两市落叶松冠层采样测量数据[J]. 遥感技术与应用, 2008, 23(3): 264-271.

SHI Ren,LIU Li,GAO Na. Retrieval of Photosynthetic Pigment Contents in Healthy andDiseased Larch Canopies by Hyperspectral Data. Remote Sensing Technology and Application, 2008, 23(3): 264-271.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2008.3.264        http://www.rsta.ac.cn/CN/Y2008/V23/I3/264

[1] Zhou Z M.Forest Pathology.Beijing:China Forest Publishing House,1990,99.[周仲铭.林木病理学[M].北京:中国林业出版社,1990.]
[2] Rock B N.Remote Sensing of Forest Damage[J].Bioscience.1986,36(7):439-445.
[3] Rock B N.Spectral Characterizition of Forest Damage Occurring on Whiteface Mountain, NY-Studies with the Fluorescence Line Imager (FLI) and Ground-based Spectrometers[C].SPIE Proceedings Volume 1298,Imaging Spectroscopyof the Terrestrial environment,1990.
[4] Boyel M.Senescence and Spectral Reflectance in Leaves ofNorthern Pin Oak [J]. Remote Sensing of Environment,1988,25:71-87.
[5] Miller J R.Detection of Spectral Effects in Individual Tree Crownsof Metal-injected Trees Using High-resolution Pushbroom Imagery[J].Proceedings of the 16th international Congess of Photogrammetry and Remote Sensing,1988,27:847-856.
[6] Wu J Y,Ni P,Feng S P,et al.Phytogeochemical Propertiesand Spectral Reflectance Characteristics of Korean Pine Forest in Spring in The Gold Mine Aera of ZhaoYuan City,Shangdong Province[J].Remote Sensing of Environment.[吴继友,倪健,冯素萍,等.山东省招远金矿区春季赤松林的植物地球化学和反射光谱特征[J].环境遥感, 1994,9(2):119-121.]
[7] Liu S W,Gan P P,Wang R S.The Application of HyperionData to Extracting Contamination Information of Vegetation in The Dexing Copper Mine,Jiangxi Province,China[J]. Remote Sensing of Land & Resources,2004,1:6-10.[刘圣伟,甘甫平,王润生.用卫星高光谱数据提取德兴铜矿区植被污染信息[J].国土资源遥感,2004,1:6-10.]
[8] Li Y M,Ni S X,Wang X Z.The Robustness of Liner Regression Model in Rice Leaf Chlorophyll Concentration Prediction [J].Journal of Remote Sensing,2003,7(5):364-371.[李云梅,倪绍祥,王秀珍.线形回归模型估算水稻叶片叶绿素含量的适宜性分析[J].遥感学报,2003,7(5):364-371.]
[9] Zhao X,Liu S H,Wang P J,et al.A Method for InvertingChlorophyll Content of Wheat Using Hyperspectral[J].Geography and Geo-Information Science,2004,20(3):36-39.[赵祥,刘素红,王培娟,等.基于高光谱数据的小麦叶绿素含量反演[J].地理与地理信息科学,2004,20(3):36-39.]
[10] Sun X M,Zhou Q F,He Q X.Hyperspectral Variables in Predicting Leaf Chlorophyll Content and Grain Protein Content in Rice[J].Acta Agronomica Sinica,2005,31(7):844-850.[孙雪梅,周启发,何秋霞.利用高光谱参数预测水稻叶片叶绿素和籽粒蛋白质含量[J] .作物学报,2005,31(7):844-850.]
[11] Tong Q X,Zhang B,Zheng L F.Hyperspectral Remote Sensing and It's Multidisciplinary Applications[M].Beijing:Publishing House of Electronics Industry,2006:42.[童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用[M].北京:电子工业出版社,2006.]
[12] Blackburn G A.Quantifying Chlorophylls and Caroteniods atLeaf and Canopy Scales:An Evaluation of Some Hyperspectral Approaches[J].Remote Sensing of Environment,1998,66:273-285.

[1] 陈伟民,张凌,宋冬梅,王斌,丁亚雄,许明明,崔建勇. 基于AdaBoost改进随机森林的高光谱图像地物分类方法研究[J]. 遥感技术与应用, 2018, 33(4): 612-620.
[2] 苏阳,祁元,王建华,徐菲楠,张金龙. 基于航空高光谱影像的额济纳绿洲土地覆被提取[J]. 遥感技术与应用, 2018, 33(2): 202-211.
[3] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.
[4] 郭宇柏,卓莉,陶海燕,曹晶晶,王芳. 基于空谱初始化的非负矩阵光谱混合像元盲分解[J]. 遥感技术与应用, 2018, 33(2): 216-226.
[5] 刘爱林,郭宝平,李岩山 . 基于离散粒子群算法的凸多模态高光谱图像端元提取研究[J]. 遥感技术与应用, 2018, 33(2): 227-232.
[6] 吴兴,张霞,孙雪剑,张立福,戚文超. SPARK卫星高光谱数据辐射质量评价[J]. 遥感技术与应用, 2018, 33(2): 233-240.
[7] 宋婷婷,付秀丽,陈玉,魏永明,王钦军,程先锋. 云南个旧矿区土壤锌污染遥感反演研究[J]. 遥感技术与应用, 2018, 33(1): 88-95.
[8] 刘慧珺,苏红军,赵-波. 基于改进萤火虫算法的高光谱遥感多特征优化方法[J]. 遥感技术与应用, 2018, 33(1): 110-118.
[9] 李伟娜,韦玮,张怀清,刘华. 基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J]. 遥感技术与应用, 2017, 32(5): 809-817.
[10] 肖昊,王杰. 基于IDL和MATLAB混合编程的两种光谱混合分析方法比较[J]. 遥感技术与应用, 2017, 32(5): 858-865.
[11] 唐超,邵龙义. 高光谱遥感地物目标识别算法及其在岩性特征提取中的应用[J]. 遥感技术与应用, 2017, 32(4): 691-697.
[12] 李恒凯,欧彬,刘雨婷,邱玉宝. 基于混合像元分解的高光谱影像柑橘识别方法[J]. 遥感技术与应用, 2017, 32(4): 743-750.
[13] 苏红军,赵波. 基于共形几何代数的高光谱遥感波段选择方法[J]. 遥感技术与应用, 2017, 32(3): 539-545.
[14] 史飞飞,高小红,杨灵玉,何林华,贾伟. 基于HJ-1A高光谱遥感数据的湟水流域典型农作物分类研究[J]. 遥感技术与应用, 2017, 32(2): 206-217.
[15] 李焱,王让会,管延龙,蒋烨林,吴晓全,彭擎. 基于高光谱反射特性的土壤全氮含量预测分析[J]. 遥感技术与应用, 2017, 32(1): 173-179.