摘要
利用植被的光谱数据,探讨了植被冠层的光谱反射特征和诊断性光谱吸收特征。根据植被光谱特征和连续统去除法(CR),介绍了识别植被种类和预测植被冠层营养元素等生化组分含量的可能性。运用一阶微分反射比(FDR)和从连续统去除的光谱吸收特征中获得的波段深度(BD)、连续统去除后微分反射比(CRDR)、波段深度比(BDR)和归一化波段深度指数(NBDI)等变量,利用逐步线性回归模型并基于光谱吸收特征的变量来选择波长,并通过相关分析来预测植被冠层生化组分。
This paper analyzes the spectral reflected features and diagnostic absorption features of vegetation based on the hyperspectral imaging data,and describes the probability of recognizing plant species and predicting its biochemical concentrations like the macronutrients in terms of the vegetation spectral features and the continuum-removed absorption features.By use of First Derivative Reflectance （FDR） and some variables derived from continuum-removed absorption features such as Band Depth （BD）,Continuum-Removed Derivative Reflectance （CRDR）, Band Depth Ratio （BDR）,Normalized Band Depth Index （NBDI） and so on,also utilizing selected wavelengths by stepwise linear regression model according to the variables from the spectral absorption features,and combining correlation analysis,the vegetation canopy biochemicals can be predicted.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第31期213-215,222,共4页
Computer Engineering and Applications
基金
国防科技工业民用专项科研技术研究项目
国家基础测绘经费资助项目
关键词
植被遥感
吸收特征
生化组分预测
遥感生物化学
vegetation remote sensing
absorption features
predicting biochemicals
biochemistry of remote sensing
