【摘要】 以香格里拉市云南松、高山松、云杉和冷杉4种典型针叶树种为研究对象,运用ASD Field Spec 3地物光谱仪测定野外叶片光谱,并对原始光谱进行微分变换处理,再采用Fisher判别分析方法对4种针叶树种最佳波段窗口进行分析.结果表明:Fisher判别分析能有效判别4种典型针叶树种原始光谱、一阶微分光谱和二阶微分光谱差异显著的波段,主要位于近红外波段,最佳波段窗口分别为980～989、415～424、960～969 nm;原始光谱的二阶微分处理更能有效判别4种针叶树种,Fisher总判别精度高达98.8%;根据4种典型针叶树空间分布特征,将其分为云南松、高山松和云冷杉两组,云南松、高山松的最佳波段窗口为870～879、1 020～1 029、530～539 nm,云杉、冷杉的最佳波段窗口为540～549、520～529、1 150～1 159 nm.本研究结果可为中大尺度机载、星载高光谱遥感树种精细分类提供指导.更多还原

【Abstract】 The original spectrum of 4 typical conifer species in Shangri-la City, namely Pinus yunnanensis, Pinus densata, Picea asperata and Abies fabri, were measured by ASD Field Spec 3 spectrometer in November. Then, first-order differential and second-order differential were applied to the original spectrum data to identify characteristic bands for each species. Lastly, Fisher discriminant analysis was used to select the optimal band window among characteristic bands that effectively distinguish species one from another. The results show that near-infrared region was the waveband that showed significant differences in the original spectrum, first-order differential spectrum and second-order differential spectrum which can be effectively distinguished by Fisher discriminant analysis. The characteristic bands detected were 980-989, 415-424, 960-969 nm. Second-order differential turned out to be the most effective data processing method with Fisher′s total discriminant accuracy being 98.8%. Four species were regrouped into P.yunnanensis, P.densata and P.asperata, A.fabri according to their spatial distribution characteristics. The optimal band windows for distinguishing P.yunnanensis from P.densata were 870-879, 1 020-1 029, 530-539 nm, while the optimal band windows for distinguishing P.asperata from A.fabri were 540-549, 520-529, 1 150-1 159 nm.更多还原