期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN:2194-9042
电子版ISSN:2194-9050
出版年度:2008
卷号:XXXVII Part B7
页码:369-376
出版社:Copernicus Publications
摘要:Developments in hyperspectral remote sensing have provided new indices or indicators of biochemical and biophysical properties. Most of the studies involving the novel spectral indices have been conducted at the leaf scale and have been rarely investigated for species discrimination. The objectives of the study were to determine hyperspectral indices that (i) are likely to be influenced by change in spectral measurement from the leaf to the canopy scale and (ii) can discriminate species at both scales. Leaf and canopy reflectance measurements were made from six species (3 shrubs, 3 trees) using an ASD spectroradiometer. The two-sample t test was used to evaluate whether significant differences exist between leaf and canopy indices, while differences between species pairs (15 pairs) were evaluated with ANOVA and pair-wise Bonferroni adjusted t tests. The hyperspectral indices evaluated in this study were, in general, sensitive to the change in spectral measurement scale from the leaf to the canopy. However, among the indices studied, red-edge positions (REP) extracted by the linear extrapolation I method were least sensitive to the change in measurement scale as three out of the six species showed no significant differences between the leaf and canopy indices. With respect to species discrimination, the canopy indices were better discriminators than the leaf indices. This is essential for air- or spaceborne remote sensing of species assemblages. The photochemical reflectance index (PRI) showed the highest potential to discriminate species at the canopy scale (all 15 pairs), while the linear extrapolation REPs showed the highest potential to discriminate the same species pairs (10 pairs) at both scales. Hyperspectral indices might provide new possibilities of differentiating plant species
关键词:Imaging Spectroscopy; Spectral Indices; Species Discrimination; Leaf and Canopy Remote Sensing