title: [The changes in spectral features of the staple-food bamboos of giant panda after flowering].
abstract: Large-area flowering of the giant pandas' staple food is an important factor which can influence their survival. Therefore, it is necessary to predict the bamboo flowering. Foping Nature Reserve was taken as the study area. The research selected the giant pandas' staple-food bamboos Bashania fargesii, Fargesia qinlingensis and Fargesia dracocephala with different flowering situations (i. e., flowering, potential flowering, non-flowering with far distance) to measure the spectral reflectance of bamboo leaves. We studied the influence of bamboo flowering on the spectral features of three bamboo species through analyzing the original spectral reflectance and their red edge parameters. The results showed that (1) the flowering changed the spectra features of bamboo species. The spectral reflectance of B. fargesii shows a pattern: flowering bamboo < potential flowering bamboo < non-flowering bamboo with far distance, while F. qinlingensis and F. dracocephala show the different pattern: flowering bamboo > or = potential flowering bamboo > non-flowering bamboo with far distance. Among three bamboo species, F. dracocephala showed the greatest change, and then F. qinlingensis. (2) After bamboo flowering, the red edge of B. fargesii has no obvious shifting, while the other two bamboos have distinctive shifting towards the shorter waves. The study found that the original spectral feature and the red edge all changed under various flowering states, which can be used to provide the experimental basis and theoretic support for the future prediction of bamboo flowering through remote sensing.
authors: State Key Joint Laboratory of Environment Simulation and Pollution Control, Beijing 100084, China. xuehua-hjx@tsinghua.edu.cn
authors: Xue-Hua
authors: XH
authors: Liu
authors: Yan
authors: Y
authors: Wu
mesh_terms: D000818:Animals; D002681:China; D035264:Flowers; D018515:Plant Leaves / Q000737:chemistry*; D058998:Remote Sensing Technology / Q000379:methods; D031787:Sasa / Q000737:chemistry*; D013057:Spectrum Analysis / Q000379:methods*; D001503:Ursidae