Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí lâm nghiệp đề tài: An approach for the analysis of vegetation spectra using non-linear mixed modeling of truncated power spectra. | Ann. For. Sci. 61 2004 515-523 INRA EDP Sciences 2004 DOI forest 2004046 515 Original article An approach for the analysis of vegetation spectra using non-linear mixed modeling of truncated power spectra Steen MAGNUSSENa Nicholas COOPSb Joan E. LUTHERc Allan L. CARROLLa a Natural Resources Canada Canadian Forest Service 506 West Burnside Road Victoria V8Z 1M5 BC Canada b CSIRO Forestry and Forest Products Private Bag 10 Clayton South Vic. 3169 Australia c Natural Resources Canada Canadian Forest Service PO Box 960 Corner Brook A2H 6J3 NL Canada Received 15 July 2003 accepted 17 October 2003 Abstract - Analysis of vegetation spectra is often characterized by an adverse ratio of sample size to number of wavelengths. A reduction in the dimensionality of the spectra is needed to ensure consistent estimates. We propose a reduction based on a non-linear mixed modeling of power spectra transforms of truncated Fourier series representations of vegetation spectra. Two sets of foliage spectral data obtained from balsam fir Abies balsamea exposed to different silvicultural regimes and three eucalypt species Eucalyptus spp. demonstrate the method. Only the first 42 frequencies in a power spectrum contributed significantly to the variance of a spectrum. Power spectra were dominated by a small number of low frequencies the influence of frequency was described well by an exponentiated quadratic polynomial model with significant fixed and random effects. Model parameters can be subject to physiological inference and hypothesis testing. nonlinear-mixed model Fourier transform power spectra hypothesis testing classification Résumé - Méthode d analyse des spectres de vegetation par modélisation mixte non linéaire des spectres de puissance tronqués. L analyse des spectres de végétation est souvent caractérisée par un rapport négatif entre la taille de l échantillon et le nombre de longueurs d ondes. Une reduction de la dimension des spectres est nécessaire pour garantir des .
