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中国科学院昆明植物研究所知识管理系统
Knowledge Management System of Kunming Institute of Botany,CAS
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昆明植物所硕博研究生... [1]
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2010 [1]
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Pinus yunn... [1]
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资助项目:Pinus yunnanensis is one of the main timber species in Yunnan province, and widely distributed in southwest of China. Forest stand growth models are built for predicting stand growth, which could provide useful quantitative information for forest management. Understanding stand growth is also important for estimating forest carbon stock and carbon sequestration. Several studies of growth model for Pinus yunnanensis have been reported, however, most of which focus on a specific part of growth model. Therefore, it is necessary and meaningful to further the study of growth model for important stand factors. As a scaling-up tool for the observational data, remote sensing technique makes scaling biomass estimation from ground plot to a large scale more feasible and effective. Stand growth model was studied based on 91 sample plots data collected in the Yangliu Township, Longyang District, Baoshan Prefecture. Nonlinear fitting was used in fitting and selecting alternative growth functions, models of site index, density and stand age, average DBH, average tree height and stock volume were built in this study. Stock volume calculated from plot data was converted to aboveground biomass, belowground biomass and carbon stock according to selected equations and parameters. A total of 18 remote sensing variables were derived from SPOT 4 imagery, including reflectance of each band, normalized difference vegetation index, ration vegetation index, etc. The correlationship between aboveground biomass and remote sensing variables was analyzed, based on their significance of correlation, multiple regression, stepwise regression, nonlinear fitting were used to establish the aboveground biomass model. The effect of image processing levels on biomass estimation was compared, 3 types of images were analyzed including fusioned image without radiometric correction, radiometrically corrected to top of atmosphere (TOA) reflectance, and atmospherically corrected to surface reflectance. The main conclusions of this study are as follow: (1) Schumacher function fitted the stand growth better in site index, single variable average DBH, average tree height and stock volume growth model. Models fitted from Schumacher were more stable as the coefficient of variation was much lower than other alternative functions. S function was the best model in the fitting between stand density and age. (2) Comparing with the single variable average DBH growth model, fitting was improved a little after reparameterizing the model by introducing site index and stand density index variables. Stepwise regression improved the fitting significantly, the model with average tree height, density and age variables had best fitting without colinearity between variables. Repameterizing the average tree height growth model improved the fitting results a lot by introducing site index and density index variables. (3) Single age variable stock volume growth model had low coefficient of determination, but it was improved a lot by introducing site index and stand density index in reparameterization. Stand age was excluded in the stepwise regression modles, if raplace average tree height with stand age, the stepwise regression model that included age variable had higher coefficient of dertmination, but might have collinearity between variables when using a higher colinearity caritia. (4) Image analysis indicated that middle infrared was an important band for biomass estimation, and atmpspheric correction could improve fitting results of aboveground bimass model. Modified ratio vegetation index calculated from middle infrared and red band had highest correlation coefficient with aboveground biomass in pansharpened image, while modified NDVI derived from middle infrared and red band had highest coefficient in both radometrically corrected image. Comparing the fitting results of multiregression, stepwise regression and single variable nonlinear fitting, S biomass model was the most suitable model in all images. The final aboveground biomass S model was built from the atmospherically corrected image with modified NDVI as variable. Biomass estimation error of S model with surface reflectance image was evaluated, results shown estimation had high root of mean square error (RMSE), and quite small relative bias, which indicating that this method was suitable for mapping biomass spatial distribution on large scale. Finally, above ground biomass and carbon stock of Pinus yunnanensis were estimated with S model and surface reflectance image.
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云南松林分生长模型和生物量的遥感估测
学位论文
, 北京: 中国科学院研究生院, 2010
郎荣
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提交时间:2013/01/28