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基于ALOS遙感數(shù)據(jù)紋理及紋理指數(shù)的柞樹蓄積量估測
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北京市教育委員會省部共建項目(2009GJKY01)


Estimating Stand Volume of Xylosma racemosum Forest Based on Texture Parameters and Derivative Texture Indices of ALOS Imagery
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    摘要:

    以北京市懷柔區(qū)柞樹林為研究對象,,通過計算ALOS衛(wèi)星2.5m分辨率融合影像在不同窗口下的紋理特征及衍生紋理指數(shù),采用多元逐步回歸模型建立柞樹地面實(shí)測蓄積量與ALOS影像紋理特征及衍生紋理指數(shù)的相關(guān)關(guān)系,,比較紋理特征及衍生紋理指數(shù)擬合柞樹蓄積量模型的精度,,篩選最優(yōu)反演模型及最優(yōu)紋理生成窗口,。結(jié)果表明:同一紋理生成窗口下,基于衍生紋理指數(shù)的柞樹蓄積量反演模型(R2adj=0.603,、RMSE為19 899 4m3/hm2)精度優(yōu)于基于紋理特征的柞樹蓄積量反演模型(R2adj=0.217,、RMSE為27 943 8m3/hm2);結(jié)合同一窗口的紋理特征及衍生紋理指數(shù)進(jìn)行柞樹蓄積量建模,,精度可進(jìn)一步提升(R2adj=0.747,,RMSE為15 887 6m3/hm2);基于所有窗口的紋理特征及衍生紋理指數(shù)建立多元逐步回歸模型,,可得到柞樹蓄積量估測的最優(yōu)模型(R2adj=0.807,,RMSE為13 856 5m3/hm2),;11×11窗口為最優(yōu)紋理生成窗口,其對應(yīng)最優(yōu)單窗口模型擬合優(yōu)度為:R2adj=0.747,,RMSE為15 887 6m3/hm2,。

    Abstract:

    The Xylosma racemosum forest located in Huairou District of Beijing was chosen as research objects, texture parameters as well as derivative texture indices of different window sizes from ALOS fusion imagery with resolution of 2.5m were measured. Stepwise multiple regression models were developed to describe the relationship between textures (including texture parameters and derivative texture indices) and field measurements of stand volume. The main objective was to compare estimation accuracy between model established by texture parameters and that by derivative texture indices, select the most effective Xylosma racemosum stand volume estimate model and select the most effective window size. Results indicate that the value of adjusted R2 of fitting models established by derivative texture indices were better than those of texture parameters at the same window size, the value of adjusted R2 of stand volume model could be improved significantly by combination of texture parameters and derivative texture indices at the same window size, the optimal estimation model of Xylosma racemosum stand volume was obtained when all of the texture parameters and derivative texture indices of all window sizes were introduced into stepwise multiple regression, 11×11 was the optimal window size with the largest adjusted R2 for fitting Xylosma racemosum stand volume by texture parameters and derivative texture indices generated at one single window size.

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劉 俊,畢華興,朱沛林,孫 菁,朱金兆,陳 濤.基于ALOS遙感數(shù)據(jù)紋理及紋理指數(shù)的柞樹蓄積量估測[J].農(nóng)業(yè)機(jī)械學(xué)報,2014,45(7):245-254. Liu Jun, Bi Huaxing, Zhu Peilin, Sun Jing, Zhu Jinzhao, Chen Tao. Estimating Stand Volume of Xylosma racemosum Forest Based on Texture Parameters and Derivative Texture Indices of ALOS Imagery[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):245-254.

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  • 收稿日期:2014-03-07
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  • 在線發(fā)布日期: 2014-07-10
  • 出版日期: 2014-07-10
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