森林地上生物量的极化干涉SAR相干层析估测方法

发布时间：2018-08-25 12:17

【摘要】：应用机载单基线极化干涉SAR(Pol-InSAR)数据,基于极化相干层析(PCT)技术提出了一种反演森林地上生物量的新方法。首先采用单基线PCT提取每个像元的森林相对反射率垂直分布,然后按林分统计得到森林平均相对反射率垂直分布;再次对森林平均相对反射率垂直分布进行高斯函数拟合,提取林分层析测量高;最后以通过样地调查统计得到的20个林分的地上生物量为参考数据,采用交叉验证方法建立和评价基于层析测量树高的地上生物量估测模型,并与基于经典三阶段反演的林分优势木平均树高估测地上生物量的方法进行对比。结果表明:基于层析测量高的反演模型决定系数(R2)为0.822,均方根误差(RMSE)为53.14 t·hm-2,比基于经典三阶段反演算法的林分地上生物量估测方法具有更高的估测精度。该反演方法简单易行,能够有效提高森林地上生物量估测精度,在该研究区未出现信号饱和现象。
[Abstract]:A new method for retrieving forest aboveground biomass based on polarimetric coherence tomography (PCT) is proposed based on airborne single baseline polarimetric interference SAR (Pol-InSAR) data. First, the vertical distribution of forest relative reflectance of each pixel was extracted by single baseline PCT, and then the vertical distribution of forest average relative reflectance was obtained by stand statistics, and then the vertical distribution of forest average relative reflectance was fitted by Gao Si function. In the end, the above ground biomass of 20 stands obtained from the survey of sample plots was used as reference data to establish and evaluate the above ground biomass estimation model based on the cross validation method. The method of estimating aboveground biomass was compared with the average tree height of dominant stand based on the classical three-stage inversion. The results show that the inversion model determination coefficient (R2) is 0.822 and the root mean square error (RMSE) is 53.14 t / hm-2, which is more accurate than the stand aboveground biomass estimation method based on the classical three-stage inversion algorithm. The inversion method is simple and feasible and can effectively improve the estimation accuracy of forest aboveground biomass without signal saturation in the study area.
【作者单位】：中国林业科学研究院资源信息研究所
【基金】：国家973计划项目(2013CB733404) 863计划重点项目(2011AA120402)
【分类号】：S718.5;TN958

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