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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >In Search of the Statistical Properties of High-Resolution Polarimetric SAR Data for the Measurements of Forest Biomass Beyond the RCS Saturation Limits
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In Search of the Statistical Properties of High-Resolution Polarimetric SAR Data for the Measurements of Forest Biomass Beyond the RCS Saturation Limits

机译：寻找高分辨率极化SAR数据的统计特性，以测量超出RCS饱和极限的森林生物量

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摘要

The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the L-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and K-distributions, the K-distribution is found to fit best to the L-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the K-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the L-band RCS. Thus, the order parameter of the K-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method
机译：这封信的目的是介绍研究有效参数的结果，这些有效参数描述了高分辨率合成孔径雷达（SAR）图像与森林参数之间的关系。这项研究基于对日本北海道针叶林上的极化机载Pi-SAR数据进行的非高斯纹理分析。首先分析森林生物量方面的雷达截面（RCS）。发现L带RCS随生物量稳定增加并以约40吨/公顷饱和。这些结果与以前的研究相似。然后研究了图像振幅的概率密度函数，并且在瑞利，对数正态，威布尔和K分布中，发现了K分布最适合所有极化的L带数据，尽管威布尔分布同样适合。此外，发现交叉极化图像中树木生物量与K分布的有序参数之间的相关性非常高，并且有序参数随着生物量的增加而一致地增加到大约100吨/公顷，这远远超过了L波段RCS的饱和极限。因此，K分布的阶次参数可能是一个有前途的新参数，可以从高分辨率极化SAR数据中估计森林生物量，其范围比常规RCS方法大得多。

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