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Forest remote sensing: Inversion using a coherent scattering model and a genetic algorithm

机译:森林遥感:使用相干散射模型和遗传算法进行反演

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In remote sensing for forest monitoring, Synthetic Aperture Radar (SAR) imaging is a powerful tool to investigate wide forested areas, particularly at low frequencies (less than 1GHz) where the electromagnetic waves penetrate deeply under the forest canopy. Radar measurements allow us to obtain information on the forest biomass, such as density, heights and dielectric properties of trees. In order to extract this information, it is necessary to analyze the wave-tree interactions involved when SAR measurements are performed. At low frequencies, the small branches and leaves of trees do not significantly contribute to the electromagnetic fields scattered by a forest area and can be safely neglected. The main contributions to the scattered field come from the trunks and primary branches. In this context, we have built up an approximate model where the latter are considered as finite circular cylinders. The total scattered field is then obtained by coherently adding the contributions of the different trunks and branches and the contribution of each of them is obtained through the coherent sum of various elementary scattering mechanisms such as single, double and triple bounces.
机译:在用于森林监测的遥感中,合成孔径雷达(SAR)成像是研究宽阔森林区域的强大工具,尤其是在低频(小于1GHz)的电磁波深入森林冠层下方的情况下。雷达测量使我们能够获取有关森林生物量的信息,例如树木的密度,高度和介电特性。为了提取此信息,有必要分析执行SAR测量时涉及的波树相互作用。在低频下,树木的小树枝和树叶不会显着增加森林区域散布的电磁场,因此可以安全地忽略。对散布场的主要贡献来自树干和主要分支。在这种情况下,我们建立了一个近似模型,其中后者被视为有限圆柱体。然后,通过将不同主干和分支的贡献相干地相加来获得总散射场,并且通过各种基本散射机制(如单次,两次和三次反弹)的相干总和来获得每个主干和分支的贡献。

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