多基线POLINSAR植被高度与植被下地形估计
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摘要
作为地球生态系统的重要组成部分,植被对全球物产估计、碳循环评估等方面具有极其重要的意义。然而常规的植物学研究方法很难在全球尺度下获取植被结构信息。作为主动传感器的代表,合成孔径雷达(SAR)能够提供全球实时观测的能力。而更为高级的干涉合成孔径雷达(INSAR)与极化干涉合成孔径雷达(POLINSAR)也在植被研究方面展现出了较强潜力。
     在雷达理论中,植被垂直结构函数(VVSF)定义为以垂直高度为自变量,对应高度复散射系数为因变量的复杂函数。当前的VVSF估计方法可以分为三大类:VVSF参数化法、Fourier-Legendre法与层析法(TSAR)。以Random Volume over Ground(RVoG)与Oriented Volume over Ground(OVoG)模型为代表的参数化法采用带少量参数的经验函数近似VVSF,并利用POLINSAR数据进行求解;而Fourier-Legendre法在POLINSAR系统的基础上采用Fourier-Legendre多项式近似逼近VVSF;层析法则需要利用数十次重轨观测SAR影像对VVSF进行严格的数学估计。为了减少求解所需的影像数量,本文针对VVSF的植被高度与植被下地形两个特定参数进行求解。本文的两个主要贡献如下:
     (1)本文改进了常规的多基线RVoG模型,并提出了对应求解方案进行植被高度与植被下地形起伏信息估计。考虑到多基线观测条件下风引起的枝叶摇曳过程,本文在多基线改进RVoG模型中证明了时间去相干影响的不可忽视性;同时,本研究在改进多基线RVoG时引入了树桩引起的植被层干涉相位偏移,使得改进模型更符合真实植被散射过程。在模型求解方面,本研究利用多基线POLINSAR的信息互补性,解决了植被下地表干涉相位求解模糊问题与树高-消光系数求解模糊问题。此外,本研究还利用真实实验数据评价了基线对森林高度与植被下地形提取精度的影响,并进行了相关扩展讨论。
     (2)从信息利用的角度来说,多基线RVoG模型树高与植被下地形提取主要依赖POLINSAR数据中的干涉信息,而蕴含在POLINSAR极化信息内的极化方位角(POA)则与地形变化息息相关。本研究提出了一种顾及地形起伏的自适应植被-地表模型。虽然植被-地表总体散射过程较为复杂,但多基线POLINSAR数据提供了求解自适应植被-地面联合模型的可能性。为了简化模型的求解过程,本文采用极化散射矩阵熵近似估计Arii体散射随机参数的可行解范围,然后提出了一种新的目标优化函数来求解植被-地面模型的参数。
As part of the earth ecosystem, the vegetation is of fundamental importance to agricultural production and the carbon cycle. It is, however, difficult, at a global scale, to obtain forest areas and to analyze the vertical structure of vegetation using conventional botanical methods. As an active remote sensing approach, the synthetic aperture radar (SAR) provides global observation ability all the time. The subsequent advanced interferometric and polarimetric techniques (INSAR and POLINSAR) show a potential capability to vegetation researches.
     In radar theory, the intricate vegetation vertical structure function (VVSF) gives an inherent relation between the radar complex backscattering coefficient and the special vertical altitude. The current VVSF algorithms can be partitioned into three categories, which contain VVSF parameterization, Fourier-Legendre expansion and tomography, to solve the dozens of unknowns. The parameterization method, such Random Volume over Ground (RVoG) and Oriented Volume over Ground (OVoG) models, utilizes the empirical exponent VVSF expression to simplify the solution process by POLINSAR images. The Fourier-Legendre method uses the Fourier-Legendre polynomial series to approximate the original VVSF on POLINSAR systems. Furthermore, the tomography method (TSAR) estimates the strict VVSF directly by tens of repeating-pass INSAR images. To reduce the images number, we try to solve the special unknowns of VVSF, the vegetation height and underlying ground altitude, but not VVSF itself. In this dissertation, two major contributions are as follows:
     (1) The original VVSF parameterization method, RVoG model, is extended into multi-baseline observed condition. The wind-driven canopy flicker and the vertical canopy fill factor are considered in the forest interferometric process. Furthermore, the multi-baseline algorithm is utilized to solve the RVoG underlying ground phase and height-extinction ambiguous problems. In the real data experimental part, the RVoG model and the baseline influence are also evaluated by multi-baseline POLINSAR data.
     (2) Technically speaking, the RVoG model only uses the polarimetric information to obtain the vertical location of certain backscattering mechanisms. Actually, the underlying ground caused polarimetric orientation angle (POA) can also be extract by sophisticated polarimetric backscattering model. In this dissertation case, the novel adaptive ground scattering model is proposed to approximate the underlying mechanism. Combining with the Arii vegetation volume scattering model, the multi-baseline POLINSAR set is utilize to solve the adaptive model. To simplify the estimation procedure, the coherence matrix entropy is employed to estimate the solution rangeing of Arii volume randomness parameter. Then, a new objective optimum function is proposed to solve the unknowns.
引文
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