Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation

Jie Wan,Haiqiang Fu,Jun Hu,Peng Shen,Jianjun Zhu,Changcheng Wang

doi:10.3390/f12040444

Abstract

The key point of forest height and underlying topography inversion using synthetic aperture radar tomography (TomoSAR) depends on the accurate positioning of the phase centers of different scattering mechanisms. The traditional nonparametric spectrum analysis methods (such as beamforming and Capon) have limited vertical resolution and cannot accurately distinguish closely spaced scatterers. In addition, it is very difficult to accurately estimate the ground or canopy heights with single polarimetric SAR images because there is no guarantee that the vertical profile will generate two clear and separate peaks for all resolution cells. A polarimetric TomoSAR method based on SKP (sum of Kronecker products) decomposition and iterative maximum likelihood estimation is proposed in this paper. On the one hand, the iterative maximum likelihood TomoSAR method has a higher vertical resolution than that of the traditional methods. On the other hand, the separation of the canopy scattering mechanism and the ground scattering mechanism is conducive to the positioning of the phase centers. This method was applied to the inversion of forest height and underlying topography in a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana with six passes of polarimetric SAR images. The inversion accuracy of underlying topography of the proposed method was up to 1.489 m and the inversion accuracy of forest height was up to 1.765 m. Compared with the traditional polarimetric beamforming and polarimetric capon methods, the proposed method greatly improved the inversion accuracy of forest height and underlying topography.

Highlights

Forests are important parts of the global ecosystem and play a vital role in the global carbon and oxygen cycle
We proposed a Pol-TomoSAR method based on SKP decomposition and maximum likelihood estimation
The new method was used in the inversion of forest height and underlying topography over a tropical forest, and reliable results were obtained

Summary

Introduction

Forests are important parts of the global ecosystem and play a vital role in the global carbon and oxygen cycle. Long-wavelength synthetic aperture radar has strong penetrating power and can be used to analyze the vertical structure of the forests. Polarization interference synthetic aperture radar (PolInSAR) technology is one of the most important tools for forest vertical structure inversion [1,2,3]. Related studies have proposed a three-stage algorithm [4] and nonlinear iterative algorithm [5] based on the random volume over ground (RVoG) model [4]. They have been successfully applied to the inversion of forest height. The performances of PolInSAR methods rely on reasonable model assumptions for the forest, and the calculation of related parameters is complicated

Methods

Findings

Discussion

Conclusion