Abstract
Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.
Highlights
The biophysical parameters of forest, such as the underlying topography and forest height, are important when quantifying the above-ground biomass (AGB) [1,2,3,4]
Tomography (TomoSAR) technique is able to achieve three-dimensional imaging, it can provide the vertical structure of the observed scene, allowing us to recognize these scattering phase centers
The idea behind the concept of TomoSAR is that it forms an additional aperture along the vertical direction by combining multi-baseline synthetic aperture radar (SAR) acquisitions [5,6]
Summary
The biophysical parameters of forest, such as the underlying topography and forest height, are important when quantifying the above-ground biomass (AGB) [1,2,3,4]. The retrieval of the underlying topography and forest height with the SAR technique involves the vertical location detection of the ground and canopy scattering phase centers. The idea behind the concept of TomoSAR is that it forms an additional aperture along the vertical direction by combining multi-baseline SAR acquisitions [5,6]. This makes it possible for TomoSAR to achieve the vertical resolution and discriminate the different scatterers within one resolution cell. TomoSAR is regarded as a viable tool for underlying topography and forest height estimation
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