TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany

Forest Structure Characterization From SAR Tomography at L-Band

Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical structure of forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D forest monitoring. However, the link between 3-D radar reflectivity and 3-D forest structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical forest structure from tomographic SAR data at L-band. For this, forest structure is parameterized into a set of a horizontal and a vertical structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate forest site of Traunstein in Germany. The obtained horizontal and vertical structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the forest structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework

Observing the Forest from Lidar and Radar Remote Sensing

Lidar and radar are remote sensing systems that have proven their potential to accurately monitor forest structure characteristics. Both are active systems. They send their own pulses and measure the time elapsed between the transmission of the pulse and its reception after it has been reflected in a scatterer. However, Lidar employs laser light, while radar operates with microwaves, which allows the latter to operate under any weather condition. The viewing geometry is nadir and side looking for the Lidar and the radar respectively, among other differences. Mainly due to the availability of commercial systems, contrarily to radar, Lidar has been extensively employed by forest scientists and practitioners in the last decade. The analysis in parallel of both systems can help to introduce the use of radar data to the ecology community and to identify complementarities between them. This is particularly relevant in views of the forthcoming space missions, such as JEDI and Tandem-L

An evaluation of Pol-InSAR complementarities between L- and S-band in forest structure observation

There is a common agreement on the relevance of L-band wavelengths (around 20 cm) for forest observation. For instance, the larger backscatter dynamic range increases the sensitivity to larger biomass gradients with respect to C- and X-band. Furthermore, L-band allows the penetration into and through dense forest canopies in all forest ecosystems, remaining sensitive to canopy structure elements, and possibly leading to more relevant structure estimates than P-band. Finally, the larger temporal stability of the scatterers may allow the implementation of polarimetric interferometric (Pol-InSAR) acquisitions in repeat pass modes. In contrast, S-band applications in the forest domain are rather unexplored. The interest in S-band is being raised by its implementation on a number of space borne platforms, e.g. HJ-1C (China), NovaSAR-S (United Kingdom) and NISAR (United States and India). Clear potentials of S-band polarimetry have been found for instance for land classification, while S-band interferometry has been shown to lead to accurate estimates of digital elevation models for instance in a dual-frequency framework with X-band. Differently from L-band, the shorter wavelength allows the realization of flexible single-pass implementations with relevant interferometric sensitivity even on airborne platforms. Despite the first airborne experiments, the S-band performance in terms of forest structure characterization from polarimetric interferometric (Pol-InSAR) data has not been systematically evaluated yet. After the first S-band Pol-InSAR data acquisition, larger scale campaigns have been performed by the DLR’s F-SAR airborne platform on a number of test sites. In this context, the purpose of this work is to further investigate the complementarity of L- and S-band for forest structure observation as a consequence of the difference in penetration and sensitivity to sizes of vegetation elements, starting from multibaseline Pol-InSAR data. In particular, the role and importance of polarimetry is compared for the two frequencies. First of all, the spectrum of the ground-to-volume ratios is estimated under the Random-Volume-over-Ground assumptions, and its variability is evaluated as function of terrain slopes and incidence angle. Then, the distributions of the interferometric coherences within the Pol-InSAR coherence region are considered in different forest stands. Finally, the analysis is extended to vertical reflectivity profiles estimated by tomographic techniques. Experiments are carried out by using multibaseline Pol-InSAR data sets at L- and S-band acquired simultaneously by the F-SAR platform over the forest site of Traunstein (South of Germany) with (nominally) uniformly distributed baselines. In the S-band case, single-pass Pol-InSAR acquisitions are available as well, allowing to assess directly temporal decorrelation effects. Field inventory data collected continuously in space over 25 ha at single-tree level and fine-beam airborne lidar data are used as a reference

Definition of Tomographic SAR Configurations for Forest Structure Applications at L-Band

Synthetic aperture radar tomography (TomoSAR) at lower frequencies allows the reconstruction of the 3-D radar reflectivity of volume scatterers allowing access to their physical 3-D structure by means of multiangular SAR acquisitions. The performance of the reconstruction critically depends on the number and (spatial) distribution of the tomographic acquisitions (tracks). This dependence is addressed in this letter with respect to forest applications (volume scatters) at L-band. The letter discusses the optimum definition of tomographic configurations based on the peak sidelobe level (PSL) of the point spread function (PSF). For demonstration, a tomographic data set consisting of 15 acquisitions acquired by the DLR's F-SAR system at L-band over the Traunstein test site in Germany is used, complemented by airborne LiDAR measurements. Three different reconstruction algorithms (Fourier beamforming, Capon beamforming, and compressive sensing) are implemented and compared to each other for scenarios with a reduced number of acquisitions. Although the limitation of the specific forest, the results show the potential of using the PSL of the PSF to define tomographic configurations optimized for forest structure applications