Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management.

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.

The aim of this paper is to compare L- and P-band vertical backscattering profiles estimated by means of synthetic aperture radar (SAR) tomography and full light detection and ranging (lidar) waveforms in terms of their ability to distinguish different tropical forest structure types. The comparison relies on the unique DLR F-SAR and NASA Land, Vegetation and Ice Sensor (LVIS) lidar datasets acquired in 2016 in the frame of the AfriSAR campaign. In particular, F-SAR and LVIS data over three different test sites complemented by plot field measurements are used. First, the SAR and lidar three-dimensional (3-D) datasets are compared and discussed on a qualitative basis. The ability to penetrate into and through the canopy down to the ground is assessed at L- and P-band in terms of both the ground-to-volume power ratio and the performance to estimate the location of the underlying ground. The effect of polarimetry on the visibility of the ground is discussed as well. Finally, the 3-D measurements for each configuration are compared with respect to their ability to derive physical forest structure descriptors. For this, vertical structure indices derived from the volume-only 3-D radar reflectivity at L- and P-band and from the LVIS profiles are compared against each other as well as against plot-derived indices.

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

Model-based forest height inversion from Pol-InSAR data relies on the realistic parameterization of the underlying (vertical) radar reflectivity function. In the context of interferometric TanDEM-X measurements - especially in the global single pol DEM mode - this is not possible due to the limited dimensionality of the observation space. In order to overcome this, the use of lidar waveforms to directly approximate the TanDEM-X reflectivity is proposed. This allows the forest height estimation from a single, single polarimetric, bistatic TanDEM-X acquisition. In order to extend the proposed lidar-supported inversion schema to areas only partially covered or sampled by (waveform) lidar measurements, the use of a “mean” (vertical) reflectivity profile is further proposed. This “mean” reflectivity profile is defined by means of the eigenfunctions of the available set of lidar waveforms. Both approaches are demonstrated and validated using TanDEM-X and airborne waveform lidar data acquired in the framework of the AfriSAR 2016 campaign over the Lopé National Park, in Gabon.

Synthetic Aperture Radar Interferometry (InSAR) is able to provide important information for the characterization of the surface topography of glaciers and ice sheets. However, due to the inherent penetration of microwaves into dry snow, firn, and ice, InSAR elevation models are affected by a penetration bias. The fact that this bias depends on the snow and ice conditions as well as on the interferometric acquisition parameters complicates its assessment and makes it also relevant for measuring topographic changes. Recent studies indicated the potential for model based compensation of this penetration bias. This paper follows this approach and investigates the performance of two subsurface volume models for this task. Single-channel and polarimetric approaches are discussed for random and oriented volume scenarios. The model performance is assessed on two test sites in the percolation zone of the Greenland ice sheet using fully polarimetric airborne X-, C-, L, and P-band InSAR data. The results indicate that simple models are able to partially compensate the penetration bias and provide more accurate topographic information than the interferometric phase center measurements alone.