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 research adapts an effective change detection approach originally applied to mapping fire scar from a stationary synthetic aperture radar (SAR) scene to the problem of oil spills from SAR data. The method presented here combines several advanced image processing techniques to mitigate some of the common performance limitations of SAR-based oil spill detection. Principally among these limitations are the following. First, the radar cross section of the areas affected by an oil spill strongly depends on wind and wave effects, and is, therefore, highly variable. Second, the radar cross section of oil covered water is often indistinguishable from other dark ocean features such as low wind areas, which leads to errors and uncertainties in oil spill detection. In this paper, we introduce a multi-image analysis, the Lipschitz regularity (LR), and wavelet transforms as a combined approach to mitigate these performance limitations. We show that the LR parameter is much less sensitive to variations of wind and waves in an oil spill SAR imagery, lending itself well for normalizing and suppressing ocean background using image ratio processing. We combine LR processing with a multiscale technique based on the wavelet transform to additionally achieve high-quality noise suppression. To describe the performance of this approach under controlled conditions, we applied our method to simulated SAR data of wind-driven oceans with oil spills and low wind areas. We also applied our method to several real-world oil spill data acquired during the 2010 Deep Water Horizon spill in the Gulf of Mexico.

This paper aims at introducing the recent theory of compressive sensing to radar imaging systems in order to retrieve the imaged scene with better resolution and a reduced amount of collected samples. As a result of the application of the alternative imaging technique proposed, the use of matched filtering is avoided and the effect of its sidelobes in the images is drastically diminished. Furthermore, the amount of data to be stacked in the sensor and then downlinked to the ground station is meaningfully lower. This permits a more efficient management of resources.