The Chinese L-band twin-satellite SAR constellation, LuTan-1, that was launched in 2022 became the first spaceborne L-band bistatic InSAR mission. In this work, we will explore this valuable dataset of bistatic InSAR mode to estimate forest height. Since the majority of this mode only acquires single-polarization (HH-pol) data, we use the few-look InSAR phase histogram method developed by our previous work to estimate the digital terrain height (DTM) and InSAR phase center height simultaneously. Then, the HH-pol complex InSAR coherence measurements in combination with the DTM (or phase center height) are used to invert for forest total height using a physical model approach, namely the Random Volume over Ground (RVoG) model. Preliminary inversion results are shown and validated against spaceborne lidar (NASA’s GEDI and ICESat-2/ATLAS) and airborne lidar data over the tropical test site on the Hainan island of China.

This paper presents 30 m gridded mosaic of forest canopy height for the New England region of the United States, encompassing Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island, covering a total area of 18 million hectares. The forest height estimates were derived based on ALOS repeat-pass SAR interferometry (InSAR) observations (100 InSAR pairs) and a semi-empirical physical model [1]. sd Further efforts were devoted to automating this approach for generating large-scale forest height products and evaluate the performance of the products at different sites (e.g., flat, hilly area, etc). As validated against NASA’s LVIS airborne LiDAR, this approach presents an accuracy of 3–4 m (RMSE) over flat area and an accuracy of 4-5 m per sub-hectare pixel over hilly area on the order of sub-hectare pixel (0.81 ha). This approach demonstrates promising values in the context of combining low-frequency InSAR observations and LiDAR measurements from existing and future spaceborne missions.

This article evaluates the recently introduced phase histogram (PH) technique for estimating forest height and vertical structure using theoretical modeling and experimental synthetic aperture radar (SAR) data. The PH technique assigns each pixel in an SAR interferogram to a specific height bin based on the value of the corresponding interferometric phase, thus allowing for the estimation of the forest’s vertical structure by accumulating pixels magnitudes within a given spatial window. This approach is radically different from the one employed by SAR tomography (TomoSAR), which allows for direct imaging of the 3-D structure of the vegetation by jointly focusing on SAR data from multiple trajectories. Importantly, PHs can be built using as few as two images (a single interferogram), whereas TomoSAR is well-known to perform best when many images area available. Accordingly, the main question we intend to address in this article is to what extent and in which conditions single-baseline PHs can be used as a surrogate of TomoSAR (in the absence of multibaseline data). Experimental analyses are conducted using L-band tomographic SAR data from the ESA campaign TomoSense, flown in 2020 at Eifel Park in North West Germany. TomoSense data include 30 + 30 monostatic overpasses acquired along two opposite flight headings, and are complemented by airborne, terrestrial, and unmanned aerial vehicle (UAV) Lidar surveys. Lidar data are used to generate a forest canopy height model (CHM) and vertical profiles of leaf area density (LAD), taken as the main reference in the evaluation of PHs. Multibaseline tomographic data are produced and investigated to assess the actual sensitivity of radar data to forest structure at this site, as well as to provide indications about the performance of a radar instrument when multiple baselines are available. Experimental results indicate that the PH technique can only loosely approximate the vertical structure produced by TomoSAR. Still, it can produce a reasonably good estimate of forest height. In particular, TomoSAR and the PH technique are observed to have an average root mean square error (RMSE) with respect to Lidar estimate of 2.8 and 4.45 m in North-West heading data, and 1.84 and 5.46 m in South-East heading data, respectively. The observed results are interpreted in light of a simple physical model to characterize PHs depending on the number of scatterers within the SAR resolution cell, on which basis we derive analytical expressions to predict height dispersion in PHs. The proposed model indicates that the concept of PH is inherently based on the assumption of a single dominant scatterer within any single SAR resolution cell. If this is not the case, PHs produce an intrinsic dispersion that does not represent the actual vertical distribution of scatterers within the vegetation. Consistently, we conclude that the PH technique is inherently best suited for the analysis of high- or very-high resolution data, which suggests its use in the context of higher frequency SAR missions (e.g., Tandem-X) and when there are few acquisitions available.

This paper presents a promising fusion prototype for forest stand height inversion using L-band spaceborne repeat-pass SAR interferometry (InSAR) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR measurements. NASA’s GEDI mission provides sparsely but extensively distributed LiDAR measurements which could serve as ample calibration samples to improve the forest height estimates based on InSAR information and semi-empirical scattering model. Based on previous efforts, this paper further removed the assumptions that were made given by the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage fitting scheme. Making good use of local GEDI samples in this approach allows a finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion. This approach is validated at the Howland Forest in Maine, U.S. by using ALOS InSAR and GEDI LiDAR data, where the estimates achieve a RMSE of 3.8m at a sub-hectare spatial resolution (e.g., 0.81 ha). The above experimental results demonstrates a promising prototype towards a large-scale forest height mapping using existing and future spaceborne L-band InSAR missions (JAXA’s ALOS-1/2, China’s L-SAR, NASA-ISRO’s NISAR), as well as spaceborne LiDAR missions (e.g. NASA’s GEDI, JAXA’s MOLI and China’s TECIS).

The new ESA TomoSense campaign aims at investigating a temperate forest located at the Eifel natural park, north-west Germany by means of Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Multifrequency (P, L, C), tomographic SAR data were acquired by repeated flights at different heights. Particularly in L- and C-band surveys, two aircrafts were simultaneously flying operated in a single-pass bistatic interferometric configuration. Such dataset could motivate advanced SAR technologies, and support scientific applications for future spaceborne SAR missions. However, a direct tomographic reconstruction on TomoSense L-band data presented unwanted artifacts due to: i) uncertainties in provided navigational data, and ii) a potential presence of clock mismatches as no dedicated communication link was employed for time synchronization. A dedicated calibration approach is therefore developed to compensate above disturbances using natural scatterers. Experimental results indicate that our proposed calibration approach is able to remarkably enhance the interferometric and tomographic performances on TomoSense L-band data.

PIN inference attack leveraging keystroke-induced side-channel information poses a substantial threat to the security of people’s privacy and properties. Among various PIN inference attacks, video-assisted method provide more intuitive and robust side-channel information to infer PINs. But it usually requires there is no visual occlusion between the attacker and the victims or their hand gestures, making the attackers either easy to expose themselves or inapplicable to the scenarios such as ATM or POS terminals. In this paper, we present a novel and practical video-assisted PIN inference system, ArmSpy, which infers victim’s PIN by observing from behind the victims in a stealthy way. Specifically, ArmSpy explores the subtle keystroke-induced arm posture changes, including elbow bending angle changes and the spatial relationship between different arm joints, to infer the PIN entries. We develop the keystroke inference mechanism to detect the keystroke events and pinpoint the keystroke positions, and then accurately infer the PINs with the proposed inferred PIN coordination mechanism. Extensive experimental results demonstrate that ArmSpy can achieve over 67% average accuracy on inferring the PIN with 3 attempts and even over 80% for some victims, indicating the severity of the threat posed by ArmSpy.

The new ESA TomoSense campaign aims to explore the retrieval of biophysical quantities over forests for different acquisition geometries and radar parameters. Tomographic SAR acquisitions are currently being carried out using different wavelengths, both monostatic and bistatic systems and opposite views. This work presents the current advances in the analyses and calibration of the TomoSense data stacks to make them suited for scientific analyses. Airborne monostatic P-band acquisitions as received by MetaSensing presented artifacts connected to the acquisition geometry. Coherence and phase fluctuations were compensated thus obtaining clean tomographic reconstructions and a clear identification of the terrain level. Bistatic L-band data are expected to be available in short time as well.

Tomography SAR (TomoSAR) methods recover the 3D structure of targets by processing several SAR images simultaneously. Depending on the degree of approximation, recovering the vertical structure can amount to a 1D problem (processing a vector of pixels), a 2D problem (processing a matrix) or a 3D problem (processing the whole 3D stack at once). The computational burden decreases from the 3D to the 1D, but the constraints for a proper reconstruction are tighter. Hence, this paper discusses the limit and criterion for the feasibility of each method. The huge computational burden of TomoSAR 3D method is addressed by a fast implementation on GPUs. Theoretical analyses and our approach are demonstrated on simulated data, as well as on real data from the ESA AlpTo-moSAR campaign.