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Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) based forest height estimation for ecosystem monitoring and management has been developing rapidly in recent years. Spaceborne Pol-InSAR systems with long temporal baselines of several days always lead to severe temporal decorrelation, which can cause a forest height overestimation error. However, most forest height estimation studies have not considered the change in dielectric property as a factor that may cause temporal decorrelation with a long temporal baseline. Therefore, it is necessary to propose a new method that considers dielectric fluctuations and random motions of scattering elements to compensate for the temporal decorrelation effect. The lack of ground truth for forest canopy also needs a solution. Unsupervised methods could be a solution because they do not require the use of true values of tree heights as the ground truth to calculate their estimation accuracies. This paper aims to present an unsupervised forest height estimation method called Dielectric Fluctuation and Random Motion over Ground (DF-RMoG) to improve accuracy by considering the dielectric fluctuations and random motions. Its performance is investigated using Advanced Land Observing Satellite (ALOS)-1 Pol-InSAR data acquired over a German forest site with temporal intervals of 46 and 92 days. The authors analyze the relationship between forest height and different parameters with DF-RMoG and conventional models. Compared with conventional models, the proposed DF-RMoG model significantly reduces the overestimation error due to temporal decorrelation in forest height estimation according to its lowest average forest height.

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor.

Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation. There is evidence suggesting that corticogeniculate connections may account for this decorrelation in the LGN. In this study, we propose a computational model based on biological evidence reported by Wang et al. (2006), who demonstrated that the influence pattern of V1 feedback is phase-reversed. The output of our model shows how corticogeniculate connections decorrelate LGN responses and make an efficient representation. We evaluated our model using criteria that have previously been tested on LGN neurons through cell recording experiments, including sparseness, entropy, power spectra, and information transfer. We also considered the role of the LGN in higher-order visual object processing, comparing the categorization performance of human subjects with a cortical object recognition model in the presence and absence of our LGN input-stage model. Our results show that the new model that considers the role of the LGN, more closely follows the categorization performance of human subjects.

The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA’s L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R2 = 0.75, RMSE = 5.3) as compared to 1 ha (R2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

In the framework of developments of 3D forest Tomography, the issue of extensive and detailed characterizations of temporal decorrelation phenomena has recently emerged, especially for the future spaceborne missions. In particular, height-varying (stratified) behaviour of long-term temporal decorrelation mechanisms has been analyzed by advanced 4D (3D+Time) Differential SAR Tomography processing applied to airborne data, and dedicated radar-tower campaigns have been conducted or are running. In this work, new Differential Tomography analyses exploiting a very quick acquisition ground-based miniradar are presented, aiming to open investigation of both height- and time-varying characteristics of the short-term decorrelation processes of the complex moving and non-stationary (dynamic) volumetric scatterers of windblown forests. This innovative characterization methodology and the first reported findings can be useful especially for the development of advanced spaceborne Tomography systems based on formation-flying and 3D correlative track-pair only processing.

This paper addresses the potential and limitations of polarimetric synthetic aperture radar (SAR) interferometry (Pol-InSAR) inversion techniques for quantitative forest-parameter estimation in tropical forests by making use of the unique data set acquired in the frame of the second Indonesian Airborne Radar Experiment (INDREX-II) campaign - including Pol-InSAR, light detection and ranging (LIDAR), and ground measurements - over typical Southeast Asia forest formations. The performance of Pol-InSAR inversion is not only assessed primarily at L- and P-band but also at higher frequencies, namely, X-band. critical performance parameters such as the ldquovisibility of the groundrdquo at L- and P-band as well as temporal decorrelation in short-time repeat-pass interferometry are discussed and quantitatively assessed. Inversion performance is validated against LIDAR and ground measurements over different test sites.

In this paper we present results from the ground-based ESA campaign TropiScat, aimed at evaluating the temporal coherence at P-band in a tropical forest in all polarizations and at different heights within the vegetation layers. The TropiScat equipment has been operated since October 2011 at the Paracou field station, French Guiana, to continuously produce height-range images of the forest below with a temporal sampling of 15 minutes. The forest temporal behaviour can then be captured by analyzing the interferometric coherence between images gathered at different times, considering time scales on the order of hours, days, and months. Temporal coherence at the ground level was found to be higher than 0.8 at 27 days in all polarimetric channels, whereas temporal coherence at canopy height was found to be about 0.8 at 4 days and about 0.65 at 27 days, witnessing coherence sensitivity to height.

Several studies showed that Pol-InSAR data especially at lower frequencies such as L- and P-band, are sensitive to the vertical distribution of scatterer along forest height. A model-based inversion of forest height and structural parameters was introduced and demonstrated. However, in repeat-pass airborne/spaceborne SAR system, temporal decorrelation is one of the most critical parameter for a successful Pol-InSAR forest parameter inversion. Temporal decorrelation is caused by the dynamic changes of the scatterers and/or the variation of the dielectric properties of the scatterers within the scene occurring in the time between the two SAR acquisitions. Due to its random character it is difficult to estimate and compensate temporal decorrelation contributions from the complex interferometric coherence. Temporal decorrelation lowers the estimated coherence and therefore also the estimated volume decorrelation which is directly related to the vertical distribution of scatterers. A biased (lowered) volume coherence leads to a biased forest parameter estimation in Pol-InSAR inversion. In this study, we assess the quantitative analysis of temporal decorrelation as a function of time using experimental data acquired by DLR’s E-SAR system. Then we evaluate the implementation of multi-baseline inversion technique for compensating temporal decorrelation. The behavior and compensation of temporal decorrelation will be demonstrated by means of three campaign data sets: BioSAR 2007 and TempoSAR 2008/2009.

Spaceborne SAR system concepts and mission design is often based on algorithms developed and the experience gathered from airborne SAR experiments and associated dedicated campaigns. However, airborne SAR systems have better performance parameters than their future space-borne counterparts as their design is not impacted by mass, power, and storage constraints. This paper describes a methodology to extrapolate spaceborne quality SAR image products from long wavelength airborne polarimetric SAR data which were acquired especially for the development and validation of bio/geo-retrieval algorithms in forested regions. For this purpose not only system (sensor) related parameters are altered, but also those relating to the propagation path (ionosphere) and to temporal decorrelation.

We consider branching random walks in $d ge 3$ with a Lipschitz branching rate function and show that the system, starting either in a Poisson field or in equilibrium, decorrelates over long time horizons, and employ this to obtain an ergodic theorem. We use coupling and a stochastic representation of the Palm distribution.

The potential of satellite borne interferometric measurements for the mapping of slow land subsidence has been investigated. Two test sites, covered by the ERS-1 satellite, were selected in the Netherlands: the provinces of Groningen and Zeeland. In situ measurements on the weather and the actual subsidence were gathered. It is shown that under favourable conditions measurement accuracies for land subsidence in the range of millimetres are feasible. Atmospheric effects together with temporal decorrelation are the major limitations to the accuracy of the technique for this type of long term measurement.