Recent acceleration of rock glaciers is well recognized in the European Alps, but similar behavior is hardly documented elsewhere. Also, the controlling factors are not fully understood. Here we provide evidence for acceleration of a rock glacier complex in northern Norway, from 62 years of remote sensing data. Average annual horizontal velocity measured by aerial feature tracking increased from ~0.5 myr−1 (1954–1977) to ~3.6 myr−1 (2006–2014). Measured by satellite synthetic aperture radar offset‐tracking, averages increased from ~4.9 to ~9.8 myr−1 (2009–2016) and maximum velocities from ~12 to ~69 myr−1. Kinematic analysis reveals different spatial‐temporal trends in the upper and the lower parts of the rock glacier complex, suggesting progressive detachment of the faster front. We suggest that permafrost warming, topographic controls, and increased water access to deeper permafrost layers and internal shear zones can explain the kinematic behavior.

Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

Complex-valued nonstationary random processes have nonvanishing complementary second-order moment functions. In this paper, we propose generalized dual-frequency and time-frequency coherence functions for harmonizable processes. The proposed generalized spectral coherences are based on widely linear estimators, and they result in coherence measures that combine Hermitian and complementary moment functions. We show that for analytic processes, and more surprisingly also for real-valued processes, additional second-order information becomes available through the generalized coherences. We offer illuminating geometrical interpretations of the proposed coherences through Hilbert space inner product formulations. Finally, we extend the theory to generalized cross-coherences between pairs of harmonizable processes.

In this paper we propose a new method for estimating the Ambiguity Function (AF) of a random process with limited spreading support. The observed process is modelled as the aggregation of a non-stationary signal of interest and noise. As the AF has limited spreading, thresholding is a suitable estimation procedure. Some key stochastic properties of the Empirical Ambiguity Function are derived to obtain a suitable threshold. Based on a median absolute deviation estimator for the variance, we derive a suitable threshold, which forms the basis for our proposed estimator. The estimator is tested on both artificial and real signals, and our results demonstrate a remarkably high resolution and reduced variance.

The European Space Agency's (ESA) "SAR for REDD" project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C-band SAR can compensate for the L-band's deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4-5 percent points and 1-2 percent points for C-and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C-and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program's and JAXA's ALOS-1/2 based global FNF maps.

This paper proposes a new estimation procedure for the ambiguity function of a non-stationary time series. The stochastic properties of the empirical ambiguity function calculated from a single sample in time are derived. Different thresholding procedures are introduced for the estimation of the ambiguity function. Such estimation methods are suitable if the ambiguity function is only non-negligible in a limited region of the ambiguity plane. The thresholds of the procedures are formally derived for each point in the plane, and methods for the estimation of nuisance parameters that the thresholds depend on are proposed. The estimation method is tested on several signals, and reductions in mean square error when estimating the ambiguity function by factors of over a hundred are obtained. An estimator of the spread of the ambiguity function is proposed.

In this paper, we propose a method to monitor the extent of different facies on glaciers on Svalbard. The extent of the firn facies can be used as an indicator for trends in mass balance. We produce an averaged image from Envisat ASAR wide swath mode scenes for an entire winter season. The averaged image is segmented into three classes using a Gaussian Mixture Model. The method is able to detect the area covered by firn for years when the glacier has negative or zero mass balance, while quantitative results are difficult to achieve for years with positive mass balance.

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