Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.

Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods.

Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative manner—in recent years. The availability of satellite data with very high revisiting time has gained momentum through the European Space Agency’s Sentinel missions, offering new application potential for Earth observation. This paper reviews the status of recent techniques such as terrestrial laserscanning, remote sensed imagery, and synthetic aperture radar in monitoring high mountain environments with a particular focus on the impact of new platforms such as Sentinel-1 and -2 as well as unmanned aerial vehicles. The study area comprises the high mountain glacial environment at the Pasterze Glacier, Austria. The area is characterized by a highly dynamic geomorphological evolution and by being subject to intensive scientific research as well as long-term monitoring. We primarily evaluate landform classification and process characterization for: (i) the proglacial lake; (ii) icebergs; (iii) the glacier river; (iv) valley-bottom processes; (v) slope processes; and (vi) rock wall processes. We focus on assessing the potential of every single method both in spatial and temporal resolution in characterizing different geomorphic processes. Examples of the individual techniques are evaluated qualitatively and quantitatively in the context of: (i) morphometric analysis; (ii) applicability in high alpine regions; and (iii) comparability of the methods among themselves. The final frame of this article includes considerations on scale dependent process detectability and characterization potentials of these Earth observation methods, along with strengths and limitations in applying these methods in high alpine regions.

This paper analyses the critical phase unwrapping step in a differential interferometric phase (D-InSAR) stack where both the solving of conventional methods and alternative approaches are discussed. It can be shown that including the temporal relationship between interferograms in the phase unwrapping step improves the results. This leads to the three-dimensional extended minimum cost flow algorithm. To unwrap the phase in a multitemporal way a motion model has to be considered. The estimation of these parameters is an important step. By default, the parameters are estimated in an iterative search process, where in each step, a linear program has to be solved. The best parameters are defined by the minimal costs. Often the choice of this search space is not straightforward. Furthermore, with this discrete optimization function, the solution is often not unique. This paper presents an alternative way to estimate the motion model parameters by maximizing a continuous function, the ensemble phase coherence. With the help of a closed-loop simulation and real data, both methods, the standard and the alternative way, are numerically compared and analyzed. Consequently, it is shown that maximizing the ensemble phase coherence is a good alternative to the established iterative procedure. It offers the advantage that the run time can be reduced considerably and is thus well suited in the processing of large data sets.

One of the most critical steps in a multitemporal D-InSAR analysis is the resolution of the phase ambiguities in the context of phase unwrapping. The Extended Minimum Cost Flow approach is one of the potential phase unwrapping algorithms used in the Small Baseline Subset analysis. In a first step, each phase gradient is unwrapped in time using a linear motion model and, in a second step, the spatial phase unwrapping is individually performed for each interferogram. Exploiting the temporal and spatial information is a proven method, but the two-step procedure is not optimal. In this paper, a method is presented which solves both the temporal and spatial phase unwrapping in one single step. This requires some modifications regarding the estimation of the motion model and the choice of the weights. Furthermore, the problem of temporal inconsistency of the data, which occurs with spatially filtered interferograms, must be considered. For this purpose, so called slack variables are inserted. To verify the method, both simulated and real data are used. The test region is the Lower-Rhine-Embayment in the southwest of North Rhine-Westphalia, a very rural region with noisy data. The studies show that the new approach leads to more consistent results, so that the deformation time series of the analyzed pixels can be improved.

We have used high resolution optical satellite stereo data to generate a digital surface model containing building heights, so that 3-dimensional information is available for the purpose of urban situational awareness. In addition we have applied change detection techniques to Radarsat fine beam imagery with a resolution of 10 meters to find infrastructural changes and to assess damages in order to obtain up-to-date infrastructural information. We show that the changes obtained from the radar data can be characterized using other data, such as high resolution optical imagery, maps, and data from GIS. Furthermore we show how these results can be combined with a digital surface model to produce an actual description of the urban area needed for urban situational awareness.