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.

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ABSTRACT:This work proposes a simple filtering approach that can be applied to digital surface models in order to extract digital terrain models. The method focusses on robustness and computational efficiency and is in particular tailored to filter DSMs that are extracted from satellite stereo images. It represents an evolution of an existing DTM generation method and includes distinct advancement through the integration of multi-directional processing as well as slope dependent filtering, thus denoted "MSD filtering". The DTM generation workflow is fully automatic and requires no user interaction. Exemplary results are presented for a DSM generated from a Pléiades tri-stereo image data set. Qualitative and quantitative evaluations with respect to highly accurate reference LiDAR data confirm the effectiveness of the proposed algorithm.

For stereometric processing of optical image pairs, the concept of epipolar geometry is widely used. It helps to reduce the complexity of image matching, which can be seen to be the most crucial step within a workflow to generate digital elevation models. In this paper, it is shown that this concept is also applicable to the cocircular geometry of synthetic aperture radar (SAR) image pairs. First, it is proven that, for any feasible SAR acquisition, the deviation from true epipolar geometry is within subpixel range and therefore acceptably small. Based on this, we propose a method to create "epipolar" geometry for arbitrary stereo configurations of any SAR sensor through appropriate geometric image transformations. Consequently, the semiglobal matching (SGM) algorithm can be applied, which is restricted to epipolar geometry and is thus known to be highly efficient. This innovative approach, integrating both epipolar transformation and SGM, has been applied to a TerraSAR-X stereo data set. Its benefit has been demonstrated in a comparative assessment with respect to results, which have been previously achieved on the same test data using state-of-the-art stereometric methods.

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.