Karlheinz Gutjahr

Forest Assessment Using High Resolution SAR Data in X-Band

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 the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data

TerraSAR-X can acquire image data in various resolutions down to a range of about 1 m. Moreover, the sensor can operate at various imaging beams and thus acquire image data at different off-nadir viewing angles. These circumstances led to a stimulation of the traditional stereo-mapping approach, as TerraSAR-X image pairs became available in high resolution and in various geometric dispositions. With respect to 3-D surface mapping, TerraSAR-X stereo data processing, therefore, is a serious alternative to synthetic aperture radar interferometry, which can be addressed as the evolving mapping technique of the last decade. Within the TerraSAR-X science program of the German Aerospace Center (DLR), high-resolution multibeam data sets in Spotlight mode were acquired for several Austrian test sites. In general, three images were obtained from either ascending or descending orbits. In order to exploit the 3-D mapping accuracy of TerraSAR-X, stereo-radargrammetric mapping techniques were applied to the data sets, thereby utilizing stereo pairs as well as multi-image data sets in various dispositions. This paper focuses on one of the selected test sites and refers to the issues of 2-D and 3-D mapping-accuracy assessment as well as to surface model and vegetation-height-model generation. Validation of these products was widely restricted to visual analysis due to the lack of adequate high-quality reference products.

The Epipolarity Constraint in Stereo-Radargrammetric DEM Generation

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.

Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation

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.

Using worldwide available TerraSAR-X data to calibrate the geo-location accuracy of optical sensors

A method to calibrate the geo-location accuracy of optical sensors is presented which is based on a novel multi-modal image matching strategy. This concept enables to transfer points from highly accurate TerraSAR-X imagery to optical images. These points are then used to register the images or to update the optical sensor models. The potential of the methodology is demonstrated on Spot 5, Ikonos and RapidEye images.

TerraSAR-X value added image products

The space mission TerraSAR-X is the first German space project implemented under a Public Private Partnership (PPP). Cooperation partners are the German Aerospace Centre (DLR) and EADS Astrium GmbH. Within this construct, DLR will be responsible for the scientific use of the TerraSAR-X data, whereas commercial marketing will be undertaken exclusively by Infoterra GmbH, a wholly-owned EADS Astrium subsidiary. In a co-operation between Infoterra GmbH and Joanneum Research, Value Added products and processors have been developed for TerraSAR-X data. These products are mainly oriented at the area of interest or are mapping products which represent a higher level of image processing in terms of radiometric correction and orthorectification, mosaics, subsets and merges. In this paper, these products are described. Further, an insight into the automated and semi-automated production chain is provided.

Rapid Mapping and Damage Assessment

This chapter covers two main topics. The first deals with rapid mapping of damages for generating an overview, while the second deals with detailed assessment of damages. For the topic rapid mapping, fast procedures and methods for obtaining overview maps and information from satellite imagery are the main focus. For the second topic, production of accurate and detailed information from satellite imagery is more of an issue. The chapter is based on the activities of the various partners that have contributed to the GMOSS work package handling this subject. These activities comprise both security issues as well as natural disasters

DEM-based epipolar rectification for optimized radargrammetry

The quality of DEMs derived via radargrammetry mainly depends on the similarity of the SAR stereo images, taken under different look angles, in the image matching step. This work presents a novel pre-processing method that allows generating more similar epipolar images which are corrected by the underlying topography thus leading SAR specific distortion corrected stereo pairs. The evaluation w.r.t LiDAR data shows the increased quality of resulting DEMs.

Humid tropical forest monitoring with multi-temporal L-, C- and X-band SAR data

Humid tropical forest monitoring with EO is limited by frequent cloud cover and rapid forest regrowth. Both can be overcome by using temporally dense SAR image stacks. We present a method that uses the coefficient of variation of multi-temporal SAR data stacks to map tropical forest disturbances. The SAR data pre-processing and the forest change detection workflows are described and illustrated. The method is tested at a humid tropical forest site in the Republic of Congo. At this test site we use data from three different SAR sensors: ALOS PALSAR, Sentinel-1 and TerraSAR-X. The forest disturbance maps are validated by visual interpretation and compared to the Landsat based Humid Tropical Forest Disturbance Alerts available from Global Forest Watch. Change mapping accuracies for plots larger than 0.5 ha are very high: 76% for ALOS PALSAR, 96% for TerraSAR-X and 98% for Sentinel-1. For Sentinel-1, producer accuracies were derived for different forest disturbance types. The overall accuracy is 81.8%, with highest values for deforestation in oil palm plantations and burnt areas. The results are similar to the accuracy of the Humid Tropical Forest Disturbance Alert layer, which detects 85.6% of all reference areas. We also show that fusion of the disturbance maps on a result level is possible. The presented method could be adapted to near real-time processing and to a combined processing with optical EO data.

3D-mapping from TERRASAR-X staring spotlight data

Radargrammetry is a well-established technique for deriving digital surface models (DSMs) from synthetic aperture radar images. This work focuses on the novel Staring Spotlight mode of the TerraSAR-X satellite which delivers images with a GSD down to 16 cm. Employing such images, the 2D and 3D geo-location accuracy of the physical sensor models - integrated into SAR range/Doppler equations - is assessed and an automated workflow for DSM extraction is presented. An analysis of the mapping potential completes this study by evaluating the DSM accuracies w.r.t. reference LiDAR data.