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Forest Assessment Using High Resolution SAR Data in X-Band
Research Group on Remote Sensing and Geoinformation, Institute for Information and Communication Technologies, Joanneum Research, Wastiangasse 6, 8010 Graz, Austria
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Received: 18 January 2011; in revised form: 25 February 2011 / Accepted: 14 March 2011 / Published: 13 April 2011
Abstract: 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.
Keywords: SAR; high resolution; X-band; forestry; mapping; radargrammetry; classification; DSM/DTM
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Cite This Article
MDPI and ACS Style
Perko, R.; Raggam, H.; Deutscher, J.; Gutjahr, K.; Schardt, M. Forest Assessment Using High Resolution SAR Data in X-Band. Remote Sens. 2011, 3, 792-815.
Perko R, Raggam H, Deutscher J, Gutjahr K, Schardt M. Forest Assessment Using High Resolution SAR Data in X-Band. Remote Sensing. 2011; 3(4):792-815.
Perko, Roland; Raggam, Hannes; Deutscher, Janik; Gutjahr, Karlheinz; Schardt, Mathias. 2011. "Forest Assessment Using High Resolution SAR Data in X-Band." Remote Sens. 3, no. 4: 792-815.