Dostalova, A., Navacchi, C., Greimeister-Pfeil, I., Small, D., & Wagner, W. (2022). The effects of radiometric terrain flattening on SAR-based forest mapping and classification. Remote Sensing Letters, 13(9), 855–864. https://doi.org/10.1080/2150704X.2022.2092911
Terrain-induced variations of radar backscatter represent an important limiting factor of many Synthetic Aperture Radar (SAR)-based applications. Radiometric terrain flattening (RTF) is a well-established method that minimizes these variations in SAR imagery. To fully understand the implications of SAR RTF, validation of its impact on the derived products is needed. In this study, we quantified the influence of the RTF on a forest mapping and classification algorithm over Austria, and compared the classification results for the conventional sigma naught and radiometrically terrain-corrected gamma backscatter. The overall accuracy for forest/non-forest mapping and forest type classification improved by 2% and 4%, respectively, over the whole of Austria, with improvements of up to 16% and 20%, respectively, in regions with strong topography.
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Project title:
Austrian Data Cube: An EODC service for the Austrian EO user community: 865999 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) Feasibility of using Sentinel-1 terrain-flattened gamma naught backscatter across EO platforms (ESA-ESRIN)
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Research Areas:
Environmental Monitoring and Climate Adaptation: 100%
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Science Branch:
1059 - Sonstige und interdisziplinäre Geowissenschaften: 100%
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