The TanDEM-X DEM Mosaicking: Fusion of Multiple Acquisitions Using InSAR Quality Parameters

Gruber, Astrid; Wessel, Birgit; Martone, Michele; Roth, Achim

doi:10.1109/jstars.2015.2421879

Cited by 63 publications

(64 citation statements)

References 21 publications

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“…The residual phase trend is caused by small errors in the satellite positions, which result in baseline errors. Similar trends have been reported by other authors [6,15,16,21,49]. The error is easily observed in overlapping areas between adjacent swaths and can be corrected, to first order, by fitting an error model to the data.…”

Section: Scenes With Phase Trendssupporting

confidence: 88%

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

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This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%-25% (27-30 tons/ha and 52-65 m 3 /ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level.

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Section: Scenes With Phase Trendssupporting

confidence: 88%

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

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“…Gruber et al [12] fused interferometric synthetic aperture radar (InSAR) DEMs by the means of weighted average based on derived height errors. As the errors are higher at the borders, decreased weights towards the borders allow for smoother transitions between DEMs.…”

Section: Introductionmentioning

confidence: 99%

Fusion of high-resolution DEMs for water flow modeling

Petrášová

Mitášová

Petras

et al. 2017

Open geospatial data, softw. stand.

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Background: New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be updated at a low cost in selected study areas with newer, often higher resolution data using unmanned aerial systems (UAS) or terrestrial sensors. However, differences in spatial coverage and levels of detail often create discontinuities along the newly mapped area boundaries and subsequently lead to artifacts in results of DEM analyses or models of landscape processes. Methods: To generate a seamless updated DEM, we propose a generalized approach to DEM fusion with a smooth transition while preserving important topographic features. The transition is controlled by distance-based weighted averaging along the DEMs' blending overlap with spatially variable width based on elevation differences. Results: We demonstrate the method on two case studies exploring the effects of DEM fusion on water flow modeling in the context of precision agriculture. In the first case study, we update a lidar-based DEM with a fused set of two digital surface models (DSMs) derived from imagery acquired by UAS. In the second application, developed for a tangible geospatial interface, we fuse a georeferenced, physical sand model continuously scanned by a Kinect sensor with a lidar-based DEM of the surrounding watershed in order to computationally simulate and test methods for controlling storm water flow. Conclusions:The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.

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“…1. This figure shows the topography of Iceland and is generated by mosaicking TanDEM-X DEM tiles (Gruber et al, 2015). In addition, the lava field topographical changes are also investigated with two complementary TanDEM-X acquisitions over the Holuhraun plain.…”

Section: Introductionmentioning

confidence: 99%