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Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications., Urban Data Science, Mathematical Geodesy and Positioning

This study aims to identify and understand the annual scale sediment transport patterns in a fully engineered dune-dune slack system that was created from seabed sediments for grey dune and moist dune slack habitat creation at the Delfland coast (the Netherlands). The annual morphological development was analysed using LiDAR elevation data. Daily aeolian sediment transport was simulated across the foredune and in the adjacent dune slack, using the numerical aeolian sediment transport model AeoLiS. All simulated transport events were combined to reveal how aeolian sediment transport behaves on an annual scale. Planting two continuous strips of marram grass along the top of the foredune induced rapid growth of the foredune, while limiting sediment supply to the dune slack. The armouring layer of shells that formed at the surface of the engineered topography, functioned as a supply-limiting factor and restricted the sediment entrainment and deflation of the dune slack. Therefore, the formation of a moist dune slack habitat may take longer for this engineered case than in a natural case with similar boundary conditions.

River ice is a major contributor to flood risk in cold regions due to the physical impediment of flow caused by ice jamming. Although a variety of classifiers have been developed to distinguish ice types using HH or VV intensity of SAR data, mostly based on data from RADARSAT-1 and -2, these classifiers still experience problems with breakup classification, because meltwater development causes overlap in co-polarization backscatter intensities of open water and sheet ice pixels. In this study, we develop a Random Forest classifier based on multiple features of Sentinel-1 data for three main classes generally present during breakup: rubble ice, sheet ice and open water, in a case study over the Athabasca River in Canada. For each ice stage, intensity of the VV and VH backscatter, pseudo-polarimetric decomposition parameters and Grey Level Co-occurrence Matrix texture features were computed for 70 verified sample areas. Several classifiers were developed, based on i) solely intensity features or on ii) a combination of intensity, pseudo-polarimetric and texture features and each classifier was evaluated based on Recursive Feature Elimination with Cross-Validation and pair-wise correlation of the studied features. Results show improved classifier performance when including GLCM mean of VV intensity, and VH intensity features instead of the conventional classifier based solely on intensity. This highlights the complementary nature of texture and intensity for the classification of breaking river ice. GLCM mean incorporates spatial patterns of the co-polarized intensity and sensitivity to context, while VH intensity introduces cross-polarized surface and volume scattering signals and is less sensitive to wind than the commonly used co-polarized intensity. We conclude that the proposed method based on the combination of texture and intensity features is suitable for and performs well in physically complex situations such as breakup, which are hard to classify otherwise. T, Physical and Space Geodesy, Mathematical Geodesy and Positioning

River ice is a major contributor to flood risk in cold regions due to the physical impediment of flow caused by ice jamming. Although a variety of classifiers have been developed to distinguish ice types using HH or VV intensity of SAR data, mostly based on data from RADARSAT-1 and -2, these classifiers still experience problems with breakup classification, because meltwater development causes overlap in co-polarization backscatter intensities of open water and sheet ice pixels. In this study, we develop a Random Forest classifier based on multiple features of Sentinel-1 data for three main classes generally present during breakup: rubble ice, sheet ice and open water, in a case study over the Athabasca River in Canada. For each ice stage, intensity of the VV and VH backscatter, pseudo-polarimetric decomposition parameters and Grey Level Co-occurrence Matrix texture features were computed for 70 verified sample areas. Several classifiers were developed, based on i) solely intensity features or on ii) a combination of intensity, pseudo-polarimetric and texture features and each classifier was evaluated based on Recursive Feature Elimination with Cross-Validation and pair-wise correlation of the studied features. Results show improved classifier performance when including GLCM mean of VV intensity, and VH intensity features instead of the conventional classifier based solely on intensity. This highlights the complementary nature of texture and intensity for the classification of breaking river ice. GLCM mean incorporates spatial patterns of the co-polarized intensity and sensitivity to context, while VH intensity introduces cross-polarized surface and volume scattering signals and is less sensitive to wind than the commonly used co-polarized intensity. We conclude that the proposed method based on the combination of texture and intensity features is suitable for and performs well in physically complex situations such as breakup, which are hard to classify otherwise. T, Physical and Space Geodesy, Mathematical Geodesy and Positioning

This study aims to identify and understand the annual scale sediment transport patterns in a fully engineered dune-dune slack system that was created from seabed sediments for grey dune and moist dune slack habitat creation at the Delfland coast (the Netherlands). The annual morphological development was analysed using LiDAR elevation data. Daily aeolian sediment transport was simulated across the foredune and in the adjacent dune slack, using the numerical aeolian sediment transport model AeoLiS. All simulated transport events were combined to reveal how aeolian sediment transport behaves on an annual scale. Planting two continuous strips of marram grass along the top of the foredune induced rapid growth of the foredune, while limiting sediment supply to the dune slack. The armouring layer of shells that formed at the surface of the engineered topography, functioned as a supply-limiting factor and restricted the sediment entrainment and deflation of the dune slack. Therefore, the formation of a moist dune slack habitat may take longer for this engineered case than in a natural case with similar boundary conditions.