Digital terrain modeling with orthogonal polynomials

Florinsky, Igor V.; Pankratov, A. N.

doi:10.21469/22233792.1.12.01

Cited by 6 publications

(3 citation statements)

References 21 publications

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Section: Topography In Earth Systemsmentioning

confidence: 99%

“…With the success of Earth and environment systems with these scale-diversified processes, persistent demands exist for extending their utility to new and expanding scopes (Ringler et al, 2008;Tarolli, 2014;Wilson, 2012), as exemplified by lapse-rate-controlled functional plant distributions (Ke et al, 2012), orographic forcing imposed on oceanic and atmospheric dynamics (Nunalee et al, 2015;Brioude et al, 2012;Hughes et al, 2015), topographic dominated flood inundations (Bilskie et al, 2015;Hunter et al, 2007), and many other geomorphological (Wilson, 2012), soil (Florinsky and Pankratov, 2015), and ecological (Leempoel et al, 2015) examples from Earth systems. However, as numerical simulation systems evolved to incorporate broader scales and finer processes to produce more exact predictions (Ringler et al, 2011;Weller et al, 2016;Wilson, 2012;Zarzycki et al, 2014), how to accurately assimilate or transform the finePublished by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Topography In Earth Systemsmentioning

confidence: 99%

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A high-fidelity multiresolution digital elevation model for Earth systems

Duan¹,

Lin

Zhu³

et al. 2017

Geosci. Model Dev.

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Abstract. The impact of topography on Earth systems variability is well recognised. As numerical simulations evolved to incorporate broader scales and finer processes, accurately assimilating or transforming the topography to produce more exact land-atmosphere-ocean interactions, has proven to be quite challenging. Numerical schemes of Earth systems often use empirical parameterisation at sub-grid scale with downscaling to express topographic endogenous processes, or rely on insecure point interpolation to induce topographic forcing, which creates bias and input uncertainties. Digital elevation model (DEM) generalisation provides more sophisticated systematic topographic transformation, but existing methods are often difficult to be incorporated because of unwarranted grid quality. Meanwhile, approaches over discrete sets often employ heuristic approximation, which are generally not best performed. Based on DEM generalisation, this article proposes a high-fidelity multiresolution DEM with guaranteed grid quality for Earth systems. The generalised DEM surface is initially approximated as a triangulated irregular network (TIN) via selected feature points and possible input features. The TIN surface is then optimised through an energy-minimised centroidal Voronoi tessellation (CVT). By devising a robust discrete curvature as density function and exact geometry clipping as energy reference, the developed curvature CVT (cCVT) converges, the generalised surface evolves to a further approximation to the original DEM surface, and the points with the dual triangles become spatially equalised with the curvature distribution, exhibiting a quasi-uniform high-quality and adaptive variable resolution. The cCVT model was then evaluated on real lidar-derived DEM datasets and compared to the classical heuristic model. The experimental results show that the cCVT multiresolution model outperforms classical heuristic DEM generalisations in terms of both surface approximation precision and surface morphology retention.

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Section: Topography In Earth Systemsmentioning

confidence: 99%

Section: Topography In Earth Systemsmentioning

confidence: 99%

A high-fidelity multiresolution digital elevation model for Earth systems

Duan¹,

Lin

Zhu³

et al. 2017

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…Topography is one of the main factors controlling processes operating at or near the surface layer of the planet (Florinsky and Pankratov, 2015;Wilson and Gallant, 2000). With the success of Earth and environment systems with these scale diversified processes, there exist persistent demands for extending their utility to new and expanding scopes (Ringler et al, 2008;Tarolli, 2014;Wilson, 2012), as exemplified by lapse-rate controlled functional plant distributions (Ke et al, 2012), orographic forcing imposed on oceanic and atmospheric dynamics (Nunalee et al, 2015;Brioude et al, 2012;Hughes et al, 2015), topographic dominated flood inundations (Bilskie et al, 2015;Hunter et al, 2007), and many other geomorphological (Wilson, 2012), soil (Florinsky and Pankratov, 2015), and ecological (Leempoel et al, 2015) examples from Earth systems. However, as numerical simulation systems evolved to incorporate broader scales and finer processes to produce more exact predictions (Ringler et al, 2011;Weller et al, 2016;Wilson, 2012;Zarzycki et al, 2014), how to accurately assimilate or transform the fine resolution topography has proven to be a quite difficult task (Bilskie et al, 2015;Chen et al, 2015;Tarolli, 2014).…”

Section: Topography In Earth Systemsmentioning

confidence: 99%

A high-fidelity multiresolution DEM model for Earth systems

Duan

Zhu

et al. 2016

Preprint

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Abstract. The topographic impacts on modifying Earth systems variability have been well recognised. As numerical simulations evolved to incorporate broader scales and finer processes, accurately embedding the underlying topography to simulate land – atmosphere – ocean interactions, or performing commensurate scale transformation to topography while considering high-fidelity retention have proven to be quite difficult. Numerical schemes from Earth systems either use empirical parameterization as sub-grid scale and downscaling skills to express topographic endogenous processes, or rely on insecure point interpolation to induce topographic forcing, which create bias and input uncertainties. DEM generalisation provides systematic topographic transformation by considering loyal fidelity, but existing heuristic approaches are not performed optimally because of point clustering, or are difficult to incorporate into numerical systems because of sliver triangles. This article proposes a novel high-fidelity multiresolution DEM model with high-quality grids to meet the challenges of scale transformation. The generalized DEM model is initially approximated as a triangulated irregular network (TIN) via selected terrain feature points, control points, and possible embedded terrain features. The TIN surface is then optimized through an energy-minimized centroidal Voronoi tessellation (CVT). By devising a robust discrete curvature as a density function and exact geometry clipping as an energy reference, the developed curvature CVT (cCVT) converges, the generalized model evolves to a further approximation to the original DEM surface, and the points and their dual cells become equalized with the curvature distribution, exhibiting a quasi-uniform high-quality grid. The cCVT model is then evaluated on real LiDAR-derived DEM datasets compared to the classical heuristic method. The experimental results show that the cCVT multiresolution model outperforms classical heuristic DEM generalisations in terms of both surface approximation precision and surface morphology retention.

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Universal Analytical Modeling

Florinsky

2016

Digital Terrain Analysis in Soil Science and Geology

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Digital terrain modeling with orthogonal polynomials

Cited by 6 publications

References 21 publications

A high-fidelity multiresolution digital elevation model for Earth systems

A high-fidelity multiresolution digital elevation model for Earth systems

A high-fidelity multiresolution DEM model for Earth systems

Universal Analytical Modeling

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