Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California

Morris, Christopher M.; Welborn, Toby L.; Minear, J. T.

doi:10.3133/sir20205082

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second data set contains ground observations collected by terrestrial laser scanner surveys in Grapevine Canyon near Scotty's Castle, Death Valley National Park (Morris et al 2020). It updated flood-inundation maps that were affected by a large flood in 2015 to current conditions.…”

Section: Weighted Error Indicatorsmentioning

confidence: 99%

Error indicators and adaptive refinement of the discrete thin plate spline smoother

Fang¹

2019

ANZIAMJ

View full text Add to dashboard Cite

The discrete thin plate spline is a data fitting and smoothing technique for large datasets. Current research only uses uniform grids for this discrete smoother, which may require a fine grid to achieve a certain accuracy. This leads to a large system of equations and high computational costs. Adaptive refinement adapts the precision of the solution to reduce computational costs by refining only in sensitive regions. The error indicator is an essential part of the adaptive refinement as it identifies whether certain regions should be refined. Error indicators are well researched in the finite element method, but they might not work for the discrete smoother as data may be perturbed by noise and not uniformly distributed. Two error indicators are presented: one computes errors by solving an auxiliary problem and the other uses the bounds of the finite element error. Their performances are evaluated and compared with 2D model problems. References H. Chui and A. Rangarajan. A new point matching algorithm for non-rigid registration. Comput. Vis. Image Und., 89 (23): 114141, 2003. doi:10.1016/S1077-3142(03)00009-2. W. F. Mitchell. A comparison of adaptive refinement techniques for elliptic problems. ACM T. Math. Software, 15 (4): 326347, 1989. doi:10.1145/76909.76912. S. Roberts, M. Hegland, and I. Altas. Approximation of a thin plate spline smoother using continuous piecewise polynomial functions. SIAM J. Numer. Anal., 41(1):208234, 2003. doi:10.1137/S0036142901383296. G. Sewell. Analysis of a finite element method. Springer-Verlag, 1985. doi:10.1007/978-1-4684-6331-6. R. Sprengel, K. Rohr, and H. S. Stiehl. Thin-plate spline approximation for image registration. In P. IEEE EMBS, volume 3, pages 11901191. IEEE, 1996. doi:10.1109/IEMBS.1996.652767. L. Stals. Efficient solution techniques for a finite element thin plate spline formulation. J. Sci. Comput., 63(2):374409, 2015. doi:10.1007/s10915-014-9898-x. G. Wahba. Spline models for observational data, volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, 1990. doi:10.1137/1.9781611970128.

show abstract

Section: Weighted Error Indicatorsmentioning

confidence: 99%

Error indicators and adaptive refinement of the discrete thin plate spline smoother

Fang¹

2019

ANZIAMJ

View full text Add to dashboard Cite

show abstract

Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California

Abstract: For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov/.

Cited by 1 publication

References 19 publications

Error indicators and adaptive refinement of the discrete thin plate spline smoother

Error indicators and adaptive refinement of the discrete thin plate spline smoother

Contact Info

Product

Resources

About