Abstract
Abstract
The paper describes the method of reducing the errors, primarily caused by vegetation, in open digital models like Aster and SRTM. These models do not usually meet the requirements of hydrological correctness due to the errors of different origin. The main errors of DEM are the distortion of the tilt angles and the absolute altitude component in the areas occupied by forest vegetation. We propose the method for eliminating vegetation cover by a local interpolation function and Lagrange polynomial interpolation. The paper also considers the comparison of the proposed method with other methods such as Triangulation Irregular Network (TIN), Inverse Distance Weighting (IDW), Natural Neighbor, Kriging, Radial Basis Functions.
Subject
General Physics and Astronomy
Reference21 articles.
1. EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data;Robinson;ISPRS J. Photogram. Remote Sens,2014
2. Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia;Elkhrachy;Ain Shams Engineering Journal,2018
3. Investigating two-dimensional, finite element predictions of floodplain inundation using fractal generated topography;Bates;Hydrological Processes,1998
4. Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic Modeling in large, low-gradient and data-sparse catchments;Jarihani;Journal of Hydrology,2015
5. A total error-based multiquadric method for surface modeling of digital elevation models;Chen;GIScience & Remote Sensing,2016
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Seepage Reconstruction of Large Civil Engineering Structures Based on Depth Learning;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28