Surface network extraction from high resolution digital terrain models

IF 1.8 Q2 GEOGRAPHY Journal of Spatial Information Science Pub Date : 2021-06-30 DOI:10.5311/josis.2021.22.681
É. Guilbert
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引用次数: 1

Abstract

A surface network is a topological data structure formed by a set of thalwegs and ridges on a digital terrain model. Its computation relies on the detection of saddles on the terrain. Hence, computation methods must guarantee enough saddles are detected but also that no improper conflicts between ridges and thalwegs are created, leading to an inconsistent network. This paper presents a new approach that maximizes the number of saddles and ensures this topological consistency for high-resolution terrain models represented by a raster grid. The grid is triangulated in order to preserve saddles and to facilitate thalweg and ridge computation. It does not require any user parameter and lines remain aligned with triangulation edges, avoiding numerical errors. The method also includes a coherent partitioning of the terrain into hills and dales. A case study shows that the surface network computation can be achieved in reasonable time and hence can be applied to the analysis of large terrain models.
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基于高分辨率数字地形模型的地表网络提取
地表网络是由数字地形模型上的一组谷线和山脊形成的拓扑数据结构。其计算依赖于对地形上鞍座的检测。因此,计算方法必须确保检测到足够的鞍座,但也必须确保山脊和谷线之间不会产生不适当的冲突,从而导致网络不一致。本文提出了一种新的方法,该方法可以最大化鞍的数量,并确保由光栅网格表示的高分辨率地形模型的拓扑一致性。对网格进行三角测量,以保留鞍座并便于进行谷线和山脊计算。它不需要任何用户参数,直线与三角测量边保持对齐,避免了数值误差。该方法还包括将地形连贯地划分为山丘和山谷。实例研究表明,地表网络计算可以在合理的时间内实现,因此可以应用于大型地形模型的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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