Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations
Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations
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DOI：
10.5194/esurf-8-431-2020
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Richard Barnes;K. Callaghan;A. Wickert
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文献类型：
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作者：
Richard Barnes;K. Callaghan;A. Wickert
抽象的。数字高程模型的内倾区域--为地形分析和水文建模带来了困难。类似的“凹陷”也出现在图像处理和形态分割中，它们可能代表噪声、感兴趣的特征或两者兼而有之。在这里，我们提供了一个新的数据结构-抑郁症的层次结构-捕捉完整的拓扑和地形复杂性的凹陷在一个地区。我们将洼地视为网络，其方式类似于地表水的流动路径，在这个过程中，各个子洼地合并在一起形成元洼地，这个过程一直持续到它们开始向外排水。这种层次结构可用于选择性地填充或突破洼地或加速水文流动的动态模型。完整的、注释良好的开源代码和正确性测试可在GitHub和Zenodo上获得。
Abstract. Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.