针对地形粗糙度模型种类繁多、概念相近和模型混杂,以及难于针对具体研究样区恰当选取等问题,提出一种基于语义规则判别和剖面特征匹配的粗糙度模型评价算法。通过对面积比率模型、矢量粗糙度模型、表面粗糙度因子、基于标准差计算的统计模型等四类八种常用地形粗糙度模型的测试表明,该算法对粗糙度剖面的转折特征和局部地形变异特征敏感,能够反映地形粗糙度表面的局部变化和线性方向上的连续变化特征,适用于评价地形粗糙度。借助该算法,探讨了八种地形粗糙度模型的适用性,可为不同地形特征、数据源等条件下地形粗糙度模型的有效选择提供一种定量评价方法。
As an important terrain factor, surface roughness calculated by digital elevation models (DEM) is directly used in geoscience models such as soil erosion models, surface photo-thermal simulation and so on. But there has been no generally accepted definition of surface roughness until now. Various types and similar concepts of surface roughness may cause difficulty for selecting a suitable surface roughness model. In this paper, we present a new algorithm for assessing surface roughness models based on semantics and profile characteristics. Some contrast experiments are presented by choosing hybrid landform types as sample areas, using DEM produced by traditional topographic map digitizing and LiDAR technology. Main conclusions are drawn as follows: (1) The method we presented in this paper can accurately express the local variation of terrain profiles suitable for assessing surface roughness models. (2) Both triangular boundary effect of TIN model generated by traditional contour based DEM production and surface random noise in DEM generated by LiDAR technology based DEM production influence the calculation of surface roughness. DEM data should firstly be filtered and noise reduced beforehand. (3) An SAR model is insensitive to the flat relief. It is not suitable for areas of plain and valley plain dominated areas. Roughness models based on vector calculus are accurate in expressing ridge and valley lines and straight slope areas which is not effectively described by SAR models. SDev model is more suitable for application in fine scale DEM. Slope based standard deviation model can be applied to most types of terrain, which is sensitive to four semantic rules. But SDsp model is more sensitive to break of slope.Curvature based standard deviation models are not advantageous in surface roughness description except for their high sensitivity in surface roughness of flat relief areas.
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