The geomorphological characterisation of Digital Elevation Models
The geomorphological characterisation of Digital Elevation Models
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发表时间：
1996
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通讯作者：
J. Wood
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作者：
J. Wood
技术和问题被认为是周围的数字高程模型（DEM）所代表的表面形式的表征。开发了一套适用于基于栅格的地理信息系统（GIS）的软件工具。表征有三个具体目标，即识别空间模式，识别形式上的尺度依赖性，并允许结果可视化。通过确定适当的定量措施和可视化过程，可以在GIS中启用的DEM中的错误的特性的评估。这些都是通过轮廓线程分形表面和比较四个不同的空间插值的轮廓进行评估。最有效的错误特征被发现是那些识别高频空间模式。DEM误差的空间排列的可视化是用来开发一个确定性的误差模型的基础上，当地的表面坡度和方面。DEM的参数化使用一阶和二阶导数的二次曲面拟合在一定范围内的规模。这提供了优于传统方法的基础上3 × 3的本地窗口，因为地貌形式可以在任何规模的特点。形态学参数相结合，给出一个功能分类，也可以应用在一个范围内的尺度。多尺度测量相结合，给出一个功能的隶属函数，描述了如何属性随规模的变化。这些功能可视化使用模态和熵的可变性措施。可视化规模依赖性的另一种方法，建议以图形方式表示在各种空间滞后的空间格局的统计措施。这被发现最适合用于检测表面中的结构各向异性。通过将其应用于湖区、峰区和达特穆尔的不相关表面、分形表面和地形测量DEM来评估表征工具。
Techniques and issues are considered surrounding the characterisation of surface form represented by Digital Elevation Models (DEMs). A set of software tools suitable for use in a raster based Geographical Information System (GIS) is developed. Characterisation has three specific objectives, namely to identify spatial pattern, to identify scale dependency in form and to allow visualisation of results. An assessment is made of the characteristics of error in DEMs by identifying suitable quantitative measures and visualisation processes that may be enabled within a GIS. These are evaluated by contour threading a fractal surface and comparing four different spatial interpolations of the contours. The most effective error characterisations are found to be those that identify high frequency spatial pattern. Visualisation of spatial arrangement of DEM error is used to develop a deterministic error model based on local surface slope and aspect. DEMs are parameterised using first and second derivatives of quadratic surfaces fitted over a range of scales. This offers advantages over traditional methods based on a 3 by 3 local window, as geomorphometric form can be characterised at any scale. Morphometric parameters are combined to give a feature classification that may also be applied over a range of scales. Multi-scale measurements are combined to give a feature membership function that describes how properties change with scale. These functions are visualised using modal and entropy measures of variability. An additional method of visualising scale dependency is suggested that graphically represents statistical measures of spatial pattern over a variety of spatial lags. This is found most appropriate for detecting structural anisotropy in a surface. Characterisation tools are evaluated by applying them to uncorrelated surfaces, fractal surfaces and Ordnance Survey DEMs of Lake District, Peak District and Dartmoor.