首页> 外文会议>10th International Symposium on Spatial Data Handling, Jul 9-12, 2002, Ottawa, Canada >Quantifying Uncertainty of Digital Elevation Models Derived from Topographic Maps
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Quantifying Uncertainty of Digital Elevation Models Derived from Topographic Maps

机译:量化从地形图得出的数字高程模型的不确定性

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This paper explores a methodology for quantifying the uncertainty of DEMs created by digitising topographic maps. The origins of uncertainty in DEM production were identified and examined. The uncertainty of DEM data was quantified by computing a vector total of Root Mean Square Error (RMSE) from the source map, sampling and measurement errors, and the interpolation process. Distributional measures including accuracy surfaces, spatial autocorrelation indices, and variograms were also employed to quantify the magnitude and spatial pattern of the uncertainty. The test for this methodology utilises a portion of a 1:24 000 topographic map centred on Stone Mountain in northeastern Georgia, USA. Five DEMs, constructed with different interpolation algorithms, are found to have the total RMSE ranging from 4.39 to 9.82 meters, and a highly concentrated pattern of uncertainty in rugged terrain. This study suggests that the RMSE provides only a general indicator of DEM uncertainty. Detailed studies should use distributional measures to understand how the uncertainty varies over a surface.
机译:本文探索了一种量化通过数字化地形图创建的DEM不确定性的方法。确定并检查了DEM生产中不确定性的根源。 DEM数据的不确定性通过从源图,采样和测量误差以及插值过程计算矢量均方根误差(RMSE)的总和来量化。包括精度表面,空间自相关指数和变异函数在内的分布度量也用于量化不确定性的大小和空间模式。此方法的测试使用了以美国乔治亚州东北部的斯通山为中心的1:24 000地形图的一部分。发现五个采用不同插值算法构建的DEM的总RMSE范围为4.39至9.82米,并且在崎terrain不平的地形中高度集中了不确定性模式。这项研究表明,RMSE仅提供DEM不确定性的一般指标。详细的研究应使用分布测度来了解整个表面的不确定性如何变化。

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