• 地球信息科学 •

### 顾及数据特性的格网DEM分辨率计算

1. 南京师范大学虚拟地理环境教育部重点实验室, 南京 210046
• 收稿日期:2009-08-30 修回日期:2009-11-12 出版日期:2010-05-20 发布日期:2010-05-20
• 作者简介:刘学军(1965-),男,陕西合阳人,博士,教授,博士生导师。主要研究方向为DEM及其地形分析、GIS空间分析、空间数据不确定性等。E-mail:liuxuejun@njnu.edu.cn
• 基金资助:

国家自然科学基金资助项目(40971230、40571120);国家863计划资助项目(2006AA12Z212)

### Resolution analysis of grid digital elevation model based on data property

LIU Xue-jun, WANG Yan-fang, JIN Bei, MA Jin-juan

1. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University,Nanjing 210046, China
• Received:2009-08-30 Revised:2009-11-12 Online:2010-05-20 Published:2010-05-20

Abstract:

Horizontal resolution, which directly determines the degree of the closeness for DEM representing landform, is one of key variables for grid DEM. It also has distinct effects on the accuracy of terrain parameters and geosciences simulation based on grid DEM. So many researchers focus on the study of how to choose or decide a suitable resolution. This paper puts forward a method of suitable horizontal resolution based on geostatistics and nonparametric density estimation, which combines macroscopic variance and microcosmic variance. Firstly, supports of various scales are made by dividing the sampled data with different grids. Then regularization theory in geostatistics is used to carry out regularization variation of different supports based on the elevation sampled data. In order to ascertain the optimal support size to express macroscopic spatial variability structure of terrain, semivariance at a lag of one support interval plotted against different support sizes. The support size at which the peak occurs may help to identify the predominant scale of macroscopic spatial variation of the raw data, so it is named optimal support in this paper. After that, the theory estimation of the optimal bin size that can estimate the probability density function is referred to decide the appropriate resolution in the optimal scale support. The resolution is the suitable grid size to express the microcosmic terrain variance. Finally, the method was verified in practice by taking the sampled data sited in the Loess Plateau which is in the north of Shanxi province. Anisotropy, compute efficiency, RMSE statistics and contour-matching are used to analyze the results. The paper shows that the results resolution meets the exact accuracy limits for the given quality index. It is proved that the method may serve as a guide to decide the resolution from sampled elevation point data considering variance information contents of the raw data and topographic expression. The paper did the experiments only by taking the data from the Loess Plateau for examples. Future work needs to involve other topographic data and other different scales. Also, methods for verifying the result resolution should be further considered.