不确定性分析是数字地形分析的重要理论与方法，栅格DEM地形分类的不确定性分析依赖经典测量误差理论存在缺陷，亟待多学科综合、深层次地探索。本研究拟从熵的原型理论结合其演变历程，采用整体方法与还原方法相结合、定性分析与定量实验相结合的科学思维，多视角、较系统定位性描述栅格DEM地形分类不确定性分析的熵理论，构建栅格DEM地形分类不确定性熵分析的认知体系、关联机理与科学内涵。通过拓展熵在地形分析领域的不确定性分析模型，剖析栅格DEM地形分类的不确定性影响因素，揭示经典测量误差理论在度量栅格DEM地形分类不确定性方面存在的缺陷，并建立信息熵模型。顾及尺度效应问题，从宏观和微观两个层面研究栅格DEM地形地貌分类不确定性熵分析存在的差异性，探索不同尺度下栅格DEM地形分类不确定性熵分析指标与层次结构。研究结果将完善熵用于不确定性度量的理论，为测绘地理空间分析的不确定性研究提供可借鉴理论与方法。

Uncertainty analysis is an important theory and method of digital terrain analysis. There are defects existing in grid DEM landform classification uncertainty analysis based on classical surveying error theory. It is urgently to be investigated by multi-disciplinary and deep-exploration. With the prototype theory of entropy and evolution, the integral method and reduction method, the qualitative analysis and quantitative experiments, a multi perspective, systematic description of grid DEM landform classification uncertainty entropy theory should be made up and a systematical cognition must be constructed. By expanding the entropy analysis uncertainty analysis model in the field of landform, the affecting factors of DEM landform classification uncertainty was analyzed and the defects existing in the theory of classical surveying error for grid DEM landform classification uncertainty was reveled. At the same time, the establishment of information entropy model was carried up. Considering the problem of scale effect, the existing differences from macroscopic and microscopic scale grid DEM landform classification uncertainty entropy would be investigated and the analysis index and hierarchical structure under different scales of grid DEM landform classification should be established. The results will improve the entropy for uncertainty measurement theory, provides the reference for the theory and method of surveying and mapping geographic spatial analysis research of uncertainty.