面向地貌学本源的DEM增值理论框架与构建方法

数字高程模型（DEM）在表达地貌形态、认知地表过程、揭示地学机理等研究中发挥着基础性的作用,是重要的地理空间数据模型,广泛地应用于地学分析与建模中。但是,传统DEM具有属性单一的天然缺陷,难以支撑面向地学过程与机理挖掘的地球系统科学研究。亟待在传统DEM的基础上实现其数据模型的增值,服务于新地貌学研究范式和新对地观测技术背景下的数字地形建模与分析。立足于以上问题,本文构建了DEM增值的理论框架,主要包括DEM增值的概念、内涵、内容、类别、不同增值类别之间的相互关系,以及此理论框架的研究意义和应用范畴。提出了DEM增值的构建方法,包含：① 强调地上地下一体化、时间空间相耦合的DEM空间维度和时间维度增值方法;② 重视地下、地表和地上物质构成,形态属性耦合的物质属性和形态属性增值方法;③ 顾及自然过程、人工作用的地物对象、地貌形态的地物要素和形体要素增值方法。最后,分别以数字阶地模型、数字坡地模型和数字流域模型为例,阐释DEM在面向地貌学本源问题时的不同增值方法及应用场景。期望通过对DEM进行维度、属性和要素3个层面的增值,实现现代对地观测技术背景下数字高程模型表达方法的突破,并支撑知识驱动的数字地貌问题分析。

Geomorphology-oriented theoretical framework and construction method for value-added DEM

Digital elevation model (DEM) plays a fundamental role in the research of the earth system by expressing surface shape, understanding surface process, and revealing surface mechanism. DEM is widely used in geography and even in geological modeling and analysis. However, the existing DEM has its limitations in supporting the earth system research focused on geographical process and mechanism mining due to single attribute of the traditional DEM data model. Hence, establishing the value-added data model on the basis of traditional DEM is urgently necessary to serve digital elevation modeling and terrain analysis under the background of a new geomorphology research paradigm and earth observation technology. We construct a theoretical framework for value-added DEM that mainly includes concept, connotation, content and categories. The relationship between different types of value-added DEMs as well as the research significance and application category of this theoretical framework are also proposed. Different methods of value-added DEMs are listed below: (1) value-added methods of DEM space and time dimension that emphasize the integration of the ground and underground as well as coupling of time and space; (2) attribute-based value-added methods composed of materials (including underground, surface, and overground materials) and morphological properties; and (3) value-added methods of features and physical elements that consider geographical objects and landform features formed by natural processes and artificial effects. The digital terrace, slope, and watershed models are used as examples to illustrate application scenarios of the three value-added methods. This study aims to improve expression methods of DEM under the background of new surveying and mapping technologies by adding value to the DEM at three levels of dimensions, attributes, and elements, and support knowledge-driven digital geomorphological analysis in the era of big data.