Object-based classification of landforms based on their local geometry and geomorphometric context

Terrain as a continuum can be categorized into landform units that exhibit common physical and morphological characteristics of land surface which may serve as a boundary condition for a wide range of application domains. However, heterogeneous views, definitions, and applications on landforms yield incompatible nomenclature that lacks interoperability. Yet, there is still room for developing methods for classification of land surface into landforms that can provide different disciplines with a basis of landscape description that is also commonsense to human insight. This study proposes a method of landform classification that reveals general geomorphometry of the landscape. A set of landform classes that are commonsense to human insight and relevant to various disciplines is adopted to generate landforms at the landscape scale. The proposed classification method is based on local geometry of the surface and the geomorphometric context in a higher level framework. A set of digital terrain models (DTMs) at relevant scale is utilized where local geometry is represented with morphometric DTMs, and the geomorphometric context is incorporated through 'relative terrain position' and 'terrain network.' 'Object-based image analysis' tools that have the ability to segment and classify DTMs into representative terrain objects and connect those objects in a multi-level hierarchy is utilized. Ambiguities in landforms both in attribute and geographical space are represented via fuzzy classification. The proposed method is applied to two different case areas to evaluate the efficiency and stability of the outcomes. Results reveal a reasonable amount of consistency where landform classes can be utilized as general or multi-purpose regarding some ambiguity that is already inherent in landforms.