A Hierarchical System for Multi-Scale and Object-Based Landform Classification a
Clemens EISANKa,1 Department of Geography and Geology, University of Salzburg, Austria Abstract. Landforms are scale-dependent features of the land surface, and therefore are structured in a hierarchical manner. They define boundary conditions for applications in geomorphology, hydrology and related fields. Due to the increasing number of multi-scale studies in those disciplines, automated approaches to hierarchical landform classification based on land surface models are required. The presented research aims at developing landform ontologies as basis for hierarchical landform classification in object-based image analysis (OBIA). Outcomes are expected to contribute to a better understanding of the hierarchical structure of landforms and associated processes. If the proposed system proves to be transferable, there is the potential to implement it in software products, or to use it for automated landform mapping from databases. Keywords. Geomorphometry, OBIA, landform hierarchy, ontology, scale
1. Background and Motivation Landform classification is a major topic in geomorphometry, the science of quantitative land surface analysis. Landforms are defined as homogeneous parts of the earth’s surface in terms of land surface parameters (LSPs) such as slope gradient, elevation, and curvature. As discrete spatial units – either analog or digital – they present boundary conditions for applications in hydrology, geomorphology, soil science and related fields. A current overview is provided by [1]. Through GIS and based on digital elevation models (DEMs) at different resolutions (from 1 m LiDAR models to 90 m SRTM) it became possible to delineate and map landforms at a variety of scales. Depending on the spatial scale different domains of landforms can be distinguished. At broad scales general forms are identified (e.g. mountains, plains), while at fine scales more specific landforms – as subdivisions of the broad-scale forms – are recognized (e.g. gullies, hillslopes). Thus, the land surface shows a hierarchical structure of landforms [2]. Hierarchical terrain classification is needed to improve our understanding of how landforms and associated processes are interrelated within (horizontal) and across scales (vertical). So far, due to cell-based approaches, multi-level classifications have been lacking transferability. Interoperability of terrain classification systems require methods for the selection of characteristic scales [3] as well as ontological approaches for defining candidate landforms, landform properties, and landform structures [4, 5]. It still remains a great challenge to procure an automated classification system capable of 1
Corresponding Author: Clemens Eisank, University of Salzburg, Hellbrunnerstraße 34, 5020 Salzburg, Austria; E-mail:
[email protected].
mapping landforms at a variety of scales in order to digitally represent real-world landform hierarchies [6]. Object-based image analysis (OBIA) has several advantages over cell-based methods [7], and provides a powerful framework for both hierarchical terrain segmentation and classification based on a priori developed landform ontologies. Though, the potential and applicability of OBIA in the context of landform modelling has not yet been adequately investigated. These are the major shortcomings that demonstrate the significance of the proposed PhD research. 2. Research Objectives The four major objectives of the presented research are described in the following sections. 2.1. Developing Landform Ontologies Knowledge about landforms with respect to morphometric properties (e.g. slope, aspect, curvatures), morphology (e.g. circularity, elongation), and topological relationships (e.g. adjacent to, part of, contained by) will be formalized by ontological modelling. 2.2. Identifying Characteristic Scales in Land-Surface Models Multi-resolution segmentation will be performed on land surface models. Statistical methods will be applied to identify those scales, where digital objects best represent a group of similar-sized real-world features. 2.3. Semantic Import and Rule Set Development Semantics as from the proposed landform ontology will be linked with object properties in the software domain in order to develop transferable classification rules. 2.4. Hierarchical Landform Classification The system will be applied on an area with high alpine characteristics. Classification results will be validated based on field reference and expert knowledge. 3. Related Work In the last two decades questions of interoperability became increasingly important in GI science, and thus, the significance of ontologies increased. Landform ontologies have mainly been discussed on a theoretical or conceptual level [4, 5, 8]. Only a few authors proposed ontological models for structuring terrain knowledge [9, 10]. Landform classification has been of interest for many scientists. Most of them followed cell-based approaches (see [11] for a short review). Recently, OBIA has been used for landform classifications due to its potential of overcoming limitations of cellbased systems [12, 13, 14].
4. Methodological Approach Initially, knowledge about landforms, landform properties, and landform structure is formalized through ontological modelling. We use an ontology editor (e.g. Protégé 2000) for designing landform ontologies. Through multi-resolution segmentation in OBIA a range of object levels is generated for several land surface models. This procedure produces homogeneous spatial entities with boundaries such that coarser scale entities have precise boundaries within which finer scale entities nest perfectly (Figure 1). This is a condition for developing hierarchical classifications of landforms. In the next step, characteristic segmentation scales are detected by the method of local variance [15]. Then, the ontology is associated with object properties at the identified scales in order to define suitable rules for the hierarchical classification system in OBIA software. Final results are evaluated by experts, and manual classifications from field work. Probably, there will be an iterative process of classification, validation and refinement of rules until results are satisfying. In order to assess the transferability of the algorithm, the system will be tested on additional areas with similar topographic characteristics. The workflow is presented in Figure 2.
Figure 1. Hierarchical structure of image objects from multi-resolution segmentation in OBIA
Figu ure 2. 2 M Methoodolo ogicaal wo orkfllow for f hierar h rchiccal laandfo form classificationn
5. Res R earrch Car C rrieed O Out Wee suucceessfu fullyy ap pplieed the staatistticall metho m od of locaal varia v ance [15] forr deetecctingg chaaracteristicc scaaless off lannd surfaace moodels inn OB BIA A, annd – foor comppariison n reaason ns – also o inn a ccell--bassed env e vironnmeent ((Fig guree 3) [166].
Figu F ure 3. 3 Exxampples for f sccale signnaturees. Circle C es in ndicaate chharaccterisstic sscalees
In addition, we started developing landform ontologies. We proposed a semantic model that links landform concepts and the image object domain for the mapping of glacial landforms [17]. 6. Research Contribution to GI Science The proposed landform ontology can break new ground towards generic and automated hierarchical classification of landforms. If the developed ontology proves to be transferable, we plan to implement it as tool for application in OBIA software. In addition, this research will demonstrate the potentials of OBIA for multi-scale terrain analysis. Linking OBIA and geomorphometry will finally open new avenues towards discrete surface analysis [18]. Acknowledgements The presented PhD research is supported by the Austrian Science Fund (FWF) through the stand-alone project SCALA (Scales and Hierarchies in Landform Classification, FWF-P20777-N15, http://www.scala-project.at). References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]
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[14] S. Gosh, T.F. Stepinski and R. Vilalta, Automatic Annotation of Planetary Surfaces With Geomorphic Labels, IEEE Transactions on Geoscience and Remote Sensing 48 (2010), 175-185. [15] C.E. Woodcock and A.H.Strahler, The factor of scale in remote sensing, Remote Sensing of Environment 21 (1987), 311-332. [16] C. Eisank and L. Drăguţ, Detecting Characteristic Scales of Slope Gradient, in Proceedings of the Geoinformatics Forum Salzburg, A. Car, G. Griesebner and J. Strobl, eds., Wichmann, Heidelberg, 2010, in press. [17] C. Eisank, L. Drăguţ, J. Götz and T. Blaschke, Developing a semantic model of glacial landforms for object-based terrain classification – the example of glacial cirques, in Proceedings of GEOBIA 2010, in press. [18] J. Strobl, Segmentation-based terrain classification, in Advances in Digital Terrain Analysis, Q. Zhou, B. Lees and G. Tang, eds., Springer, Berlin Heidelberg, 2008, 125-139.
