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Placing soil‐genesis and transport processes into a landscape context: A multiscale terrain‐analysis approach
Author(s) -
Möller Markus,
Volk Martin,
Friedrich Klaus,
Lymburner Leo
Publication year - 2008
Publication title -
journal of plant nutrition and soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.644
H-Index - 87
eISSN - 1522-2624
pISSN - 1436-8730
DOI - 10.1002/jpln.200625039
Subject(s) - context (archaeology) , terrain , landform , scale (ratio) , soil map , computer science , cartography , geography , geology , soil science , soil water , archaeology
Landforms and landscape context are of particular importance in understanding the processes of soil genesis and soil formation in the spatial domain. Consequently, many approaches for soil generation are based on classifications of commonly available digital elevation models (DEM). However, their application is often restricted by the lack of transferability to other, more heterogeneous, landscapes. Part of the problem is the lack of broadly accepted definitions of topographic location based on landscape context. These issues arise because of: (1) the scale dependencies of landscape pattern and processes, (2) different DEM qualities, and (3) different expert perceptions. To address these problems, we suggest a hierarchical terrain‐classification procedure for defining landscape context. The classification algorithm described in this paper handles object detection and classification separately. Landscape objects are defined at multiple scales using a region‐based segmentation algorithm which allows each object to be placed into a hierarchical landscape context. The classification is carried out using the terrain attribute mass‐balance index across a range of scales. Soil genesis and transport processes at established field sites were used to guide the classification process. The method was tested in Saxony‐Anhalt (Germany), an area that contains heterogeneous land surfaces and soil substrates. The resulting maps represent adaptation degrees between classifications and 191 semantically identified random samples. The map with the best adaptation has an overall accuracy of 89%.