Skip to Main Content
Landform mapping is more important than ever before, yet the automatic recognition of specific landforms remains difficult. Object-based image analysis (OBIA) steps out as one of the most promising techniques for tackling this issue. Using the OBIA approach, in this study, a multiscale mapping workflow is developed and applied to two different input data sets: aerial photographs and digital elevation models. Optical data are used for gully mapping on a very local scale, while terrain data are employed for drumlin mapping on a slightly broader scale. After a multiresolution segmentation, a knowledge-based classification approach was developed for the multiscale mapping of targeted landforms. To identify well-suited scale levels for data segmentation, the estimation-of-scale-parameter tool was applied. Contrast information and shape properties of segments were implemented for gully classification. Contextual and shape information was utilized for mapping drumlins. An accuracy assessment was performed by comparing classification results with independent reference data sets that were delineated manually from the input data. We achieved satisfactory agreements between mapped and reference landforms. Knowledge-based identification of segment features improves both accuracy and transferability of the classification system.