Comparison of Visual and Object-Based Automatic Methods to Identify Landforms in Yazd-Ardakan Basin

Document Type : علمی - پژوهشی

Author

Instructor in Dep. of Geography, Payam Noor University (PNU)

Abstract

Identification of dominant landforms is important in a landscape because they applicable in various types of urban planning, tourism planning, spatial planning, etc. In this study, the landforms of Yazd-Ardakan basin were identified by two visual and automatic methods and then were compared. In automatic method, were used by Multiresolution and Contrast Split image segmentation in the object based concepts for identification of geomorphological landforms. The results showed using “Multiresolution Segmentation” due to consider the shape parameter is appropriate in the recognition of the landforms structure and their natural boundary such as alluvial fan but using the “contrast split image segmentation” is appropriate for micro-landform recognition such as braided river at the surface alluvial fans. The results of the comparison of visual and automated landforms maps showed that the visual approach was only useful for macro-landforms such as mountain masses, types of pediments, Ardakan playa and were barely detectable dunes, But the object based automatic approach not only mentioned landforms but also smaller landforms were identified such as the transverse dunes, alluvial fans, badlands, inselbergs. To evaluate the accuracy of automatic landforms identification models were used both qualitative and quantitative methods; in the qualitative evaluation were used the overlay technique to the visual investigation of matching the map of the model with Google Earth images. The quantitative evaluation was used the confusion matrix. The results of the evaluations showed that Overall Accuracy and Kappa coefficient for the multiresolution algorithm in landforms recognition are 97.46% and 96.53% respectively. Also, Commission and Omission errors showed that the minimum identification errors are related to soft surfaces such as Plain, but the maximum of the identification errors are related to rough surfaces like mountainous. 

Keywords

References

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