Semi-Automated Object-Based Model for Producing Gully Erosion Inventory Map (Case Study: Lighvan watershed)

Document Type : Complete scientific research article


1 University of Tabriz – Faculty of Agriculture – Department of Soil Science

2 University of Tabriz – Faculty of Geography and Planing – Department of Remote Sensing and Geographic

3 University of Maragheh – Faculty of Agriculture – Department of Soil Science


Background and objectives: Due to the location of Iran in a dry and semi-arid region, it is always affected by sloping instability and erosion as the most severe type of erosion, gully erosion. This erosion pattern occurred in different parts of Iran and continuously over many years, and during erosion and the transfer of high sediment volume, it destroyed roads, facilities, pastures, slopes, etc. This will require the identification of high risk areas and the development of sensitivity maps. In recent years, the processing of satellite imagery as an advanced method has been widely used by researchers to increase the accuracy and save time and money. The object-oriented analysis of images is one of the most important methods for extracting information from satellite imagery, which is based on spectral, form and spatial characteristics and using expert knowledge to identify complications.
Materials and methods: In this research, the Lighwan watershed basin was studied as one of the most important sub-basins of Aji Chay in East Azarbaijan Province and the satellite images of Sentinel-2 (2016) with spatial resolution of 10, 20 and 60 meters for the processing and identification of gully Was used. The images were processed using the eCognition software and applied with different types of algorithms to design a semi-automatic model based on object-oriented analysis. Finally, in order to evaluate the accuracy of the model, the identified Gully were mapped out and calculated using ArcGIS software to match the ground reality map and the formation of the error matrix, manufacturer accuracy, user accuracy and kappa coefficient for each of the algorithms.
Results: The results showed that the Density and Compactness algorithms had the highest and lowest accuracy of the manufacturer (manufacturer accuracy was 88 and 78 respectively). While based on Kappa coefficient, the asymmetry algorithm has the highest accuracy compared to other methods (kappa = 0.91). Then, the shape index and density algorithms with kappa coefficient equal to 0.89 and 0.85 provided acceptable accuracy for the classification and identification of the gully.
Conclusion: The use of object-oriented methods due to the increased accuracy of classifying and identifying surface effects and phenomena can be used as a suitable solution for future soil studies and natural phenomena. In the present study, semi-automatic semi-automatic model for ditch identification was presented using spectral and geometric properties of Sentinel-2 satellite images and object-oriented processing in eCognition software environment.
Key words: Object Oriented Algorithms, Object-Oriented Processing, Segmentation, Classification


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