Gulling is a complex process depending on several factors and involving a wide range of sub-processes.Different types of gullies were distinguished and described in literature. Their contribution to soil erosionchanges in relation with the typology and their presence is influenced by different controlling factors.Mapping and classifying gullies is crucial for monitoring soil erosion. So far, no systematic definition ofmorphological characteristics of the different types of gullies and of their controlling factors has beenmade. The present work aims to suggest an innovative approach to automatically classify gullies byintegrating remote sensing, GIS and a classification algorithm. The study was carried out in three subcatchments(20km2) of the Platani River basin, located in southwest Sicily (Italy). Two gullies inventories(2014 and 2008 years), containing more than 400 erosion features, were prepared by integrating GoogleEarth and aerial orthophotographs images and further field checks. Once mapped, gullies were classifiedby using the location in the landscape, the morphology and the dominant erosion process leading to theirformation as criteria. Several primary and secondary topographic attributes were selected as independentvariables in the classification model. The Classification and Regression Tree (CART) algorithm was usedto predict the location of the different types of gullies and describe the influence of the different factorstaking part of the model. The results, described in terms of AUC values, show high model accuracy. CARTbasedgully classification is quicker and more objective than traditional methods. Moreover, the suggestedmethod provided important information about which is the dominant erosion process leading to gulliesformation.
|Number of pages||1|
|Publication status||Published - 2015|