LAUSR

Abstract The relationship between morphological characteristics (e.g. gravel size, coverage, angularity and orientation) and local geomorphic features (e.g. slope gradient and aspect) of desert has been used to explore the evolution process of Gobi desert. Conventional quantification methods are time-consuming, inefficient and even prove impossible to determine the characteristics of large numbers of gravels. We propose a rapid image-based method to obtain the morphological characteristics of gravels on the Gobi desert surface, which is called the “morphological characteristics gained effectively technique” (McGET). The image of the Gobi desert surface was classified into gravel clusters and background by a machine-learning “classification and regression tree” (CART) algorithm. Then gravel clusters were segmented into individual gravel clasts by separating objects in images using a “watershed segmentation” algorithm. Thirdly, gravel coverage, diameter, aspect ratio and orientation were calculated based on the basic principles of 2D computer graphics. We validated this method with two independent datasets in which the gravel morphological characteristics were obtained from 2728 gravels measured in the field and 7422 gravels measured by manual digitization. Finally, we applied McGET to derive the spatial variation of gravel morphology on the Gobi desert along an alluvial-proluvial fan located in Hami, Xinjiang, China. The validated results show that the mean gravel diameter measured in the field agreed well with that calculated by McGET for large gravels (R2 = 0.89, P