McGET: A rapid image-based method to determine the morphological characteristics of gravels on the Gobi desert surface


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 < 0.001). Compared to manual digitization, the McGET accuracies for gravel coverage, gravel diameter and aspect ratio were 97%, 83% and 96%, respectively. The orientation distributions calculated were consistent across two different methods. More importantly, McGET significantly shortens the time cost in obtaining gravel morphological characteristics in the field and laboratory. The spatial variation results show that the gravel coverage ranged from 88% to 65%, the gravel diameter was unimodally distributed and ranged from 19 mm to 13 mm. Most gravels were bladed or rod-like, with a mean aspect ratio of 1.57, and had no preferred orientation on the surveyed Gobi desert. From the center to the edge of the fan, gravel coverage decreased 2.2% per 100 m elevation decrease (R2 = 0.69, P < 0.001), mean gravel diameter decreased 0.5 mm per 100 m elevation decrease (R2 = 0.52, P < 0.001), and mean aspect ratio slightly increased 0.004 per 100 m elevation decrease (R2 = 0.26, P < 0.05). These results imply that surface washing was the main process on the investigated Gobi desert. This study demonstrates that the new method can quickly and accurately calculate the gravel coverage, diameter, aspect ratio and orientation from the images of Gobi desert.

Bangyou Zheng
Bangyou Zheng
Data Scientist / Digital Agronomist

a research scientist of digital agriculture at the CSIRO.

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