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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (3): 310-326    DOI: 10.1007/s40333-023-0010-y
Research article     
Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map
XU Tao1,2, YU Huan1,*(), QIU Xia3, KONG Bo4, XIANG Qing1, XU Xiaoyu5,6, FU Hao7
1College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
2Beijing SuperMap Software Co., Ltd., Beijing 100015, China
3Sichuan Real Estate Registration Center, Chengdu 610014, China
4Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China
5School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America
6Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America
7Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China
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Abstract  

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring. However, traditional methods for studying gravels are low-efficiency and have many errors. This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map (SOM) and multivariate statistical methods in the grassland of northern Tibetan Plateau. Moreover, the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed. The results showed that the morphological characteristics of gravels in northern region (cluster C) and southern region (cluster B) of the Tibetan Plateau were similar, with a low gravel coverage, small gravel diameter, and elongated shape. These regions were mainly distributed in high mountainous areas with large topographic relief. The central region (cluster A) has high coverage of gravels with a larger diameter, mainly distributed in high-altitude plains with smaller undulation. Principal component analysis (PCA) results showed that the gravel distribution of cluster A may be mainly affected by vegetation, while those in clusters B and C could be mainly affected by topography, climate, and soil. The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels, providing a new mode for gravel research.

Key words： self-organizing map      digital image processing      morphological characteristics      multivariate statistical method      environmental monitoring     
Received: 17 November 2022      Published: 31 March 2023
Corresponding Authors: * YU Huan (E-mail: yuhuan0622@126.com)
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XU Tao
YU Huan
QIU Xia
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XIANG Qing
XU Xiaoyu
FU Hao
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XU Tao, YU Huan, QIU Xia, KONG Bo, XIANG Qing, XU Xiaoyu, FU Hao. Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map. Journal of Arid Land, 2023, 15(3): 310-326.

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http://jal.xjegi.com/10.1007/s40333-023-0010-y     OR     http://jal.xjegi.com/Y2023/V15/I3/310

Fig. 1 Research area and data processing. (a), location of sampling points in the study area; (b), extraction morphological characteristics of gravels; (c), inversion morphological characteristics of gravels.
Parameter Method of calculation Meaning and unit
Perimeter (L) Software automatically calculates and counts all gravel edge pixel lengths and converts them to actual lengths mm
Area (A) Software automatically calculates and counts the pixel area contained in all gravels and converts it to the actual area mm2
Mean gravel size (dmean) ${{d}_{mean}}=2\frac{\sum\limits_{i=1}^{i=n}{{{\log }_{2}}{{F}_{i}}}}{N}$ mm
Coverage (C) $C=\frac{{{A}_{p}}}{{{A}_{q}}}$×100% Percentage of gravel area (Ap) in the sample to the sample area (Aq); %
Shape ratio (S) $S=\frac{{{L}_{p}}}{{{L}_{m}}}$ Ratio of long axis (Lp) to short axis (Lm) of gravel, and the greater the value, the more slender the shape
Roundness (G) $G=\frac{4\Pi A}{{{L}^{2}}}$ Rounding degree of particles, and 1.0 for spherical particles
Table 1 Geometric eigenvalue description of gravels
Fig. 2 (a), SOM (self-organizing map) structural diagram chart. xi1, xi2 and xi3 are the input vectors, and wij is a weight vector; (b), weight vector adjustment diagram (Zhong et al., 2019).
Fig. 3 Morphological characteristic and variation of gravels. (a), spatial distribution of morphological characteristics of gravels in 16 sample points; (b), number of sample points in different grades; (c), morphological variation of gravels.
Fig. 4 Inverting spatial distribution of gravels (a) and statistical variation (b)
Fig. 5 Result of self-organizing map (SOM). (a), SOM visualization of gravel characteristic parameters, where different colors represent different neighborhood distances; (b), change of Davies-Bouldin index with the optimal number of SOM clusters; (c), SOM iteration process; (d), changing trend of SOM center point in the clustering process; (e), node number and SOM clusters; (f), weight vector distribution.
Fig. 6 Cluster analysis of gravel morphological characteristics. (a), correlation matrix plot of morphological characteristics of gravels. Blue, green, red, and gray fonts represent the correlation between cluster A, cluster B, cluster C and the whole, respectively; blue, green and red dots represent the sample distribution of cluster A, cluster B and cluster C, respectively. *, **, and *** are the significance levels at P<0.05, P<0.01, and P<0.001 levels, respectively. (b), box-whisker plots of cluster A, cluster B and cluster C for morphological characteristics of gravels. The median value is shown as a line within the box. The red dotted line outside the box represents the mean of the samples. Circle is the outlier.
Fig. 7 Principal component analysis (PCA) and spatial distribution of gravels. (a), PCA of different clusters; (b), sample distribution of PCA; (c), representativeness of variable to PCA; (d), spatial distribution of clusters.
Fig. 8 Influence of environmental variables on gravels in different clusters. + means positive influence, and - means negative influence.
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