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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (8): 1168-1187    DOI: 10.1007/s40333-025-0107-6    
Research article     
Automatic classification of coastal sand dunes in the Namib Desert through the texture analysis approach
JIN Zikai1,2,3, LI Fayuan1,2,3,*(), LIU Lulu1,4, JIAO Haoyang1,2,3, CUI Lingzhou5
1School of Geography, Nanjing Normal University, Nanjing 210023, China
2Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
3Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4Ningbo Institute of Surveying, Mapping and Remote Sensing, Ningbo 315042, China
5College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
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Abstract  

Texture analysis methods offer substantial advantages and potential in examining macro-topographic features of dunes. Despite these advantages, comprehensive approaches that integrate digital elevation model (DEM) with quantitative texture features have not been fully developed. This study introduced an automatic classification framework for dunes that combines texture and topographic features and validated it through a typical coastal aeolian landform, namely, dunes in the Namib Desert. A three-stage approach was outlined: (1) segmentation of dune units was conducted using digital terrain analysis; (2) six texture features (angular second moment, contrast, correlation, variance, entropy, and inverse difference moment) were extracted from the gray-level co-occurrence matrix (GLCM) and subsequently quantified; and (3) texture-topographic indices were integrated into the random forest (RF) model for classification. The results show that the RF model fused with texture features can accurately identify dune morphological characteristics; through accuracy evaluation and remote sensing image verification, the overall accuracy reaches 78.0% (kappa coefficient=0.72), outperforming traditional spectral-based methods. In addition, spatial analysis reveals that coastal dunes exhibit complex texture patterns, with texture homogeneity being closely linked to dune-type transitions. Specifically, homogeneous textures correspond to simple and stable forms such as barchans, while heterogeneous textures are associated with complex or composite dunes. The complexity, periodicity, and directionality of texture features are highly consistent with the spatial distribution of dunes. Validation using high-resolution remote sensing imagery (Sentinel-2) further confirms that the method effectively clusters similar dunes and distinguishes different dune types. Additionally, the dune classification results have a good correspondence with changes in near-surface wind regimes. Overall, the findings suggest that texture features derived from DEM can accurately capture the dynamic characteristics of dune morphology, offering a novel approach for automatic dune classification. Compared with traditional methods, the developed approach facilitates large-scale and high-precision dune mapping while reducing the workload of manual interpretation, thus advancing research on aeolian geomorphology.

Key words： coastal dune      topographic texture      random forest      digital elevation model (DEM)      dune classification      gray-level co-occurrence matrix (GLCM)     
Received: 14 April 2025      Published: 31 August 2025
Corresponding Authors: *LI Fayuan (E-mail: lifayuan@njnu.edu.cn)
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JIN Zikai
LI Fayuan
LIU Lulu
JIAO Haoyang
CUI Lingzhou
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JIN Zikai, LI Fayuan, LIU Lulu, JIAO Haoyang, CUI Lingzhou. Automatic classification of coastal sand dunes in the Namib Desert through the texture analysis approach. Journal of Arid Land, 2025, 17(8): 1168-1187.

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http://jal.xjegi.com/10.1007/s40333-025-0107-6     OR     http://jal.xjegi.com/Y2025/V17/I8/1168

Fig. 1 Dune type classification in the Namib Desert. (a), barchan dune and dune chain; (b), simple linear dune; (c), composite linear dune; (d), complex linear dune; (e), grid-shaped dune; (f), star dune; (g), transverse dune; (h), dendritic dune.
Type Data Spatial resolution Usage Year Data source
DEM FABDEM 30 m Feature extraction 2023 https://data.bris.ac.uk/data/dataset
Remote sensing image Sentinel-2 10 m Sample collection and accuracy assessment 2021 https://dataspace.copernicus.eu/
Wind speed ERA5 0.25° DP calculation 2018-2022 https://www.ecmwf.int/
Table 1 Experimental data
Fig. 2 Flowchart of the method used in this study. GLCM, gray-level co-occurrence matrix; DEM, digital elevation model; FDEM, inverse digital elevation model.
Parameter Physical importance
Angular second moment Reflecting the uniformity and coarseness of texture distribution. A high angular second moment value indicates a uniform texture distribution.
Contrast Representing the degree of difference between neighboring pixels, which can be understood as the prominence or intensity of the texture. It is used to describe the roughness of surface or landform features.
Correlation Measuring the similarity of texture in different directions. A region's correlation value will be high if it exhibits strong directional characteristics.
Variance Reflecting the degree of difference between gray levels in the texture and can be used to describe the roughness of a surface or landform.
Entropy Representing the amount of information in an image and describing the complexity of the texture. Highly ordered textures have low entropy values.
Inverse difference moment A statistical feature that reflects the coarseness of a texture and is used to describe the fineness or roughness of a surface or landform.
Table 2 Six texture characteristic parameters and their physical importance
DP (VU) Wind energy environment UDI Wind direction category
0.00-200.00 Low wind energy environment 0.00-0.30 Compound wind regime and broad bimodal wind regime
200.00-400.00 Moderate wind energy environment 0.30-0.80 Broad or narrow bimodal wind regime
>400.00 High wind energy environment >0.80 Broad or narrow unimodal wind regime
Table 3 DP-based wind energy environment and unidirectional index (UDI)-based wind direction category
Fig. 3 Feature values of six coastal dune texture parameters in the Namib Desert. (a), angular second moment; (b), contrast; (c), correlation; (d), entropy; (e), variance; (f), inverse difference moment.
Fig. 4 Spatial distribution of terrain texture complexity in the Namib Desert
Fig. 5 Spatial distribution of terrain texture periodicity in the Namib Desert
Fig. 6 Spatial distribution of terrain texture correlation in the Namib Desert at 0.00° (a), 45.00° (b), 90.00° (c), and 135.00° (d) directions
Fig. 7 Spatial distribution of dune type predicted by the random forest (RF) and zonal comparison between prediction result with remote sensing image in the Namib Desert. (a), spatial distribution of eight dune classification predicted by the RF model; (b), transverse and complex linear dune; (c), simple linear and composite linear dune; (d), dendritic and transverse dune; (e), star and complex linear dune. Of which, b1-e1 show the prediction results, and b2-e2 show the corresponding remote sensing images.
Barchan dune and dune chain Simple linear dune Composite linear dune Complex linear dune Grid-shaped dune Star dune Transverse dune Dendritic dune Total
Barchan dune and dune chain 1 - 1 1 5 - 2 - 10
Simple linear dune - 12 - - - - - 2 14
Composite linear dune - 2 25 1 - - - 2 30
Complex linear dune - - 1 66 2 - - 5 74
Grid-shaped dune - - - 6 6 - - - 12
Star dune - - - - - 16 1 2 19
Transverse dune - - - - - 1 20 1 22
Dendritic dune - - - 3 3 1 2 10 19
Total 1 14 27 77 16 18 25 22 200
Table 4 Confusion matrix of coastal dune type classification
Accuracy Barchan dune and dune chain Simple linear dune Composite linear dune Complex linear dune Grid-shaped dune Star dune Transverse dune Dendritic dune
Producer accuracy (%) 100.0 85.7 92.6 85.7 37.5 88.9 80.0 45.5
User accuracy (%) 10.0 85.7 83.3 89.2 50.0 84.2 90.9 52.6
Table 5 Producer and user accuracies of each dune type
Fig. 8 Near-surface wind regimes in the Namib Desert from 2018 to 2022. (a), annual average drift potential (DP); (b), annual average resultant drift potential (RDP) with resultant drift direction (RDD); (c), unidirectional index (UDI).
Fig. 9 Relationship between dune type and near-surface wind regimes. (a), location of the remote sensing image of dune types; (b), simple linear dune; (c), composite linear dune; (d), complex linear dune; (e), transverse dune; (f), star dune; (g), dendritic dunes. Of which (b1-g1) show remote sensing image of sand dune, and (b2-g2) show the DP rose diagram of the corresponding sand dune.
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