Spatial Analysis and Morphological Clustering of Barchan Dunes Using Advanced Computational Techniques

Barchan dunes, crescent-shaped aeolian landforms shaped by unidirectional wind regimes, are integral to understanding sediment transport and landscape evolution in arid regions. This study conducts a geomorphometric analysis and clustering of barchan dunes, focusing on their spatial distribution, morphological characteristics, and environmental controls. Leveraging high-resolution digital elevation models (DEMs) and advanced clustering algorithms such as K-Means, DBSCAN, and Gaussian Mixture Models, the research identifies patterns in dune morphology and examines their relationships with local topography and climatic drivers. The methodology integrates preprocessing steps, including feature extraction and scaling, with unsupervised clustering to classify dunes based on key attributes such as width, length, height, and geographic location. Morphometric analyses reveal spatial and structural variations, while clustering results highlight patterns linked to wind regimes and sediment availability. Among the clustering techniques, DBSCAN emerges as the most effective for identifying irregularly shaped clusters, achieving higher evaluation metrics such as Silhouette Scores and Davies-Bouldin Index values. This study investigates the morphology and spatial distribution of barchan dunes using advanced clustering techniques such as DBSCAN and K-Means. The primary issue addressed is the challenge of accurately classifying and analyzing irregularly shaped dune formations under varying environmental conditions. Utilizing high-resolution digital elevation models (DEMs), the research identifies distinct morphological patterns and their correlation with wind regimes and sediment availability. The study finds that DBSCAN outperforms other clustering algorithms in handling the irregular geometries of dunes, achieving superior evaluation metrics like Silhouette Scores and Davies-Bouldin Index. These findings provide valuable insights for environmental monitoring and desertification management.