DEM‐based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental

Abstract Sand dunes are predominantly distributed in arid regions. Automatic mapping and regionalization of sand dunes in large‐scale areas are crucial to understanding the evolution trends of aeolian sand environments. Different from existing studies primarily focused on mapping the extent of desert areas, this study proposes a framework for automatic identification and comprehensive regionalization based on dunes morphology. First, the basal terrain of the dune area is constructed, and the difference between this basal terrain and the DEM is calculated using threshold segmentation to delineate the extent of dunes. Further, some landscape metrics are selected to quantify the collective morphological characteristics of sand dunes. Then, a spatially constrained multivariate clustering method is applied to regionalize dunes comprehensively. Compared to existing DEM‐based dune extraction methods, this method can represent dune characteristics more accurately. The application in the Grand Erg Oriental achieved a high extraction accuracy of 94.17%, indicating its suitability for identifying dunes with diverse types in large‐scale areas. a six‐region map is generated that can clearly demonstrate diverse landscape patterns of dunes. The results indicate that the subregions containing branching linear dunes and network dunes have the highest area proportions, accounting for 27.68% and 26.83% of the total study area, respectively. This study provides valuable reference for aeolian geomorphology research specifically offering support for studies in the Grand Erg Oriental region.

Authors

Institutions

Publication Details

Journal
Earth Surface Processes and Landforms
Published
2024-11-28
DOI
https://doi.org/10.1002/esp.6042
Citations
1
Primary Topic
Soil and Environmental Studies
Type
article
Field-Weighted Citation Impact
0.33

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DEM‐based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental

Hong Wei, Guoan Tang, Yang Chen, Xin Yang et al.
1 citations
Earth Surface Processes and Landforms
Soil and Environmental Studies
0.33
article

DEM‐based sand dune analysis: Identification and morphological regionalization in the Grand Erg Oriental

Hong Wei, Guoan Tang, Yang Chen, Xin Yang, Chenrui Li, Hui Zhu
article en
1 citations

Abstract

Abstract Sand dunes are predominantly distributed in arid regions. Automatic mapping and regionalization of sand dunes in large‐scale areas are crucial to understanding the evolution trends of aeolian sand environments. Different from existing studies primarily focused on mapping the extent of desert areas, this study proposes a framework for automatic identification and comprehensive regionalization based on dunes morphology. First, the basal terrain of the dune area is constructed, and the difference between this basal terrain and the DEM is calculated using threshold segmentation to delineate the extent of dunes. Further, some landscape metrics are selected to quantify the collective morphological characteristics of sand dunes. Then, a spatially constrained multivariate clustering method is applied to regionalize dunes comprehensively. Compared to existing DEM‐based dune extraction methods, this method can represent dune characteristics more accurately. The application in the Grand Erg Oriental achieved a high extraction accuracy of 94.17%, indicating its suitability for identifying dunes with diverse types in large‐scale areas. a six‐region map is generated that can clearly demonstrate diverse landscape patterns of dunes. The results indicate that the subregions containing branching linear dunes and network dunes have the highest area proportions, accounting for 27.68% and 26.83% of the total study area, respectively. This study provides valuable reference for aeolian geomorphology research specifically offering support for studies in the Grand Erg Oriental region.

Earth Surface Processes and LandformsVol. 50(1)
Nanjing Normal University (CN), Chuzhou University (CN), Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN)
National Natural Science Foundation of China
Life in Land
Openalex Percentile: Top 33%
Soil and Environmental Studies
0.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.