Morphodynamic Classification of Ergs Using Multi-Temporal Satellite Imagery and Machine Learning: A Case Study of the Rig-e Yalan, Lut Desert, Iran

Goorabi, Abolghasem

doi:10.61882/jeer.405.1.22

year 16, Issue 1 (Spring 2026) E.E.R. 2026, 16(1): 22-43 | Back to browse issues page

‎ 10.61882/jeer.405.1.22

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Goorabi A. Morphodynamic Classification of Ergs Using Multi-Temporal Satellite Imagery and Machine Learning: A Case Study of the Rig-e Yalan, Lut Desert, Iran. E.E.R. 2026; 16 (1) :22-43
URL: http://magazine.hormozgan.ac.ir/article-1-904-en.html

Morphodynamic Classification of Ergs Using Multi-Temporal Satellite Imagery and Machine Learning: A Case Study of the Rig-e Yalan, Lut Desert, Iran

Abolghasem Goorabi ^*

Faculty of Geography, Department of Physical Geography, University of Tehran, Tehran, Iran , goorabi@ut.ac.ir

Abstract: (361 Views)

1. Introduction
Sand dune morphodynamics, shaped by interactions between wind regimes, sediment availability, topography, and sparse vegetation, are key drivers of desert landscape evolution and indicators of desertification processes (Ashrafzadeh et al., 2017; Ehsani & Foroutan, 2014). The Rig-e Yalan region, situated in the hyper-arid Lut Desert of eastern Iran (Figure 1), represents one of the most extreme natural laboratories globally, with surface temperatures exceeding 70 °C (Mildrexler et al., 2011) and diverse dune morphologies, including barchans, linear ridges, and star dunes (Milani et al., 2021). Its enclosed topography, concentrated wind systems, and minimal human disturbance provide an exceptional setting for automated morphodynamic modeling. Traditional field surveys face challenges in this harsh environment, highlighting the importance of remote sensing, cloud-computing, and machine learning approaches (Goorabi & Yamani, 2025). The objectives of this study are: (1) to preprocess and integrate multi-sensor datasets; (2) to derive geomorphometric and spectral indices; (3) to classify homogeneous morphodynamic units using supervised and unsupervised models; and (4) to validate the classification framework for monitoring desertification and informing adaptive land management.
2. Methodology
A multi-sensor workflow was implemented in Google Earth Engine (GEE) using datasets from Sentinel-2 (10 m), Landsat-8 (30 m), Sentinel-1 SAR (10 m), and topographic data from SRTM (30 m) and ALOS PALSAR (12.5 m). Preprocessing included atmospheric correction, cloud/shadow masking (QA60, BQA), geometric and terrain correction. In total, 27 indices were extracted (Table 1), including 13 geomorphometric indices (slope, aspect, Topographic Position Index [TPI], Terrain Ruggedness Index [TRI]), 11 spectral indices (NDVI, NDWI, Bare Soil Index [BSI], etc.), and 3 Sentinel-2 vegetation indices (NDRE). Dimensionality reduction was achieved via Principal Component Analysis (PCA), where PC1–PC3 captured most variance. Two new composite metrics—the Morphodynamic Activity Index (MAI) and Integrated Dune Mobility Score (IDMS)—were developed (Table 2). Classification combined a Random Forest (RF) algorithm with 100 trees and 5-fold cross-validation, alongside K-Means clustering (K=5, Elbow Method). The COSI-Corr algorithm was applied to multi-temporal imagery for sub-pixel dune migration analysis. Validation used Google Earth Pro imagery and synthetic datasets, evaluated via overall accuracy (OA), Kappa coefficient, and confusion matrices.
3. Results
The RF model achieved an OA of 91.14% and a Kappa of 0.88, successfully delineating six morphodynamic units: active dunes (15.5%), semi-active dunes (20.8%), sandy surfaces (36.3%), hard surfaces (12.5%), wind-eroded areas (8.4%), and mixed terrains (6.5%) (Figure 2). High TRI and TPI values in the southern sector indicated stronger aeolian activity. The MAI (0.847) and IDMS (0.923) confirmed Rig-e Yalan’s extreme dune mobility, driven by negligible vegetation (mean NDVI = 0.086), elevated bare soil exposure (mean BSI = 0.180), and rugged terrain (mean TRI = 0.802). Tasseled Cap analysis revealed high brightness (mean = 0.350), near-zero greenness (–0.025), and minimal wetness (0.025), consistent with hyper-arid conditions (Figure 3). The Desertification Risk Index (DRI = 0.756) and Ecological Sensitivity Index (ESI = 0.78) highlighted severe environmental vulnerability. Pearson correlations showed a strong negative NDVI–BSI relationship (r = –0.82) and positive slope–TRI relation (r = 0.73), validating consistency. K-Means clustering corroborated the RF classification (Figure 4). COSI-Corr revealed dune migration, particularly in active dune zones, confirming dynamic morphodynamics (Figure 5).
4. Discussion & Conclusions
This study demonstrates a robust, automated framework for classifying and monitoring dune morphodynamics in hyper-arid landscapes. By integrating multi-sensor datasets and advanced machine learning, it overcomes challenges of spectral similarity and lack of field data. The RF model achieved high accuracy (91.14%, Kappa 0.88), while novel indices (MAI = 0.847, IDMS = 0.923) provide scalable tools for quantifying dune mobility, supporting desertification monitoring (Miszalski et al., 2023; Blumstein & MacManes, 2023). The use of GEE ensured efficient, reproducible analysis across large spatio-temporal datasets. Findings confirm Rig-e Yalan’s exceptional morphodynamic activity and ecological fragility (ESI = 0.78), underscoring its UNESCO World Heritage significance. The framework can be extended to other deserts (e.g., Sahara, Gobi) for global comparative studies. Limitations include absence of in-situ validation and reliance on GEE’s processing infrastructure. Future work should incorporate targeted field campaigns, test deep learning approaches, and establish global dune dynamics databases. These results provide actionable insights for climate adaptation, sustainable land management, and regional policy-making.

Keywords: Aeolian processes, Geomorphometry, Geomorphometric indices, Random Forest, Google Earth Engine

Full-Text [PDF 2082 kb] (76 Downloads)

Type of Study: Research |
Received: 2025/08/1 | Published: 2026/04/16

References

1. Ashrafzadeh, A., Ehsani, A. H., & Foroutan, M. (2017). Geomorphological analysis of sand dune systems in the Lut Desert, Iran. Journal of Arid Environments, 145, 45-53. [DOI:10.1016/j.jaridenv.2017.05.008]

2. Bagnold, R. A. (1941). The physics of blown sand and desert dunes. Methuen.

3. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. [DOI:10.1016/j.isprsjprs.2016.01.011]

4. Blasco, J. M. D., Blasco, J., & Peralta, J. (2020). SAR data for monitoring desertification processes: A review. Remote Sensing of Environment, 238, 111-123. [DOI:10.1016/j.rse.2019.111123]

5. Blumstein, D. M., & MacManes, M. D. (2023). Physiological adaptations of desert fauna to extreme aridity: A case study from the Lut Desert. Journal of Comparative Physiology B, 193(2), 89-102. [DOI:10.1007/s00360-022-01476-3]

6. Bullard, J. E., & Livingstone, I. (2002). Interactions between aeolian and fluvial systems in dryland environments. Progress in Physical Geography, 26(3), 333-357. [DOI:10.1191/0309133302pp340ra]

7. Bullard, J. E., White, K., & Livingstone, I. (2011). Morphodynamic monitoring of desert dunes using multi-temporal satellite imagery. Earth Surface Processes and Landforms, 36(12), 1571-1581. [DOI:10.1002/esp.2180]

8. Cruz, J., Oxoli, D., & Brovelli, M. A. (2023). Machine learning for geomorphological mapping in arid environments: Challenges and opportunities. Geomorphology, 425, 108-120. [DOI:10.1016/j.geomorph.2022.108120]

9. Ehsani, A. H., & Quiel, F. (2008). Application of remote sensing for mapping sand dune morphology in the Lut Desert, Iran. International Journal of Remote Sensing, 29(17), 4907-4923. [DOI:10.1080/01431160802036547]

10. Ehsani, A. H., & Foroutan, M. (2014). Wind regime and its impact on dune morphology in the Lut Desert. Aeolian Research, 15, 231-240. [DOI:10.1016/j.aeolia.2014.07.002]

11. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. [DOI:10.1016/j.rse.2017.06.031]

12. Goorabi, A. (2023). Applied geomorphometry: Data, methods, and techniques. Tehran: University of Tehran Press.

13. Goorabi, A. (2025). Geomorphometric characterization of dunes in the Rig-e-Yalan, Dasht-e-Lut: Aeolian processes and spatial analysis. Quantitative Geomorphological Research, 14(2), 24-41.

14. Hugenholtz, C. H., Levin, N., Barchyn, T. E., & Baddock, M. C. (2012). Remote sensing and spatial analysis of aeolian sand dunes: A review. Progress in Physical Geography, 36(4), 474-500. https://doi.org/10.1016/j.earscirev.2011.11.006 [DOI:10.1177/0309133312442977]

15. Kocurek, G., & Ewing, R. C. (2005). Aeolian dune field self-organization: Implications for the formation of simple versus complex dune-field patterns. Geomorphology, 72(1-4), 94-105. [DOI:10.1016/j.geomorph.2005.05.005]

16. Lancaster, N. (2009). Geomorphology of desert dunes. Routledge.

17. Livingstone, I., Wiggs, G. F. S., & Weaver, C. M. (2007). Geomorphology of desert sand dunes: A review of recent progress. Earth-Science Reviews, 80(3-4), 239-257. https://doi.org/10.1016/j.earscirev.2006.09.004 [DOI:10.1016/j.earscirev.2006.10.004]

18. Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817. [DOI:10.1080/01431161.2018.1433343]

19. Milani, L., Ehsani, A. H., & Rezaei, M. (2021). Diversity of dune morphology in the Lut Desert: A geomorphometric approach. Journal of Arid Land, 13(6), 567-582. [DOI:10.1007/s40333-021-0067-4]

20. Mildrexler, D. J., Zhao, M., & Running, S. W. (2011). Satellite-based assessment of surface temperature extremes in the Lut Desert, Iran. Remote Sensing of Environment, 115(12), 3494-3503. [DOI:10.1016/j.rse.2011.08.017]

21. Miszalski, Z., Kornas, A., & Surówka, E. (2023). Halophytic vegetation adaptations in hyper-arid environments: Insights from the Lut Desert. Plant Ecology, 224(5), 401-415. [DOI:10.1007/s11258-023-01312-7]

22. Mohammadpour, M., & Eshghizadeh, M. (2021). Challenges in field validation of remote sensing models in arid regions: A case study from Iran. Journal of Applied Remote Sensing, 15(2), 024518. [DOI:10.1117/1.JRS.15.024518]

23. Necsoiu, M., Leprince, S., Hooper, D. M., Dinwiddie, C. L., McGinnis, R. N., & Walter, G. R. (2009). Monitoring migration rates of an active subarctic dune field using optical imagery. Remote Sensing of Environment, 113(12), 2441-2447. [DOI:10.1016/j.rse.2009.07.002]

24. Oxoli, D., & Brovelli, M. A. (2022). Multi-sensor data integration for geomorphological analysis in desert environments. Sensors, 22(15), 5678. [DOI:10.3390/s22155678]

25. Vermeesch, P., & Drake, N. (2008). Remotely sensed dune celerity and sand flux measurements in the world's driest deserts. Earth Surface Processes and Landforms, 33(10), 1573-1585. [DOI:10.1002/esp.1679]

26. White, K., Bullard, J. E., & Livingstone, I. (2007). Monitoring desert dune dynamics using Landsat imagery. International Journal of Remote Sensing, 28(17), 3743-3757. [DOI:10.1080/01431160601105733]

27. Zhao, M., Zhang, H., & Running, S. W. (2025). Advances in high-resolution satellite imagery for desert geomorphology: A review. Geomorphology, 448, 109-124. [DOI:10.1016/j.geomorph.2024.109124]