Detecting areas vulnerable to sand encroachment using remote sensing and GIS techniques in the Shaqra and Tharmada Provinces, Saudi Arabia

Yousef Salem, Habes Ghrefat () and Nasser Zbnah
Additional contact information
Yousef Salem: King Saud University
Habes Ghrefat: Yarmouk University
Nasser Zbnah: King Khalid University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 6, No 47, 7509 pages

Abstract: Abstract Sand drift is a prevalent environmental issue observed in regions characterized by arid and semi-arid conditions. The current study aims to estimate the amount of sand drift, determine its trends, and model the risks of sand drift as well as its impact on vital institutions and urban and agricultural areas. The objectives were achieved through the utilization of mathematical models, remote sensing images, and geographic information systems (GIS) to model and map the risks associated with sand drift effectively. The study utilized the Analytic Hierarchy Process (AHP) to assess and prioritize the factors influencing sand drift, including wind speed, rainfall, slope, elevation, vegetation cover, soil, and crustal index moisture content. These weighted components were integrated into a GIS-based weighted overlay model to generate high-resolution spatial maps delineating areas most vulnerable to sand encroachment. The sand drift intensity exhibits a pronounced peak during the summer season, contributing approximately 35.1% of the total annual drift. The northern region of the study area emerged as a critical hotspot, accounting for 28.2% (26.7 m3/m) of the total sand transport, emphasizing the localized dynamics and their spatial variability. The supervised classification results indicated that the annual average area covered by sand in Tharmada Province from 2004 to 2012 amounted to 17.4 km, while in Shaqra Province, it reached 38 km for the same period. The results also showed that the high-risk areas in Shaqra Province are constantly increasing from 217.5 km in 2006 to 320 km in 2012. The high-risk areas in Tharmada Province ranged from 29.1 km in 2006 to 50.7 km in 2012. The integration of the Fryberger equation, Analytic Hierarchy Process (AHP), and remote sensing techniques establishes a scientifically robust framework for accurately assessing and mapping sand drift risks. The Fryberger equation provided precise quantitative insights into the dynamics of sand transport, while GIS-based spatial analysis effectively identified critical high-risk zones. The findings and methods used in the current study are considered beneficial and valuable, as they support future planning.

Keywords: Sand drifts; Remote sensing; Modelling; Risk; Saudi Arabia (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-07101-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:6:d:10.1007_s11069-024-07101-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-07101-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().