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Evaluating ecotourism potential in arid oasis landscapes using geographic information systems, analytic hierarchy process, and preference ranking organization method for enrichment evaluation: The Saoura Corridor case study
 
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Ukryj
1
AUTES Research Laboratory, University of Constantine 3, Salah Boubnider, Algeria
 
2
Department of Architecture, University of Larbi Ben M’hidi Oum El Bouaghi, Algeria
 
3
Department of Geography and Regional Planning, Larbi Ben Mhidi University, Algeria
 
4
Department of Architecture, Tahri Mohamed University, Bechar, Algeria
 
5
AUTES Research Laboratory, Salah Boubnider University of Constantine 3, Algeria
 
6
Laboratory of Evaluation of Quality in Architecture and In-Built Environment, Department of Architecture, University of Larbi Ben M’hidi Oum El Bouaghi, Algeria
 
 
Autor do korespondencji
Maya Benoumeldjadj   

AUTES Research Laboratory, University of Constantine 3, Salah Boubnider, Algeria
 
 
 
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
Sustainable spatial planning in hyper-arid and oasis environments requires rigorous, transparent, and reproducible decision-making frameworks to balance ecotourism development with fragile ecosystem conservation. This study introduces an integrated geospatial multi-criteria decision analysis (MCDA) framework combining Google Earth Engine (GEE), Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), and the PROMETHEE method to evaluate and prioritize ecotourism suitability across the Saoura corridor (Béchar, Taghit, and Kenadsa) in southwestern Algeria. While focused empirically on this corridor, the framework is designed to be transferable to comparable drylands globally, including the Middle East, Central Asia, and sub-Saharan Africa. Utilizing a 2025 Landsat 8 OLI/TIRS median composite classified with a Random Forest algorithm and ALOS PALSAR DEM data, eight biophysiographic, morphometric, and logistic criteria were modeled. The AHP pairwise comparison matrix yielded a high-reliability consistency ratio (CR = 8.5%), identifying land-use/land-cover (LULC) as the most influential criterion (30.7%). The Random Forest classification reached an overall accuracy of 97.5% (κ = 0.97) on an independent validation subset (n = 80), and PROMETHEE II was used to rank 134 spatial alternatives. The results designate Kenadsa, Béchar, and Taghit as priority development poles, providing a robust spatial tool to guide post-petroleum regional tourism planning while safeguarding vulnerable environments in support of SDGs 8, 11, 13, and 15.
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