Integrating multi-criteria decision-making and remote sensing for wildfire susceptibility assessment of the Lower Ziz Valley oases in Morocco

Oasis ecosystems in North Africa are ecologically and economically vital landscapes. Yet they are increasingly threatened by wildfires driven by intensifying climate change and anthropogenic pressures. Despite this urgency, spatially explicit wildfire susceptibility assessments for arid oasis environments remain scarce. This study develops and validates a Fire Susceptibility Index (FSI) for the Lower Ziz Valley oases in southeastern Morocco using an integrated Multi-Criteria Decision-Making (MCDM) and remote sensing framework. Six conditioning factors were selected: Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), proximity to roads and settlements, and land cover classified via a U-Net deep learning model (overall accuracy: 95%). Factor weights were determined through the Analytic Hierarchy Process (AHP; CR = 0.09) and aggregated using Weighted Linear Combination (WLC). Land cover (w = 0.30) and NDVI (w = 0.25) emerged as dominant controls, with anthropogenic proximity factors jointly accounting for 31% of the index weight. FSI values ranged from 0.30 to 0.91 (mean = 0.67), with 42.7% of the oasis area (~ 1,707 ha) classified as high susceptibility (FSI ≥ 0.7). Validation against the August 2021 fire event (318 ha) demonstrated strong predictive performance: 96.5% of burned pixels fell within high-risk zones, and mean FSI was significantly higher for burned (0.72) than unburned (0.67) pixels ( p < 0.001), with an AUC of 0.703. Corroborating validation against 60 historical fire records (2009–2024) showed that 98% of events occurred in high or extreme susceptibility zones. The resulting FSI map provides a robust spatial basis for prioritizing fuel management interventions and fire prevention strategies. Its integration into a dynamic geodatabase could further support the development of an early warning system (EWS) for operational wildfire prediction in these irreplaceable oasis landscapes.