Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions

Iraj Rahimi, Lia Duarte (), Wafa Barkhoda and Ana Cláudia Teodoro
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Iraj Rahimi: Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Lia Duarte: Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Wafa Barkhoda: Department of Computer Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran
Ana Cláudia Teodoro: Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal

Land, 2025, vol. 14, issue 7, 1-21

Abstract: Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used.

Keywords: fire susceptibility; NMF; semi-Mediterranean; semi-arid; ZGI; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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