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

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  • Iraj Rahimi

    (Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
    Darbandikhan Technical Institute, Sulaimani Polytechnic University, Wrme Street 327/76, Qrga, Sulaymaniyah 70-236, Iraq
    Institute of Earth Sciences, 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
    Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal)

  • Wafa Barkhoda

    (Department of Computer Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran
    Faculty of Information Technology, Kermanshah University of Technology, Kermanshah 67156-85420, Iran)

  • Ana Cláudia Teodoro

    (Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
    Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal)

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.

Suggested Citation

  • Iraj Rahimi & Lia Duarte & Wafa Barkhoda & Ana Cláudia Teodoro, 2025. "Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions," Land, MDPI, vol. 14(7), pages 1-21, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1334-:d:1685081
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    References listed on IDEAS

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    1. Ksenia S. Yankovich & Elena P. Yankovich & Nikolay V. Baranovskiy, 2019. "Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, March.
    2. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    3. Deniz Arca & Mercan Hacısalihoğlu & Ş. Hakan Kutoğlu, 2020. "Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 73-89, October.
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