{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T15:21:26Z","timestamp":1770996086862,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T00:00:00Z","timestamp":1750636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Land"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/land14071334","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T04:50:05Z","timestamp":1750654205000},"page":"1334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7411-8637","authenticated-orcid":false,"given":"Iraj","family":"Rahimi","sequence":"first","affiliation":[{"name":"Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"Darbandikhan Technical Institute, Sulaimani Polytechnic University, Wrme Street 327\/76, Qrga, Sulaymaniyah 70-236, Iraq"},{"name":"Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7537-6606","authenticated-orcid":false,"given":"Lia","family":"Duarte","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"given":"Wafa","family":"Barkhoda","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran"},{"name":"Faculty of Information Technology, Kermanshah University of Technology, Kermanshah 67156-85420, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-6431","authenticated-orcid":false,"given":"Ana Cl\u00e1udia","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104415","DOI":"10.1016\/j.catena.2019.104415","article-title":"Improvement of Seasonal Runoff and Soil Loss Predictions by the MMF (Morgan-Morgan-Finney) Model after Wildfire and Soil Treatment in Mediterranean Forest Ecosystems","volume":"188","author":"Zema","year":"2020","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/s13280-018-1084-1","article-title":"Human-environmental drivers and impacts of the globally extreme 2017 Chilean fires","volume":"48","author":"Bowman","year":"2019","journal-title":"Ambio"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1641\/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2","article-title":"Proximate Causes and Underlying Driving Forces of Tropical Deforestation","volume":"52","author":"Geist","year":"2002","journal-title":"BioScience"},{"key":"ref_4","first-page":"33","article-title":"GIS-Based Multi-Criteria Decision Analysis for Forest Fire Susceptibility Mapping: A Case Study in Harenna Forest, Southwestern Ethiopia","volume":"57","author":"Suryabhagavan","year":"2016","journal-title":"Trop. 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