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<p>Climate change has increased the risk of wildfires as a result of increasing temperatures and decreasing soil moisture. Therefore, continuous monitoring and reliable prediction of wildfires are essential to ensure their accurate and effective management and control. In this study, the spatial and temporal distribution of wildfires was examined using the MODIS burned area product (MCD64A1) for the period 2001–2021, and their occurrence was predicted using the Fire Weather Index (FWI) for the period 1995–2021.To adapt the FWI threshold values to the regional conditions of Oman, they were recalibrated using daily raster data, and their performance was validated. The kernel density function results showed that wildfires were more widespread and intense in the grasslands and pastures in Al-Hajar foothill xeric woodlands and shrublands, Arabian-Persian Gulf coastal plain desert ecoregions in the northern parts of Oman. Wildfires had the highest frequency during the summer, particularly in August (36%), and were most frequent in 2020 (58.2% of the total). The FWI thresholds were calibrated for Omani environmental conditions. For instance, the high-risk class threshold was modified from the standard value of 38 to 52, resulting in improved prediction accuracy (sensitivity = 81%) and reduced false alarms (49%). The results indicated that the modified FWI thresholds are well adapted <i>to</i> the local conditions of Oman and perform better than the standard threshold. In general, the results of this research are helpful in better understanding wildfire-prone zones and offer a scientific framework for wildfire forecasting and control, aiming to develop strategies for reducing risk and issuing necessary warnings.</p>

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Remote sensing analysis for wildfire monitoring and prediction in oman: insights from MODIS and fire weather index

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Hadis Sadeghi,
  • Mojtaba Shokouhi,
  • Ebrahim Asadi Oskouei,
  • Humaid AlBadi,
  • Ebrahim Fattahi,
  • Leili Khazanedari,
  • Morteza Pakdaman

摘要

Climate change has increased the risk of wildfires as a result of increasing temperatures and decreasing soil moisture. Therefore, continuous monitoring and reliable prediction of wildfires are essential to ensure their accurate and effective management and control. In this study, the spatial and temporal distribution of wildfires was examined using the MODIS burned area product (MCD64A1) for the period 2001–2021, and their occurrence was predicted using the Fire Weather Index (FWI) for the period 1995–2021.To adapt the FWI threshold values to the regional conditions of Oman, they were recalibrated using daily raster data, and their performance was validated. The kernel density function results showed that wildfires were more widespread and intense in the grasslands and pastures in Al-Hajar foothill xeric woodlands and shrublands, Arabian-Persian Gulf coastal plain desert ecoregions in the northern parts of Oman. Wildfires had the highest frequency during the summer, particularly in August (36%), and were most frequent in 2020 (58.2% of the total). The FWI thresholds were calibrated for Omani environmental conditions. For instance, the high-risk class threshold was modified from the standard value of 38 to 52, resulting in improved prediction accuracy (sensitivity = 81%) and reduced false alarms (49%). The results indicated that the modified FWI thresholds are well adapted to the local conditions of Oman and perform better than the standard threshold. In general, the results of this research are helpful in better understanding wildfire-prone zones and offer a scientific framework for wildfire forecasting and control, aiming to develop strategies for reducing risk and issuing necessary warnings.