GIS-Based Modeling for Vegetated Land Fire Prediction in Qaradagh Area, Kurdistan Region, Iraq

Sarkawt G. Salar, Arsalan Ahmed Othman, Sabri Rasooli, Salahalddin S. Ali, Zaid T. Al-Attar and Veraldo Liesenberg
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Sarkawt G. Salar: Department of Geography, College of Education, University of Garmian, Sulaymaniyah 46021, Iraq
Arsalan Ahmed Othman: Iraq Geological Survey, Al-Andalus Square, Baghdad 10068, Iraq
Sabri Rasooli: Department of Forestry, Faculty of Natural Resources, University of Guilan, Someh Sara 41996-13776, Iran
Salahalddin S. Ali: Civil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaimaniyah 46013, Iraq
Zaid T. Al-Attar: Department of Geology, University of Baghdad, Al-Jadiryah Street, Baghdad 10071, Iraq
Veraldo Liesenberg: Department of Forest Engineering, Santa Catarina State University (UDESC), Lages 88520-000, SC, Brazil

Sustainability, 2022, vol. 14, issue 10, 1-31

Abstract: This study aims to estimate the susceptibility of fire occurrence in the Qaradagh area of the Iraqi Kurdistan Region, by examining 16 predictive factors. We selected these predictive factors, dependent on analyzing and performing a comprehensive review of about 57 papers related to fire susceptibility. These papers investigate areas with similar environmental conditions to the arid environments as our study area. The 16 factors affecting the fire occurrence are Normalized Difference Vegetation Index (NDVI), slope gradient, slope aspect, elevation, Topographic Wetness Index (TWI), Topographic Position Index (TPI), distance to roads, distance to rivers, distance to villages, distance to farmland, geology, wind speed, relative humidity, annual temperature, annual precipitation, and Land Use and Land Cover (LULC). To extract fires that occurred between 2015 and 2020, 121 scenes of satellite images (most of them are scenes of Sentinel-2) were used, with the aid of a field survey. In total, 80% of the data (185,394 pixels) were used for the training dataset in the model, and 20% of the data (46,348 pixels) were used for the validation dataset. Conversely, 20% of these data were used for the training dataset in the model, and 80% of the data were used for the validation dataset to check the model’s overfitting. We used the logistic regression model to analyze the multi-data sites obtained from the 16 predictive factors, to predict the forest and vegetated lands that suffer from fire. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the accuracy of the proposed models. The AUC value is more than 84.85% in all groups, which shows very high accuracy for both the model and the factors selected for preparing fire zoning maps in the studied area. According to the factor weight results, classes of LULC and wind speed gained the highest weight among all groups. This paper emphasizes that the used approach is useful for monitoring shrubland, grassland, and cropland fires in other similar areas, which are located in the Mediterranean climate zone. Besides, the model can be applied in other regions, taking the local influencing factors into consideration, which contribute to forest fire mitigation and prevention planning. Hence, the mentioned results can be applied to primary warning, fire suppression resource planning, and allocation work. The mentioned results can be used as prior warnings of the outbreak of fires, taking the necessary measures and methods to prevent and extinguish fires.

Keywords: fires; Kurdistan Region; logistic regression; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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