| Abstrakt: |
Forest fires harm people and surrounding areas. Spatial analysis and remote sensing were used to track damaged areas in the forests of Mosul, located in northern Iraq. Geographic Information Systems (GIS) are crucial to achieving this. It showed the areas affected by fires for three years: the first (12-05-2017), the second (06-07-2021), and the third (2022-6-29). Using Sentinel-2 data, this study calculated the normalized burn rate (NBR) of affected areas before and after the fire. The Different Natural Burning Ratio (DNBR), which assesses fire intensity, was also utilized to estimate the damage caused by forest fires in Mosul. Furthermore, a map displaying the properties of forest vegetation was extracted using the Normalized Difference Vegetation Index (NDVI). To compare the vegetation before and after fire, the Difference Normalized Vegetation Index (DNDVI) was computed. To determine the relationship between DNDVI and DNBR also Enhancement vegetation Index 2 (EVI2) was calculated and the Difference Enhancement Vegetation Index (DEVI2). The methods employed were simple linear regression (R2) and Pearson correlation (r). Burn locations were identified using GIS. Using Pearson correlation and simple linear regression, fire severity was determined between three periods: 2022 (r=0.92, R2=0.8519), 2021 (r=0.49, R2=0.246), and 2017 (r=0.74, R2=0.5525). Also, the EVI2 (Enhanced Vegetation Index2), Different Normalized Difference Vegetation (DNDVI), Pearson correlation (r), and simple linear regression were used, as they were in 2017 (r=0.73, R²=0.5374) and in 2021 (r=0.47, R²=0.5374). 0.2237), and in 2022 (r=0.92, R²=0.8521). The study found that the maps resulting from these techniques can be useful for risk management and identifying areas with a high potential for fire danger. [ABSTRACT FROM AUTHOR] |
