Damage Detection in Forest Fire Areas Using Satellite Imagery

Orman fires are natural disasters that cause severe damage to ecosystems. Accurate assessment of post-fire damage is critical for monitoring ecological recovery and planning reforestation efforts. Satellite imagery is one of the most effective methods for damage detection, as it can cover large areas in a short time. Remote sensing technologies enable the identification of fire-affected areas, classification of damage severity, and analysis of long-term impacts on ecosystems.

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The main satellite systems used for monitoring forest fires and detecting damage are as follows:

• MODIS (Moderate Resolution Imaging Spectroradiometer): Detects fires using medium-resolution imagery covering large areas.
• VIIRS (Visible Infrared Imaging Radiometer Suite): Enables fire detection during both day and night and provides thermal data.
• Landsat Series: Suitable for detailed post-fire analysis due to its 30-meter spatial resolution.
• Sentinel-2: Analyzes changes in vegetation cover before and after fires with 10-20 meter resolution.

Various spectral indices are used to evaluate post-fire areas. These indices help identify burned areas, measure vegetation loss, and monitor ecological recovery.

• NDVI (Normalized Difference Vegetation Index): Used to determine changes in vegetation cover before and after fires. As NDVI values decrease, the severity of damage increases.
• NBR (Normalized Burn Ratio): A specially developed index for detecting fire impacts; highly effective in delineating fire-affected areas.
• dNBR (Differenced Normalized Burn Ratio): Determines fire intensity by calculating the difference between pre- and post-fire NBR values. Areas with high dNBR values have suffered severe damage.
• BAI (Burned Area Index): A spectral index developed to detect burned regions, commonly used in initial post-fire analyses.

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Data derived from satellite imagery after forest fires are classified to analyze fire damage. Classification methods assist in categorizing the level of damage.

• Supervised Classification: Classification is performed using predefined sample areas. Support Vector Machines (SVM) and Random Forest algorithms are commonly used.
• Unsupervised Classification: Data are grouped based on their inherent differences. Algorithms such as K-means and ISODATA are employed.
• Thresholding Approach: Burned and unburned areas are identified based on specific spectral index values.

Early detection systems are of great importance in minimizing fire damage. Satellite sensors such as MODIS and VIIRS can identify the exact locations of fires in real time by detecting thermal anomalies. Additionally, machine learning techniques can pre-identify regions with high fire risk.

Satellite imagery is used to assess reforestation and ecological recovery efforts after fires. Indices such as NDVI and NBR enable tracking of vegetation regrowth in affected areas, allowing authorities to determine where intervention is most needed.

Forest fires release large amounts of carbon into the atmosphere. Satellite imagery and spectral analyses are used to calculate carbon emissions from fires and assess their impact on global climate change.