Damage Detection in Forest Fire Areas Using Satellite Imagery

Forest fires are natural disasters that cause severe damage to ecosystems. Accurate assessment of damage after Fire is critical for monitoring ecological recovery and planning reforestation initiatives to do. Satellite imagery is one of the most effective methods for fire detection, as it can cover vast areas in short time. Remote perception technologies enable the identification of fire-affected areas, classification of damage levels, and analysis of long-term ecosystem impacts long.

^{One image from US forest fires – Euro News}

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

• 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 areas affected by fires. These indices help identify burned zones, measure vegetation cover loss, and monitor ecological recovery.

• NDVI (Normalized Difference Vegetation Index): Used to determine changes in vegetation cover before and after fires. As the NDVI value decreases, 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): The difference between pre-fire and post-fire NBR values determines fire severity. Areas with high dNBR values have suffered severe damage.
• BAI (Burned Area Index): A spectral index developed to detect burned regions, particularly useful in initial post-fire assessments.

^{Spectral Index Application on Sentinel-2 Image of the Bodrum Forest Fire – Anadolu Agency}

Data derived from satellite imagery after forest fires are classified to analyze the extent of 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 inherent differences within the dataset. Algorithms such as K-means and ISODATA are applied.
• Thresholding Approach: Burned and unburned areas are identified based on specific spectral index values.

Early detection systems play a crucial importance in minimizing fire damage. MODIS and VIIRS like satellite sensors 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 processes after fires. Indices such as NDVI and NBR enable tracking of vegetation regrowth in affected areas, helping determine where intervention is most needed.

Large quantities of carbon are released into the atmosphere during forest fires. Satellite imagery and spectral analyses are used to calculate carbon emissions from fires and assess their impacts on global climate change.