Baseline Study About Forest Fires in Indonesia Using VIIRS Nightfire Data
Keywords:
VIIRS nightfire, forest fires, machine learning, ANN, linear regression, IndonesiaAbstract
Indonesia is sensitive to the environmental crisis. When the authorities have minimum control over land conversion and the illegal burning of peatland for agriculture, it sometimes spreads to the natural forest, causes enormous forest fires, and burns the area more than was intended. Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire (VNF) uniquely records near-infrared and short-wave infrared data at night. VIIRS provided the data in KMZ format containing geographical location, ambient temperature, radiant heat, and source footprint. This detects not only heat from fires but also from light emissions, mining, and gas flares. This study conducts to analyze the characteristic of fires in Indonesia. Machine learning was applied to analyze the VNF data to predict fire spots when the gas flares appear from the fires. Forest fires in Indonesia mainly spread with temperatures of 500 0K – 1000 0K, with the temperature of the peak of fire can be more than 2500 0K. The high fire risk is during the dry season between June to November. The fire cases decreased from 2019-2023. This indicates that the authorities are taking strategic steps to control forest fires in Indonesia.
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