Jakarta, INTI - Artificial Intelligence (AI) technology is creating new opportunities for Indonesia to strengthen its efforts to mitigate forest and land fires, or karhutla, by predicting fire risks and detecting potential outbreaks before they spread.
The technology is increasingly relevant as forest and land fires continue to occur across several regions despite Indonesia’s ongoing hotspot monitoring, prevention measures, and firefighting efforts.
Analyses published by several monitoring organizations indicated that at least 218,000 hectares of forests and land showed signs of burning between January and August 2025.
Nearly half of the affected areas were located within plantation, mining, and energy concessions.
The National Disaster Management Agency (BNPB) has also reported that many forest and land fire incidents in Indonesia are linked to land-clearing activities and human negligence.
AI Can Predict Fire Risks
Indonesia has already been using satellite-based technologies through platforms such as SiPongi, along with hotspot monitoring conducted by the Meteorology, Climatology, and Geophysical Agency (BMKG) using MODIS and VIIRS satellite data to identify hotspots in near real time.
AI could take these monitoring capabilities further by analyzing multiple data sources, including weather conditions, air temperature, soil moisture, wind speed, vegetation conditions, historical fire patterns, and human activities to estimate the likelihood of a fire occurring.
This approach could enable authorities to respond before a fire breaks out by identifying high-risk areas in advance and preparing preventive measures accordingly.
AI-powered early fire detection systems have also been developed in several countries facing significant wildfire threats, including Canada following its severe wildfire season in 2023.
One example is SenseNet, a technology that combines smart cameras, gas sensors, satellite data, and predictive analytics to identify potential fire outbreaks at an early stage.
Technology to Strengthen Fire Mitigation
Forest and land fires not only cause environmental and ecosystem damage but can also generate widespread haze that affects public health, economic activities, and air transportation.
AI’s ability to process and analyze large volumes of data could help government agencies and field personnel identify areas with the highest fire risks and support faster, data-driven decision-making in real time.
The technology can complement the satellite systems and on-the-ground monitoring mechanisms already used in Indonesia’s forest and land fire management efforts.
With the right implementation, AI could help Indonesia shift its approach to forest and land fires from primarily responding to and extinguishing fires toward preventing them before they develop and spread on a larger scale.
Conclusion
The integration of Artificial Intelligence into forest and land fire management could provide Indonesia with a more proactive approach to disaster mitigation. By combining satellite imagery, weather data, environmental conditions, historical fire patterns, and human activity, AI can help identify high-risk areas and detect potential fires at an earlier stage.
As forest and land fires continue to pose environmental, health, and economic challenges, AI can complement existing monitoring and response systems by enabling faster, data-driven decision-making. With effective implementation and strong coordination among government agencies and field teams, AI could help Indonesia move beyond simply responding to fires toward preventing them before they spread and cause wider damage.
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