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Monitoring open landfill fires using integrated UAV and Sentinel-2 satellite imagery
Anjar Dimara Sakti1, Kamal Nur Fauzan2, Cokro Santoso3
1Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Indonesia.
Waste Management (New York, N.Y.)
|July 2, 2026
Summary
This study developed a machine learning model integrating UAV and satellite data to monitor open landfill fires. The model effectively mapped fire-affected zones, aiding in rapid risk assessment for waste management.
Area of Science:
- Environmental Science
- Remote Sensing
- Machine Learning
Background:
- Landfill fires present significant environmental and public health risks, especially in regions with inadequate waste management.
- Open burning at landfills contributes to air pollution and poses long-term ecological threats.
Purpose of the Study:
- To develop and evaluate a multi-sensor machine learning model for monitoring open landfill burning.
- To assess the effectiveness of integrating Unmanned Aerial Vehicle (UAV) imagery and Sentinel-2 satellite data for fire mapping.
- To create a fire-mitigation priority model incorporating burn severity and topographical data.
Main Methods:
- Utilized Random Forest (RF) and Gradient Tree Boosting (GTB) algorithms for classification of fire-affected zones.
- Integrated UAV imagery and Sentinel-2 satellite data for comprehensive spatial analysis.
- Developed a Burned Severity Index (BSI) combined with a Digital Surface Model (DSM) to analyze fire persistence in relation to elevation.
Main Results:
- Achieved high classification accuracies (80.55% and 81.31%) for fire mapping using UAV datasets.
- Demonstrated comparable cross-validated AUC values across different classifiers and data integration scenarios.
- Identified the influence of elevation on fire persistence through the fire-mitigation priority model.
Conclusions:
- UAV-satellite integration with machine learning offers a powerful approach for rapid landfill fire monitoring.
- The developed model shows potential for effective risk assessment in waste management systems.
- Future research should expand the model's scope to multiple sites and incorporate additional data sources for improved generalizability.
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