EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predicting forest fire in Indonesia using remote sensing data

Suwei Yang1, Kuldeep S Meel1, and Massimo Lupascu2
Suwei Yang et al.
  • 1National University Of Singapore, School of Computing, Singapore
  • 2National University Of Singapore, NUS Faculty of Arts and Social Sciences, Department of Geography

Over the last decades we are seeing an increase in forest fires due to deforestation and climate change. In Southeast Asia, tropical peatland forest fires are a major environmental issue having a significant effect on the climate and causing extensive social, health and economical impacts. As a result, forest fire prediction has emerged as a key challenge in computational sustainability. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System (Natural Resources Canada), are based on handcrafted features and use data from instruments on the ground. However, data from instruments on the ground may not always be available. In this work, we propose a novel machine learning approach that uses historical satellite images to predict forest fires in Indonesia. Our prediction model achieves more than 0.86 area under the receiver operator characteristic(ROC) curve. Further evaluations show that the model's prediction performance remains above 0.81 area under ROC curve even with reduced data. The results support our claim that machine learning based approaches can lead to reliable and cost-effective forest fire prediction systems.

How to cite: Yang, S., Meel, K. S., and Lupascu, M.: Predicting forest fire in Indonesia using remote sensing data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13191,, 2020

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