Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data

Brito, Thadeu; Pereira, Ana I.; Costa, Paulo Gomes da; Lima, José

http://hdl.handle.net/10198/30511

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Authors

Brito, Thadeu

Pereira, Ana I.

Costa, Paulo Gomes da

Lima, José

Abstract(s)

Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules’ best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and KNearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

Keywords

Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density

URI

http://hdl.handle.net/10198/30511

Citation

Brito, Thadeu; Pereira, Ana I.; Costa, Paulo; Lima, José (2024). Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 78-92. ISBN 978-3-031-53035-7.