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Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data
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Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density
Contexto Educativo
Citação
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.
Editora
Springer Nature