Determining Forest Fire Position from UAV Photogrammetry using Color Filtration Algorithm

Abdul Muid - Universitas Tanjungpura, Jl. Prof. Dr. Hadari Nawawi, Pontianak 78124, Indonesia
Maria Evita - Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia
Nina Aminah - Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia
Maman Budiman - Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia
Mitra Djamal - Institut Teknologi Bandung, Jl. Ganesha 10 Bandung 40132, Indonesia

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Forest fires frequently happen worldwide, especially in the dry season. A forest fire early warning system (EWS) is needed to prevent this disaster. The main part of EWS is the hotspot detection system. On the other side, Unmanned Aerial Vehicle (UAV) technology offers an alternative solution to detect the hotspot for poor satellite image processing accuracy. Remote sensing techniques with UAV working drones are progressively challenging. Drones can provide results in 2D and 3D images with high resolution and real-time. Therefore, in this research, we have used a photogrammetry application from the number of images collected by a UAV with an optimum flight plan for the mission to determine the location of the forest fire. This paper describes remote sensing experiments using drones to detect land fires. The experiment was carried out using a quadcopter drone of the DJI Phantom 4 Pro. The photos are processed using Agisoft Metashape Professional image processing software and become a 2D image. These images captured a fire simulation in a known location. After a high resolution (GSD – Ground Sampling Distance – 0.87cm/px) orthophoto had been generated, a color filtration algorithm detected a hotspot to detect a fire at the exact location. The results are almost zero deviation of longitude and latitude from the real location with 1.44 m2 and 1.06 m2 fire area from 2 experiments. This algorithm program has TPR and FPR are 0,78 and 0, respectively. Further research can develop an EWS with a combination of UAV and Wireless Sensor Networks.


Unmanned Aerial Vehicle (UAV); Forest Fires Detection; Colour Filter Algorithm; High Resolution Orthophoto

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