Conference paper Open Access

UAV-based photogrammetric 3D modelling and surveillance of forest wildfires

Krukowski, Artur; Vogiatzaki, Emmanouela

This article presents work performed in the frame of ongoing FP7-SEC project “Advanced Forest Fire Fighting” in areas of UAV-based 3D surveillance and 3D area mapping. Photogrammetric 3D scanning and modelling from H2020-SCAN4RECO project have been used for producing high-resolution models of incident areas from multi-spectral imaging. We also present a proprietary embedded sensor system used for detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire and DJI Matrice 600.

Results of real-life tests of both technologies during trials in Greece (May 2016) and in Spain (mid-November 2016) are reported. The performance of the proposed modelling approach was tested during both project trials where stacks of tree branches were set-up to be extinguished using aerial firefighting. The models based on a set of images taken by UAV’s HD-resolution camera during 10-minute flyby over a 1000 square meter area at an altitude of 40 meters was such that shapes of individual tree branches with sizes below 2 centimetres could be clearly seen in the model featuring over 100-million-point cloud and 10 million faces. Thermal images of same areas were captured with 640x480 resolution FLIR camera. The 3D models resulting from those images have been processed and are also presented here in. An embedded sensor device has been developed, aimed for mounting on a micro-UAV for detecting fire ignitions, with first version fitted onto DJI Inspire 1, while a reduced version has been developed for mounting on a smaller DJI Mavic Pro.

The concept of its operation is based on detecting changes in near-infrared spectrum corresponding to significant changes to ionised potassium and oxygen levels. The sensor uses a commercial STS-NIR micro spectrometer with either Raspberry PI revision 3 of PI-Zero 1.3 used to host acquisition software and an on-board WEB server used for transmitting the measurements as well as alerts to the C4I. Additional multi-constellation GPS receiver was added to the RASPI device to allow for precise positioning of incident areas without depending on the UAV positioning system. This way the device can be effortlessly deployed on any other flying or ground vehicle. The weight of the sensor node including spectrometer and rechargeable 2300mAh battery does not exceed 300 grams (1st version) with the latest release not exceeding 100 grams, allowing more than 4 to 6 hours of continuous operation.

The real-life validation test in Athens in May 2016 and in Spain in November 2016 have confirmed initial expectations and the anticipated performance of the sensor when used both on ground and on board of the DJI Inspire 1 and DJI Matrice UAV platforms.

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