Analysis of Remotely Piloted Aircraft Payload for Oil Spill Detection

Abstract Operational monitoring of large sea aquatorium areas with the aim of detecting and controlling oil pollution is now carried out using various technological systems, such as satellite remote sensing, sea-going vessels, various aircraft and remotely piloted aircraft (RPA). Currently, the use of RPA for the fulfilment of monitoring tasks in the aquatorium is being intensively developed and can eliminate problems of remote sensing performed by satellites and piloted aircraft, such as short presence in the monitoring area, very long delay of information (up to 48 hours) and low quality of imagery. This paper presents mathematical modelling of RPA multi-sensor pay-loads for oil spill detection, monitoring and control. Information obtained from payload sensors is critical for increasing effectiveness of detection and monitoring of oil spills. Nowadays, many types of sensors are used for oil spill detection and monitoring. The most common sensors for detection of oil pollution are optical, multispectral, hyperspectral, thermal and laser fluorometers. Some oil pollution detection sensors have limitations, such as false alarm, only daytime operation, weather restrictions. Airborne remote sensors cannot provide all information required for detection of and response to oil spills, and water quality monitoring in the spill area. A model for selecting sensors for multi sensor payload that will make it possible to optimize the application of RPA for oil spill detection was developed. The RPA payload can be increased/reduced to the greatest possible extent with the help of different types of equipment at various parameters. The mathematical model of the integrated payload considers detection capability of sensors, weather conditions, sensor characteristics, and false alarm rate. The optimal multi-sensor payload will optimize the application of RPA for oil spill detection and monitoring.