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Case Report

UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires

1
School of Built Environment, University of New South Wales, Kensington, Sydney 2052, Australia
2
School of Civil Engineering and Surveying, University of Southern Queensland, Springfield 4300, Australia
3
Faculty of Chemical Energy, University of New South Wales, Kensington, Sydney 2052, Australia
4
School of Computing Engineering and Mathematics, Western Sydney University, Locked Bag 1797, Penrith 2751, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Alistair M. S. Smith
Received: 16 June 2021 / Revised: 17 July 2021 / Accepted: 23 July 2021 / Published: 26 July 2021
Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan. View Full-Text
Keywords: bushfires; disaster management; unmanned aerial vehicles (UAVs); geographical information system (GIS); remote sensing; Victoria Australia bushfires; disaster management; unmanned aerial vehicles (UAVs); geographical information system (GIS); remote sensing; Victoria Australia
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MDPI and ACS Style

Munawar, H.S.; Ullah, F.; Khan, S.I.; Qadir, Z.; Qayyum, S. UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires. Fire 2021, 4, 40. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4030040

AMA Style

Munawar HS, Ullah F, Khan SI, Qadir Z, Qayyum S. UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires. Fire. 2021; 4(3):40. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4030040

Chicago/Turabian Style

Munawar, Hafiz S., Fahim Ullah, Sara I. Khan, Zakria Qadir, and Siddra Qayyum. 2021. "UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires" Fire 4, no. 3: 40. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4030040

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