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UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

Author

Listed:
  • Fahim Ullah

    (School of Civil Engineering and Surveying, University of Southern Queensland, Springfield 4300, Australia)

  • Sara Imran Khan

    (Faculty of Chemical Energy, University of New South Wales, Sydney 2052, Australia)

  • Hafiz Suliman Munawar

    (School of Built Environment, University of New South Wales, Sydney 2052, Australia)

  • Zakria Qadir

    (School of Computing Engineering and Mathematics, Western Sydney University, Locked Bag 1797, Penrith 2751, Australia)

  • Siddra Qayyum

    (School of Built Environment, University of New South Wales, Sydney 2052, Australia)

Abstract

Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.

Suggested Citation

  • Fahim Ullah & Sara Imran Khan & Hafiz Suliman Munawar & Zakria Qadir & Siddra Qayyum, 2021. "UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires," Sustainability, MDPI, vol. 13(18), pages 1-32, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10207-:d:634302
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    References listed on IDEAS

    as
    1. Fahim Ullah & Samad M. E. Sepasgozar, 2020. "Key Factors Influencing Purchase or Rent Decisions in Smart Real Estate Investments: A System Dynamics Approach Using Online Forum Thread Data," Sustainability, MDPI, vol. 12(11), pages 1-36, May.
    2. Fahim Ullah & Samad M. E. Sepasgozar & Changxin Wang, 2018. "A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms," Sustainability, MDPI, vol. 10(9), pages 1-44, September.
    3. Salman Atif & Muhammad Umar & Fahim Ullah, 2021. "Investigating the flood damages in Lower Indus Basin since 2000: Spatiotemporal analyses of the major flood events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2357-2383, September.
    4. Hafiz Suliman Munawar & Fahim Ullah & Siddra Qayyum & Sara Imran Khan & Mohammad Mojtahedi, 2021. "UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
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    Cited by:

    1. Kamran Iqbal & Hafiz Suliman Munawar & Hina Inam & Siddra Qayyum, 2021. "Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions," Sustainability, MDPI, vol. 13(23), pages 1-20, November.

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