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

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.

Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories

“…Li & Suh [68] examined how the credibility of public social media is affected because of misleading or insignificant information posted just to attract attention. They [67] further supported his findings through careful examinations of various Facebook posts that showed that activeness and argument strength were the key factors of credibility. Nonetheless, social media has proven itself useful and effective in times of natural hazard-related disasters.…”

“…During the 2019-2020 bushfires, Australia used unmanned aerial vehicles (UAV) to locate and monitor the bushfires potential path. Ullah [67] stated that the bushfires can be mapped using geographical information systems (GIS) and remote sensing techniques, and subsequently, the hotpots can be monitored, and damages can be assessed. Additionally, the data provided by the UAV's combined with the Australian Bureau of Meteorology and GIS data can be used to develop mapping to identify patterns and fire development [67].…”

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

“…It focuses on developing computer programs that can access data, and use it to learn for themselves. ML has been used in various fields and domains of science such as agricultural sciences [48], concrete properties prediction [46], bushfire analyses [49], and flood detection and management [13,50], among others. Nevertheless, the applications of ML techniques are very limited or do not exist for mapping the groundwater contamination risk in developing countries, particularly Pakistan.…”

“…For example, ML has been used to construct environmental risk scores beyond standard linear models [55]. In addition, it has been used for engineering risk assessment [56,57], prediction of bioconcentration factors in fish and invertebrates [58], and general monitoring of the environment [49,59].…”

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

“…Moreover, data storage in the Cloud has also become a critical issue. To manage this, the authors in [ 28 , 29 , 30 ] presented the concept of fog computing. Fog computing is a layer of processing and communication nodes that relieves the Cloud of the burden of multitasking while managing large quantities of user requests.…”