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Tayari S, Taghikhah F, Bharathy G, Voinov A. Designing a conceptual framework for strategic selection of Bushfire mitigation approaches. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118486. [PMID: 37413725 DOI: 10.1016/j.jenvman.2023.118486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Fires are an important aspect of environmental ecology; however, they are also one of the most widespread destructive forces impacting natural ecosystems as well as property, human health, water and other resources. Urban sprawl is driving the construction of new homes and facilities into fire-vulnerable areas. This growth, combined with a warmer climate, is likely to make the consequences of wildfires more severe. To reduce wildfires and associated risks, a variety of hazard reduction practices are implemented, such as prescribed burning (PB) and mechanical fuel load reduction (MFLR). PB can reduce forest fuel load; however, it has adverse effects on air quality and human health, and should not be applied close to residential areas due to risks of fire escape. On the other hand, MFLR releases less greenhouse gasses and does not impose risks to residential areas. However, it is more expensive to implement. We suggest that environmental, economic and social costs of various mitigation tools should be taken into account when choosing the most appropriate fire mitigation approach and propose a conceptual framework, which can do it. We show that applying GIS methods and life cycle assessment we can produce a more reasonable comparison that can, for example, include the benefits that can be generated by using collected biomass for bioenergy or in timber industries. This framework can assist decision makers to find the optimal combinations of hazard reduction practices for various specific conditions and locations.
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Affiliation(s)
- Sara Tayari
- School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
| | - Firouzeh Taghikhah
- School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; Discipline of Business Analytics, University of Sydney, Australia
| | - Gnana Bharathy
- School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Alexey Voinov
- Faculty of Engineering Technology, University of Twente, Netherlands
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Kantarcioglu O, Kocaman S, Schindler K. Artificial neural networks for assessing forest fire susceptibility in Türkiye. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Nasiri V, Sadeghi SMM, Bagherabadi R, Moradi F, Deljouei A, Borz SA. Modeling wildfire risk in western Iran based on the integration of AHP and GIS. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:644. [PMID: 35930117 DOI: 10.1007/s10661-022-10318-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
This study aimed at delineating the wildfire risk zones in a fire-prone region located in a rarely addressed area of western Iran (Paveh city) by assessing the potential of factors such as NDVI, topographic factors (elevation, slope, and aspect), land cover, and evaporation in explaining the fire occurrence probability. Analytic hierarchy process (AHP) and geographical information system (GIS) methods were used synergistically to integrate the mentioned factors into analysis, following an informed categorization of each factor based on the information on previous fire occurrence. In the AHP process, elevation and evaporation data were considered to be the most critical factors. It was found that the predicted wildfire risk areas were in agreement with past fire events by the use of the methodology proposed by this study. Accordingly, the study's final wildfire risk map indicated that approximately 64.7% of the study area is located in the high- and very high-risk zones. Land-use planners and decision-makers may use the developed map to setup and implement fire prevention strategies and enhance or develop the fire-surveillance logistics and infrastructure, including but not limited to the positions of fire watchtowers, fire lines, and fire sensors, with the aim to minimize potential fire impacts.
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Affiliation(s)
- Vahid Nasiri
- Faculty of Civil Engineering, Transilvania University of Brasov, Brasov, 900152, Romania
| | - Seyed Mohammad Moein Sadeghi
- Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Brasov, 500123, Romania.
- School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL, 32611, USA.
| | - Rasoul Bagherabadi
- Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, 1417643184, Iran
| | - Fardin Moradi
- Aerial Monitoring Research Group, Razi University, Kermanshah, 6714414971, Iran
| | - Azade Deljouei
- Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Brasov, 500123, Romania
- School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Stelian Alexandru Borz
- Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Brasov, 500123, Romania
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Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil. REMOTE SENSING 2022. [DOI: 10.3390/rs14133141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.
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