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Article

Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020

by 1,2,*, 3,4 and 2
1
Department of Geography, The Hebrew University of Jerusalem, Jerusalem 91905, Israel
2
Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
3
Fenner School of Environment and Society, College of Science, Australian National University, Acton, Canberra, ACT 2601, Australia
4
School of Engineering, College of Engineering and Computer Science, Australian National University, Acton, Canberra, ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Daniela Stroppiana
Received: 30 July 2021 / Revised: 1 September 2021 / Accepted: 1 September 2021 / Published: 4 September 2021
(This article belongs to the Special Issue Fire in Human Landscapes)
The summer season of 2019–2020 has been named Australia’s Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia’s citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires (>100 km2) that burnt in Australia between September 2019 and mid-February 2020 (Australia’s Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define individual polygons of large fires based on the burn date from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires’ extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP; as detected by NASA’s MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia’s Black Summer. These included 205 forest fires with an average area of 584 km2 and 186 non-forest fires with an average area of 445 km2; 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km2. Australia’s Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63; median-adjusted R2 of 64.3%), followed by all forest fires (n = 205; median-adjusted R2 of 55.8%) and all non-forest fires (n = 186; median-adjusted R2 of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires’ extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R2 of 69.1%) and the change in VHI due to fire (median-adjusted R2 of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland–urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and diversity of ecosystems in Australia. View Full-Text
Keywords: remote sensing; wildfires; forest; Australia remote sensing; wildfires; forest; Australia
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MDPI and ACS Style

Levin, N.; Yebra, M.; Phinn, S. Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020. Fire 2021, 4, 58. https://doi.org/10.3390/fire4030058

AMA Style

Levin N, Yebra M, Phinn S. Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020. Fire. 2021; 4(3):58. https://doi.org/10.3390/fire4030058

Chicago/Turabian Style

Levin, Noam, Marta Yebra, and Stuart Phinn. 2021. "Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020" Fire 4, no. 3: 58. https://doi.org/10.3390/fire4030058

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