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9 results for “bushfire”
Data from the National Prioritisation of Australian plant species after the 2019-2020 bushfires
<p>Data for 26,062 native Australian plant species assessed against ten post-fire recovery criteria. Details of criteria and methods available in Gallagher, R. V. (2020) <em>National prioritisation of Australian plants affected by the 2019–2020 bushfire season.</em> Report to the Commonwealth Dartement of Agriculture, Water and Environment. https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf </p>
Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime
<p>Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well-documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long-term impacts and patterns of ecosystem recovery. Here, we investigated the effects of recent bushfires, land-cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (<em>Synthemis</em> <em>eustalacta</em> (Burmeister, 1839)), which inhabits forests that have recently undergone severe burning. We used a temporally dynamic species distribution modeling approach that incorporated 20 years of community-science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. We also compared this to an approach that combines multiple temporally static models that use annual data. We found that for both approaches, fire-specific variables had negligible importance for the models, while percent of tree and non-vegetative cover were the most important. We also found that the dynamic model outperformed the static ones when evaluated with cross-validation. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat but no patterns of habitat expansion, contraction, or shifting. These results highlight not only the efficacy of dynamic modeling to capture spatiotemporal variables, such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records.</p>
Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime
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Predicted time‐to‐recover (days) for southeast Australia forested regions affected by the 2019–2020 bushfires.
<p>Model prediction of time-to-recover of southeast Australian eucalypt forests that burned during the megafires of 2019-2020. See the following publication for details regarding its creation.</p> <p>Rifai, S. W., De Kauwe, M. G., Gallagher, R. V., Cernusak, L. A., Meir, P., & Pitman, A. J. (2024). Burn severity and post‐fire<br>weather are key to predicting time‐to‐recover from Australian forest fires. Earth's Future, 12, e2023EF003780. https://doi.org/10.1029/2023EF003780</p>
Monash X band radar case for bushfire detection and a precipitation case used to examine uncertainty in ZDR
<p>Data sets from a mobile X-band radar used to examine uncertainty in ZDR and roHV . Data includes preciptation and wildfire ash clouds. </p> <p>The data format is <span>unprocessed, ungridded (spherical coordinates) and provided using the Eumetnet </span><a href="https://www.eumetnet.eu/wp-content/uploads/2019/01/ODIM_H5_v23.pdf" target="_blank" rel="noopener"><span>ODIMH5 model</span></a><span> of the HDF5 format</span> . </p> <p>The bublication will be May et al, J. Atmos. Oceanic. Tech titled: </p> <p><span>Accuracy of polarimetric radar Z<sub>DR</sub> estimates: Implications for the quantitative observation of meteorological and non-meteorological echoes</span></p>
Data from: Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine
<p><strong>Paper Abstract:</strong></p> <p>The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.mdpi.com/2072-4292/12/23/3942">Remote Sensing | Free Full-Text | Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine (mdpi.com)</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/KangarooIslandFireHistory_1988to2020">ZZMitch/KangarooIslandFireHistory_1988to2020: Code from "Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine" (RS, 2020) (github.com)</a></p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., Myint, S.W., 2020. Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing 12(23), <a href="https://doi.org/10.3390/rs12233942" rel="nofollow">https://doi.org/10.3390/rs12233942</a>.</p>
Modelling Bushfire Severity and Predicting Future Trends in Australia
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Bushfire Expert Panel - Fire-affected plant species data
<p>Dataset associated with assessing species for potential extinction risk status after the 2019-20 bushfire season as part of the process of formal assessment under the Australian Environmental Protection Biodiversity Conservation (EPBC) Act. </p>
Australia bushfires clear data
<p>Data from Australia 2020 bushfires in 11 languages</p>
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