Spatial databases and techniques to assist with prescribed fire management in the south-east Queensland bioregion
Sanjeev Kumar Srivastava A B F , Tom Lewis B C , Linda Behrendorff D and Stuart Phinn E
+ Author Affiliations
- Author Affiliations
A Genecology Research Centre, University of the Sunshine Coast, Sunshine Coast, Qld 4556, Australia.
B School of Science and Engineering, University of the Sunshine Coast, Sunshine Coast, Qld 4556, Australia.
C Department of Agriculture and Fisheries, Sippy Downs, Qld 4556, Australia.
D School of Agriculture and Food Science, The University of Queensland, Gatton, Qld 4343, Australia.
E School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld 4072, Australia.
F Corresponding author. Email: SSRIVast@usc.edu.au
International Journal of Wildland Fire - https://doi.org/10.1071/WF19105
Submitted: 11 July 2019 Accepted: 4 October 2020 Published online: 29 October 2020
Abstract
This paper identifies key fire history and fire-related spatial databases that can be utilised for effective planning and assessment of prescribed burns in south-eastern Queensland. To ensure that appropriate fire regimes are maintained for specific management objectives (e.g. biodiversity conservation or risk management), and to assist fire managers with planning prescribed fire and post-fire assessments, we describe, using case studies and existing tools, the application of remote sensing data and derived burned area products together with field data to potentially: (1) improve mapping of fire-prone areas; (2) improve the accuracy of mapping burned areas; (3) monitor temporal changes in fuel structure; and (4) map post-fire severity. This study utilised data collected from aerial and satellite-based multispectral, microwave and laser (LiDAR) sensors. There are several spatial databases and analytical methods available that are not currently used by fire management agencies in this region. For example, the methods to estimate fuel, such as LiDAR, are underutilised and unburned patches within a burned area are not routinely mapped. Better use of spatial datasets could lead to an improved understanding of variables such as fuel status, resulting in more efficient use of fire management resources.
Keywords: burned-area mapping, fire regimes, fuel mapping, prescribed fire management, regional, remote sensing of fire, southeast Queensland.
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