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Abstract BackgroundPrescribed fire is an essential tool employed by natural resource managers to serve ecological and fuel treatment objectives of fire management. However, limited operational resources, environmental conditions, and competing goals result in a finite number of burn days, which need to be allocated toward maximizing the overall benefits attainable with fire management. Burn prioritization models must balance multiple management objectives at landscape scales, often providing coarse resolution information. We developed a decision-support framework and a burn prioritization model for wetlands and wildland-urban interfaces using high-resolution mapping in Everglades National Park (Florida, USA). The model included criteria relevant to the conservation of plant communities, the protection of endangered faunal species, the ability to safely contain fires and minimize emissions harmful to the public, the protection of cultural, archeological, and recreational resources, and the control of invasive plant species. A geographic information system was used to integrate the multiple factors affecting fire management into a single spatially and temporally explicit management model, which provided a quantitative computations-alternative to decision making that is usually based on qualitative assessments. ResultsOur model outputs were 50-m resolution grid maps showing burn prioritization scores for each pixel. During the 50 years of simulated burn unit prioritization used for model evaluation, the mean burned surface corresponded to 256 ± 160 km2 y−1, which is 12% of the total area within Everglades National Park eligible for prescribed fires. Mean predicted fire return intervals (FRIs) varied among ecosystem types: marshes (9.9 ± 1.7 years), prairies (7.3 ± 1.9 years), and pine rocklands (4.0 ± 0.7 years). Mean predicted FRIs also varied among the critical habitats for species of special concern:Ammodramus maritimus mirabilis(7.4 ± 1.5 years),Anaea troglodyta floridalisandStrymon acis bartramibutterflies (3.9 ± 0.2 years), andEumops floridanus(6.5 ± 2.9 years). While mean predicted fire return intervals accurately fit conservation objectives, baseline fire return intervals, calculated using the last 20 years of data, did not. Fire intensity and patchiness potential indices were estimated to further support fire management. ConclusionsBy performing finer-scale spatial computations, our burn prioritization model can support diverse fire regimes across large wetland landscape such as Everglades National Park. Our model integrates spatial variability in ecosystem types and habitats of endangered species, while satisfying the need to contain fires and protect cultural heritage and infrastructure. Burn prioritization models can allow the achievement of target fire return intervals for higher-priority conservation objectives, while also considering finer-scale fire characteristics, such as patchiness, seasonality, intensity, and severity. Decision-support frameworks and higher-resolution models are needed for managing landscape-scale complexity of fires given rapid environmental changes.
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Pixels to pyrometrics: UAS-derived infrared imagery to evaluate and monitor prescribed fire behaviour and effects
BackgroundPrescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. AimsWe developed methods for processing uncrewed aircraft systems (UAS) imagery into spatially explicit pyrometrics, including measurements of fuel consumption, rate of spread, and residence time to quantitatively measure three prescribed fires. MethodsWe collected infrared (IR) imagery continuously (0.2 Hz) over prescribed burns and one experimental calibration burn, capturing fire progression and combustion for multiple hours. Key resultsPyrometrics were successfully extracted from UAS-IR imagery with sufficient spatiotemporal resolution to effectively measure and differentiate between fires. UAS-IR fuel consumption correlated with weight-based measurements of 10 1-m2 experimental burn plots, validating our approach to estimating consumption with a cost-effective UAS-IR sensor (R2 = 0.99; RMSE = 0.38 kg m−2). ConclusionsOur findings demonstrate UAS-IR pyrometrics are an accurate approach to monitoring fire behaviour and effects, such as measurements of consumption. Prescribed fire is a fine-scale process; a ground sampling distance of <2.3 m2 is recommended. Additional research is needed to validate other derived measurements. ImplicationsRefined fire monitoring coupled with refined objectives will be pivotal in informing fire management of best practices, justifying the use of prescribed fire and providing quantitative feedback in an uncertain environment.
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- PAR ID:
- 10643915
- Publisher / Repository:
- DOI PREFIX: 10.1071
- Date Published:
- Journal Name:
- International Journal of Wildland Fire
- Volume:
- 33
- Issue:
- 11
- ISSN:
- 1049-8001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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