Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests

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dc.contributor.authorMalone, Sparkle L.
dc.contributor.authorKobziar, Leda N.
dc.contributor.authorStaudhammer, Christina L.
dc.contributor.authorAbd-Elrahman, Amr
dc.contributor.otherState University System of Florida
dc.contributor.otherUniversity of Florida
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.coverage.spatialSouthern States
dc.coverage.spatialUnited States
dc.date.accessioned2018-11-20T18:52:28Z
dc.date.available2018-11-20T18:52:28Z
dc.date.issued2011-09-07
dc.description.abstractPine flatwoods forests in the southeastern US have experienced severe wildfires over the past few decades, often attributed to fuel load build-up. These forest communities are fire dependent and require regular burning for ecosystem maintenance and health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity, managers are still working to reintroduce fire to long unburned areas. Common perception holds that reintroduction of fire in long unburned forests will produce severe fire effects, resulting in a reluctance to prescribe fire without first using expensive mechanical fuels reduction techniques. To inform prioritization and timing of future fire use, we apply remote sensing analysis to examine the set of conditions most likely to result in high burn severity effects, in relation to vegetation, years since the previous fire, and historical fire frequency. We analyze Landsat imagery-based differenced Normalized Burn Ratios (dNBR) to model the relationships between previous and future burn severity to better predict areas of potential high severity. Our results show that remote sensing techniques are useful for modeling the relationship between elevated risk of high burn severity and the amount of time between fires, the type of fire (wildfire or prescribed burn), and the historical frequency of fires in pine flatwoods forests.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationMalone, S., Kobziar, L., Staudhammer, C., Abd-Elrahman, A. (2011): Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests. Remote Sensing, 3(9). DOI: https://doi.org/10.3390/rs3092005
dc.identifier.doi10.3390/rs3092005
dc.identifier.orcidhttps://orcid.org/0000-0001-9034-1076
dc.identifier.orcidhttps://orcid.org/0000-0002-6182-4017
dc.identifier.orcidhttps://orcid.org/0000-0002-5882-8498
dc.identifier.urihttp://ir.ua.edu/handle/123456789/4973
dc.languageEnglish
dc.language.isoen_US
dc.publisherMDPI
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectburn severity
dc.subjectremote sensing
dc.subjectdifferenced normalized burn ratios
dc.subjectfire frequency
dc.subjectpine flatwoods forest
dc.subjectfire model
dc.subjectwildfire
dc.subjectprescribed fire
dc.subjectPRESCRIBED FIRE
dc.subjectBOREAL FOREST
dc.subjectFLORIDA
dc.subjectVEGETATION
dc.subjectFLATWOODS
dc.subjectWILDFIRES
dc.subjectRATIO
dc.subjectEnvironmental Sciences
dc.subjectGeosciences, Multidisciplinary
dc.subjectRemote Sensing
dc.subjectImaging Science & Photographic Technology
dc.subjectEnvironmental Sciences & Ecology
dc.subjectGeology
dc.titleModeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forestsen_US
dc.typetext
dc.typeArticle
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