2011
DOI: 10.3390/rs3092005
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Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests

Abstract: Pine 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 … Show more

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Cited by 25 publications
(17 citation statements)
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“…The Osceola National Forest in North Central Florida (93, 000 ha) has a frequent (2-5 years) PB programme with an approximate annual target of 14 % of its pine forest. For the 1998-2008 period, analysis of Landsat imagery showed PB reduced the likelihood of high burn severity up to 5 years posttreatment [54].…”
Section: Landscape-to Regional-scale Studiesmentioning
confidence: 99%
“…The Osceola National Forest in North Central Florida (93, 000 ha) has a frequent (2-5 years) PB programme with an approximate annual target of 14 % of its pine forest. For the 1998-2008 period, analysis of Landsat imagery showed PB reduced the likelihood of high burn severity up to 5 years posttreatment [54].…”
Section: Landscape-to Regional-scale Studiesmentioning
confidence: 99%
“…Logistic regression has demonstrated to be a powerful method to assess the performance of spectral indices with nominal or ordinal response data [69][70][71]. The ordinal regression in this study used the fire severity field classes as dependent variable and the spectral index values as independent variables.…”
Section: Logistic Regression Analysismentioning
confidence: 99%
“…[8][9][10][11]). However, assessments of trends in prescribed burning effectiveness across the southern U.S. are surprisingly limited to a few mostly case-based studies.…”
Section: Introductionmentioning
confidence: 99%