Year

2013

Degree Name

Bachelor of Science (Honours)

ANZSRC / FoR Code

05 ENVIRONMENTAL SCIENCES, 0502 ENVIRONMENTAL SCIENCE AND MANAGEMENT, 050205 Environmental Management

Department

School of Earth & Environmental Sciences

Advisor(s)

Laurie Chisholm

Abstract

Prescribed fires are conducted to reduce the risk of large wildfires and for ecological maintenance. Prediction of the severity of a prescribed fire is important to indicate the likelihood of objectives for fuel reduction and burn coverage being achieved. The severity of prescribed fire relies on many factors including fuel, weather, and topography. Live fuel moisture content (LFMC), referring to the moisture levels in living vegetation, has been shown to have a significant influence on fire development and spread. There remains a gap in research focusing on the LFMC and fire severity relationship.

This study aimed to explore the role of LFMC as a predictor of fire severity in prescribed fires. This study analysed LFMC and fire severity from nine different prescribed fires that were conducted in eucalypt dry sclerophyll forest of the Sydney Basin Bioregion. Two proven spectral indices calculated from Landsat 5 Thematic Mapper imagery were used to quantify LFMC (Normalized Difference Infrared Index) and fire severity (differenced Normalized Burn Ratio). Field-assessed fire severity data from 48 separate plots across five of the prescribed fire areas were used for validation of spectral data. Linear regression analyses, piecewise regression analyses, and Fisher’s Exact Tests were used to analyse LFMC and fire severity data.

The relationship between LFMC and fire severity appeared to be non-linear, with stronger evidence of a threshold-type relationship. In eucalypt dry sclerophyll forest, fire activity appears to be limited until LFMC falls below a threshold range of 118% - 112%. Below this range fire severity is not predictable from LFMC, but is determined by other factors, such as weather and dead fine fuel moisture.

The results of this study can potentially be used by fire management agencies, in conjunction with current methods, to make more robust predictions about fire activity within prescribed burns.

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