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Forest fuel mapping and strategic wi...
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University of Colorado at Boulder.
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Forest fuel mapping and strategic wildfire mitigation in the montane zone of Boulder County, Colorado.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Forest fuel mapping and strategic wildfire mitigation in the montane zone of Boulder County, Colorado./
作者:
Krasnow, Kevin.
面頁冊數:
78 p.
附註:
Adviser: Thomas T. Veblen.
Contained By:
Masters Abstracts International45-05.
標題:
Agriculture, Forestry and Wildlife. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1442945
Forest fuel mapping and strategic wildfire mitigation in the montane zone of Boulder County, Colorado.
Krasnow, Kevin.
Forest fuel mapping and strategic wildfire mitigation in the montane zone of Boulder County, Colorado.
- 78 p.
Adviser: Thomas T. Veblen.
Thesis (M.A.)--University of Colorado at Boulder, 2007.
This study quantifies the forest fuel of the montane zone of Boulder County, Colorado in an effort to perform landscapelevel fire simulations, aid in wildfire mitigation planning, and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 random stratified field plots, preexisting vegetation maps, and derived variables (distance from streams and potential solar radiation), predictive classification and regression tree (CART) models were created for four fuel parameters necessary for fire simulation (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56%--62% of the variability in forest fuels and were implemented in a GIS to create maps of each fuel parameter across the montane zone of Boulder County. Overall, distance to streams was the most powerful predictor variable, accounting for 18.6% of the explained variability in forest fuels. Forest type was the second most powerful predictor, describing 17% of the variability. Solar radiation and tree cover were next accounting for 14.5 and 13.5% of the variability, respectively. Potential soil moisture was an important limiting variable to forest understory development and was instrumental in accurately modeling the heterogeneity of forest fuels in Colorado's arid Front Range. Once created, the fuel maps were validated in FARSITE (Finney 1998) against two actual fires, burning 91.4% and 88.2% of the actual fire area in each respective simulation. LANDFIRE national fuel maps (www.landfire.gov) did not validate as well, burning 77.7% and 40.3% of the actual fire area respectively. The lower accuracy of the LANDFIRE fuel maps in these simulations is likely due to slower burning surface fuel models, higher canopy base heights, and lower canopy bulk density values when compared to the maps created in the current study. The fuel maps in the current study were then used in the Treatment Optimization Module (TOM) in FlamMap (Finney et al. 2005) to illustrate a possible distribution of strategically placed area treatments (SPLATS) in a portion of Lefthand Canyon watershed. The treatmentpriority landscape, a combination of resultant treatment grids from TOM, is offered as a potential approach for planning strategically placed fuel treatments for a range of problem fire conditions.Subjects--Topical Terms:
783690
Agriculture, Forestry and Wildlife.
Forest fuel mapping and strategic wildfire mitigation in the montane zone of Boulder County, Colorado.
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This study quantifies the forest fuel of the montane zone of Boulder County, Colorado in an effort to perform landscapelevel fire simulations, aid in wildfire mitigation planning, and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 random stratified field plots, preexisting vegetation maps, and derived variables (distance from streams and potential solar radiation), predictive classification and regression tree (CART) models were created for four fuel parameters necessary for fire simulation (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56%--62% of the variability in forest fuels and were implemented in a GIS to create maps of each fuel parameter across the montane zone of Boulder County. Overall, distance to streams was the most powerful predictor variable, accounting for 18.6% of the explained variability in forest fuels. Forest type was the second most powerful predictor, describing 17% of the variability. Solar radiation and tree cover were next accounting for 14.5 and 13.5% of the variability, respectively. Potential soil moisture was an important limiting variable to forest understory development and was instrumental in accurately modeling the heterogeneity of forest fuels in Colorado's arid Front Range. Once created, the fuel maps were validated in FARSITE (Finney 1998) against two actual fires, burning 91.4% and 88.2% of the actual fire area in each respective simulation. LANDFIRE national fuel maps (www.landfire.gov) did not validate as well, burning 77.7% and 40.3% of the actual fire area respectively. The lower accuracy of the LANDFIRE fuel maps in these simulations is likely due to slower burning surface fuel models, higher canopy base heights, and lower canopy bulk density values when compared to the maps created in the current study. The fuel maps in the current study were then used in the Treatment Optimization Module (TOM) in FlamMap (Finney et al. 2005) to illustrate a possible distribution of strategically placed area treatments (SPLATS) in a portion of Lefthand Canyon watershed. The treatmentpriority landscape, a combination of resultant treatment grids from TOM, is offered as a potential approach for planning strategically placed fuel treatments for a range of problem fire conditions.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1442945
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