Date of Award:

5-2012

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Wildland Resources

Committee Chair(s)

R. Douglas Ramsey

Committee

R. Douglas Ramsey

Committee

James N. Long

Committee

Michael Jenkins

Abstract

Fire fuel inventory processes are customarily labor intensive endeavors. There is a growing need for an increase in accuracy of these inventories at a landscape level, due in large part to the ever increasing development of Wildland Urban Interface (WUI). More accurate inventory and mapping of wildland fuels will facilitate a more accurate simulation of wildfire behavior and analysis of fire behavior given a myriad of fuels treatments. This paper examines one approach to inventorying fire fuels at a landscape level and developing fuel model maps to be utilized in landscape level fire behavior simulations for use by land managers in making fire and fuels related decisions. Three dominant vegetation classes are examined: Juniper, Gambel oak, and Big Sagebrush. Data was gathered and analyzed for Army Garrison Camp W.G. Williams, Utah. IKONOS multispectral data was used to develop several spectral derivatives such as texture and Normalized Difference Vegetation Index (NDVI). These coupled with gradient data were used to develop a regressive prediction model, to predict aboveground biomass for use in fuel model assignment. It was shown that this approach was ineffective in assessing fuel load and developing fuel maps. Several other approaches are discussed as alternatives.

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Comments

Every year, millions of acres of forest and rangeland are burned in prescribed burns as well as wildfires. The costs associated with wildfires may be some of the largest we face as a society both in material goods and in life. The importance of managing fire fuels has increased with the development of the wildland-urban interface. With this increased emphasis has come the development of tools to assess, map, and simulate fuel maps at a landscape level. These fuel maps are then input into computer-aided wildfire simulation models that are used by land managers in the planning process. A current challenge for land managers is to find efficient ways to measure the amount and structure of fire fuels on a landscape level. Fuel models are one of the required inputs for software that mathematically computes wildfire rate of spread. Various methods have been used to develop fuel maps. It is the objective of this thesis to develop a method by which fuel models can be predicted and mapped on a landscape level through utilization of remotely sensed data. The proposed process for this method is: 1) develop landcover classification, 2) assess data analysis approaches for use in creation of predictive regression models, 3) correlation of data results to Natural Fuels Photo Series, and 4) translate Natural Fuels Photo Series classifications into fuel models described by Scott and Burgan.

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