Developing a new fuel load mapping strategy using: USDA FS Forest Inventory and Analysis (FIA) protocols, Digital Photogrammetry, International Classification of Ecological Communities and Disturbance History

Abstract

Fire behavior models require many variables including above ground biomass or fuel load. Fuel loads include the amount of woody debris, duff, and litter that can carry a fire. The collection of these data can be very time consuming and expensive. The purpose of this project was to develop a quantitative, multi-purpose strategy for mapping fuel loads. Study sites were located in the coastal plain (Alligator River National Wildlife Refuge and Dare County Bombing Range) and the mountains (Coweeta Hydrologic Laboratory) of North Carolina. The approach was based on classifying vegetation types based on categories at the association level of the International classification of Ecological Communities (ICEC), using digital stereo imagery. The vegetation classes were combined with field measurements of biomass volumes obtained from USDA Forest Service Forest Inventory and Analysis Phase 3 (FIA P3) plots. The methodology of using modified ICEC association level vegetation maps created from digital photogrammetry, disturbance history, and FIA P3 data, show promise as an approach to fuel mapping for the following reasons:

(1) Softcopy photogrammetry coupled with ground truthing provides a high level of accuracy for mapping to the association level of the ICEC system.

(2) Fuel loads generated from the FIA P3 plots in the field differ from fuel loads estimated by standard fire models.

(3) Fuel loads within fuel size classes vary by vegetation type, and fuel size classes for some of the modified association level classifications had distinctive fuel loads. Disturbance history appears to play a significant role in explaining why fuel loads differ within associations and will help in creating more accurate fuel maps.

The ultimate goal of this research would be to use all FIA P3 plot data from across the country to generate an index of fuel load by ICEC association level vegetation classification and disturbance history. This could lead to a valuable multi-purpose tool for both land managers and researchers to predict, prevent and manage forest biomass for wildfire.