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Linda Joyce
Rocky Mountain Research Station
240 West Prospect
Fort Collins, CO 80526
Phone: 970-498-2560
ljoyce@fs.fed.us
 United States Department of Agriculture Forest Service.USDA logo which links to the department's national site.Forest Service logo which links to the agency's national site.
USE OF REMOTE SENSING TO EXAMINE DISEASE EFFECTS ON FUEL PATTERNS


Background -- Forest diseases are among the least understood and possibly most underestimated causes of wild land fuels. Manipulating (mediating or enhancing) forest diseases could be a useful long-term strategy for managing fire risk, but the integrated models needed to characterize and predict the influence of disease on fire behavior, and to guide disease management activities are few.

Needs -- Forest pathologists and fire managers need tools that enhance collaboration and communication in decision making. A landscape scale model for spatial management of diseases is needed that integrates fuel and pathogen probability distributions with fire spread predictive models. To be a practical tool, this model needs to be based on remote sensing coupled to data from ground plots and spatial analyses. These models would be useful in selecting and prioritizing prescriptive disease management activities aimed at controlling fire risks, and in determining where to do them.

Approach -- This study has involved four phases: 1) Landsat TM imagery combined with field assessments, and spatial analyses were used to develop landscape scale predictive spatial models for several types of fuels, and to generate probability distributions of pathogens and other types of disturbances; 2) These models were then integrated into an existing fire behavior model to predict the influence of different diseases and their locations on fire spread and impact; 3) Based on simulations using these models, methods of determining the relative importance of different diseases compared to other disturbances across the landscape was developed to help prioritize management options; 4) Various methods of collaborating, communicating, and integrating the technologies developed here into the operational management decision making process are being examined.


Products/deliverables -- Products include: 1) Predictive spatial models showing varying conditions of various fuel types, different pathogens, and other kinds of disturbances within stands across the entire forest were generated; 2) These models were integrated into the fire behavior model, FARSITE, and used to run simulations. 3) A method of estimating the relative importance of diseases compared to other types of disturbances showed that root diseases caused 32% of the total fuel load, bark beetles (21%), lightning damage (11%), wind damage (10%), canker diseases (10%), and others in a ponderosa stand under endemic conditions; 4) The integrated models were transferred to managers on a National Forest for beta testing, which we hope will improve the chances that the final product will better match users needs. Unexpectedly, the most difficult part of this multiphase study has been the technology transfer phase.


Tools/applications -- Tools developed here offer a practical way to monitor fuel abundance and distribution from sub-stand to landscape scales, and to predict how fuel distribution and fire spread is influenced by changing disease dynamics and the application of different disease management activities. Application of these models could help managers develop prescriptions for mediating fire risk by managing spatial patterns of fuel generating tree diseases and other types of disturbances.

Citation: Reich, R.M.; Lundquist, J.E.; Bravo, V.A. 2004. Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire. 13: 1-11

 


 

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