Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population success, three study areas in the western United States that have experienced recent MPB outbreaks were used for evaluation. Pine density estimates for each study area were compared to measures of cumulative MPB-caused pine mortality summarized from annual Aerial Detection Surveys (ADS). ADS data provide spatial and temporal representations of MPB-caused pine mortality collected by observers in fixed wing aircraft and are the most readily available estimates of landscape-scale impacts of MPB. Regression analyses using LANDFIRE ecological systems classifications (EVTs) as units of analysis showed that the best pine density estimates explained 75 to 98% of cumulative MPB-caused tree mortality. LANDFIRE EVTs, which provide an index of the plant communities growing in a particular 30-m cell, effectively delineate distinct vegetation types that are meaningful suitability indicators for MPB-caused tree mortality. Our analyses suggested that available geospatial vegetation datasets derived from field data
and remotely sensed imagery are useful for producing spatially explicit measures of pine density for use in landscape-level modeling of MPB dynamics.

Full citation

Crabb B.A., Powell J.A., Bentz B.J. (2012). Development and Assessment of 30-Meter Pine Density Maps for Landscape-Level Modeling of Mountain Pine Beetle Dynamics. Research Paper RMRS-RP-93WWW. Fort Collins, CO. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.