Estimating the probability of mountain pine beetle red-attack damage

Accurate spatial information on the location and extent of mountain pine beetle infestation is critical for the planning of mitigation and treatment activities. Areas of mixed forest and variable terrain present unique challenges for the detection and mapping of mountain pine beetle red-attack damage, as red-attack has a more heterogeneous distribution under these conditions. In this study, mountain pine beetle red-attack damage was detected and mapped using a logistic regression approach with a forward stepwise selection process and a set of calibration data representing samples of red-attack and non-attack from the study area. Variables that were considered for inclusion in the model were the enhanced wetness difference index (EWDI) derived from a time series of Landsat remotely sensed imagery, elevation, slope, and solar radiation (direct, diffuse, and global). The output from the logistic regression was a continuous probability surface, which indicated the likelihood of red-attack damage. Independent validation data were used to assess the accuracy of the resulting models. The final model predicted red-attack damage with an accuracy of 86%. These results indicate that for this particular site, with mixed forest stands and variable terrain, remotely sensed and ancillary spatial data can be combined, through logistic regression, to create a mountain pine beetle red-attack likelihood surface that accurately identifies damaged forest stands. The use of a probabilistic approach reduces dependence upon the definition of change by the application of thresholds (upper and lower bounds of change) at the image processing stage. Rather, a change layer is generated that may be interpreted liberally or conservatively, depending on the information needs of the end user.