Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-m multispectral imagery

Abstract

The objective of this study was to gauge the effectiveness of using SPOT-5 10-m multispectral imagery to detect and map red-attack damage for an area near Cranbrook, British Columbia, Canada. A logistic regression model was used to incorporate SPOT imagery with elevation and associated derivatives for redattack detection and mapping. Separate independent sets of calibration and validation data, collected via a detailed aerial survey, were used to train the classification algorithm and vet the output maps of red-attack damage. The output from the logistic regression model was a continuous surface indicating the probability of red-attack damage. Using a greater than 50% probability threshold, red-attack was mapped with 71% accuracy (with a 95% confidence interval of ?9%). This level of accuracy is comparable to that achieved with Landsat single-date imagery in an area with similar levels of infestation. If a synoptic view of mountain pine beetle red-attack damage at the landscape level is required, and if Landsat data are unavailable, SPOT-5 10-m multispectral imagery may be considered an alternative data source, albeit an expensive one, for detecting and mapping mountain pine beetle red-attack damage.