Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery

Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance detection methods using Landsat data, the capability exists for improvement in mapping methods, yet more information is needed to determine the accuracy of these methods for mapping forest disturbances and to quantify differences between these methods and single-date image classification methods. We compared single-date (using maximum likelihood classification) to multi-date (using time series of spectral indices) classification methods of Landsat imagery and investigated how detection accuracy changed with varying levels of mortality severity. For each method, we evaluated several bands and/or spectral vegetation indices and identified the one that resulted in the highest accuracy. A fine-resolution classified aerial image within the Landsat scene was used as reference data for evaluation and comparison between methods. For the single-date image classification, we achieved a 91.0% (kappa = 0.88) overall accuracy with 11.7% omission and 2.3% commission errors for the red stage (tree mortality) class using the tasseled cap transformation indices of brightness, greenness, and wetness. For the multi-date analysis, the Band5/Band4 anomaly produced the highest accuracy among spectral indices and resulted in a 89.6% (kappa = 0.86) classification accuracy with 12.6% omission and 7.1% commission errors for the red stage class. We compared accuracies between the best single- and multi-date methods across a range of tree mortality within a pixel. The multi-date method was more accurate at intermediate levels of tree mortality, whereas the single-date method was more accurate at high mortality levels. Our results indicate that Landsat-based mapping of forest disturbances that use either single-date or multi-date methods can result in high classification accuracy.