Citation
Jones, Chris; Song, Conghe H.; & Moody, Aaron (2015). Where's Woolly? An Integrative Use of Remote Sensing to Improve Predictions of the Spatial Distribution of an Invasive Forest Pest the Hemlock Woolly Adelgid. Forest Ecology and Management, 358, 222-229.Abstract
Non-native pests and pathogens present serious challenges to the management of forested ecosystems around the world. Early detection of pest and pathogen invasions may allow timely control and prevention methods to be implemented. Species distribution models (SDMs) and remote sensing (RS) methods have both been used effectively to determine locations of pest and pathogen damage. However, previous work integrating these two methods has rarely used RS metrics that have biological meaning. We use RS difference indices that show changes in forest cover from defoliation in order to map Hemlock Woolly Adelgid (HWA), Adelges tsugae, locations using MaxEnt in the Delaware Water Gap National Recreation Area (DWGNRA). Brightness, greenness, wetness, and Normalized Difference Vegetation Index (NDVI) were calculated from Landsat Thematic Mapper (TM) images for December 1982 and 2010. A difference for each index was created by subtracting the 1982 value from the 2010 value. We compared two models, one using difference indices and the other using 2010 indices along with other ancillary data layers, to determine if the more complicated but more biologically relevant difference indices were necessary for improved model performance. Variables with low importance were removed from both models, leaving NDVI, Wetness, soil, and elevation in the two final models. The difference model had an improvement in accuracy of three percent, across a number of threshold values. Despite this small difference in accuracy, however, the infected area predicted by the difference model (5.1% of total area) was approximately 1/2 of that predicted by the single year model (9.6% of total area). These results suggest that using remote sensing difference indices improves model accuracy slightly in terms of errors of omission, but also decreases predicted area of forest infestation by about 50%, suggesting that errors of commission would be substantially reduced using the difference approach. This method can provide forest managers more accurate information on the best locations to sample and treat.URL
https://doi.org/10.1016/j.foreco.2015.09.013Reference Type
Journal ArticleYear Published
2015Journal Title
Forest Ecology and ManagementAuthor(s)
Jones, ChrisSong, Conghe H.
Moody, Aaron