Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies

The emerald ash borer (EAB) is an exotic insect pest currently threatening ash species in the Great Lakes region. Because of the potential impact on forests in this area, multiple government agencies are currently focusing their efforts on developing new technologies to detect, monitor and control this insect pest. Previous work has shown that hyperspectral remote sensing technologies can produce detailed maps of forest health and species abundance on a landscape scale. This study examines the capability of a commercially available sensor (SpecTIR VNIR) to map ash decline due to exotic EAB infestations in Michigan and Ohio. A 6-term linear regression equation based on known stress- and chlorophyll-sensitive indices was able to predict decline on a continuous 0- to 10 scale with an r-squared of 0.71 and an average jackknifed residual of 0.61. Treated as an integer, decline was predicted to within one class with 97% accuracy. The ability of this instrument to assess decline below class 4 (when dieback and transparency reach levels first noticeable in the field) is based upon pre-visual reductions in chlorophyll content and function that are characteristic of early stress. The identification of early stress is critical in containing newly introduced exotics such as EAB. While this decline prediction technique is not stress- or species-specific, it will enable land managers to assess and monitor detailed forest health across the landscape.