Low-level Adelges tsugae infestation detection in New England through partition modeling of Landsat data

- Pixels with high NDVI and low MSI values had a high probability of being infested.
- Agreement between the classification and known infestations was greatest in conifer forests.
- 78.6% of test areas surveyed were found to be infested.
- New infestations were found nearly 19 km away from known reported infestations.
- Methods reported represent significant progress in detecting low-level infestations.

The hemlock woolly adelgid (HWA) (Hemiptera: Adelges tsugae Annand) is an invasive insect causing damage to Eastern (Tsuga canadensis (L.) Carr.) and Carolina (Tsuga caroliniana Engelm.) hemlock trees in the eastern United States. Maine and New Hampshire are currently the northernmost front of HWA's range. Developing methods to locate newly infested stands is paramount in the effort to monitor HWA range expansion; presently, the most reliable method of detection requires extensive on-the-ground manual surveying. Field surveys for invasive pests like HWA consume multiple resources, limiting the amount of area that can be surveyed, and the results often misrepresent the true extent or patchiness of an invasion. Satellite based remote sensing, using vegetation indices, enables us to detect insect driven changes in forest health at a landscape scale. Our objectives were to classify HWA infested hemlock stands along the infestation front and demonstrate how the classification product could be used to improve HWA survey planning. Our workflow consisted of 1) modeling hemlock habitat suitability using a Maximum Entropy algorithm; 2) developing decision tree rules to classify likely infested stands from a Landsat time series, using the habitat suitability model to mask out non-hemlock areas; and 3) field check the final classification product.The hemlock habitat suitability model attained an overall accuracy of 68.2%. Partitioning of leaf-on multi-year (1995-2013) Landsat 5 and 8 data resulted in seven probability of infestation classes with a training R2 = 0.782. Agreement between the classification and previously reported HWA infestations was 75.0% in conifer forests, 33.3% in mixed forests and 50.0% in deciduous forests. Agreement between the classification and test-survey locations was 78.6%; verified new infestations were detected as far as 19 km away from previously reported infestations. The methods presented outline how Landsat could be used to detect low-level HWA infestations. Classification products such as this ultimately could be used by federal and state agencies to target specific areas for efficient survey, suppression, and eradication efforts.