Using insect phenology and host preferences, weather and climate records, and remote sensing to detect forest canopy disturbances

Moderate, non-stand-replacing forest disturbances are common on the landscape but remain more difficult to detect by traditional remote sensing methods than severe, sudden disturbances such as fire and harvest. Insect-caused defoliation, or slow forest decline due to some insects and forest pathogens, typically combines specific agents with their preferred host species. Changes in seasonal phenological patterns are often among the first indicators of such a disturbance. We have developed a computational methodology that combines satellite-derived remote sensing image time series (e.g., Landsat and Sentinel) with weather and climate records to estimate expected phenology in forest areas, thus identifying anomalies in remote sensing observations. These anomalies can indicate new disturbance events and help track the progression of a persistent disturbance, such as insect colonization and establishment, on the forest landscape. We combine these results with an expert-informed pest decision matrix of known native and invasive forest pest species, including their preferred forest hosts and seasonal activity, and new USFS FIA maps of forest species biomass. This collection of evidence supports the detection of new and ongoing moderate forest disturbances and our ability to differentiate between likely causes among native and invasive biotic agents (e.g., spongy moth, emerald ash borer, spruce budworm, beech bark disease) and abiotic influences (e.g., heatwaves, flash droughts, frost, windstorms). Examples of our recent work in northern Wisconsin, Minnesota, and Michigan will be used to illustrate the outcomes of this methodology