A spectral three-dimensional color space model of tree crown health.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89-0.90; median Kappa 0.85-0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

The relationship between tree mortality from a pine beetle epidemic and increased dissolved copper levels in the upper Big Thompson River, Colorado.

Bark beetle outbreaks in the Rocky Mountains caused substantial tree mortality starting in the late 1990s, and continued into the 2000s, with the most severe mortality occurring from 2002 to 2012. Over the same time period, concentrations of dissolved copper in the Big Thompson River (BTR), Colorado, USA, increased significantly and are high enough to negatively affect aquatic life. We examined correlations between dissolved copper and tree mortality in the BTR. Two sites, one consisting of water from the western side of the continental divide and one consisting of water from the eastern side, demonstrated a positive relationship between percentage tree mortality and dissolved copper. The relationships were similar except that the best relationship occurred with a 3-year lag between tree mortality and subsequent dissolved copper levels at the eastern site and with a 5-year lag at the western site. The differential time lag is potentially the result of different levels of carbon in the soil in the watersheds associated with each site because carbon can affect copper mobility. Our results suggest that bark beetle-induced tree mortality may contribute significantly to dissolved copper levels in the BTR.