Data and code from: 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.