Document Type
Article
Publication Date
10-5-2022
Department
College of Forest Resources and Environmental Science
Abstract
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.
Publication Title
PLoS ONE
Recommended Citation
Monahan, W.,
Arnspiger, C.,
Bhatt, P.,
An, Z.,
Krist, F.,
Liu, T.,
Richard, R.,
Edson, C.,
Froese, R.,
Steffenson, J.,
Lammers, T.,
&
Frosh, R.
(2022).
A spectral three-dimensional color space model of tree crown health.
PLoS ONE,
17(10).
http://doi.org/10.1371/journal.pone.0272360
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16486
Creative Commons License
This work is licensed under a
Creative Commons Public Domain Dedication 1.0 License.
Version
Publisher's PDF
Publisher's Statement
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Publisher’s version of record: https://doi.org/10.1371/journal.pone.0272360