Mapping oak wilt disease from space using land surface phenology

These codes are used to predict the present of symptomatic oak trees to oak wilt disease using land surface phenology (LSP) metrics (Fig 1). LSP metrics are derived from Analysis Ready Data (ARD) of Sentinel-2 using the processing workflow of FORCE (Fig. 2).

Fig 1. Temporal changes in the Chlorophyll/Carotenoid Index (CCI) of a pixel from an oak tree that died from oak wilt disease (a) and the expected behavior of land surface phenology metrics when oak tree conditions are compared with the surrounding healthy vegetation for a given phenological year (b).

Fig 2. Schematic workflow description.

Most of the data used in our manuscript that accompany these codes are available at the DRUM. Please follow the instructions described there for proper citation of the data if used.

Products available

We made available all the scenes of LSP (L3) and the predicted maps of probabilities (L4) for both states (i.e., Minnesota and Wisconsin). These can be accessible using a free Globus account.

Citing codes, manuscript, and data.

Codes

@codes{oak-wilt,
  author       = {Guzmán, J.A., Pinto-Ledezma, J., Frantz, D., Townsend, P.A., Juzwik, J., Cavender-Bares, J.},
  title        = {Mapping oak wilt disease from space using land surface phenology},
  month        = August,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.8275122},
  url          = {https://doi.org/10.5281/zenodo.8275122}
}

Manuscript

@article{oak-wilt,
  author       = {Guzmán, J.A., Pinto-Ledezma, J., Frantz, D., Townsend, P.A., Juzwik, J., Cavender-Bares, J.},
  title        = {Mapping oak wilt disease from space using land surface phenology},
  journal      = {Remote Sensing of Environment},
  year         = {2023},
  volume       = {pending},
  pages        = {pending}
  doi          = {pending}
}