Forest Condition Assessment

An accompanying code and data repository for the paper Pasquarella, V.J., Mickley, J.G., Barker Plotkin, A., MacLean, R. G., Anderson, R. M., Brown, L. M., Wagner, D. L., Singer, M. S., & Bagchi, R. (2021). Predicting defoliator abundance and defoliation measurements using Landsat-based condition scores. Remote Sensing in Ecology and Conservation. https://doi.org/10.1002/rse2.211

Here, we compare the relative predictive ability of various parameterizations of remote-sensed forest condition assessments on abundance and damage from a Lymantria dispar outbreak in southern New England.

Instructions:

• Data files should be stored inside the data directory.
• Scripts for Earth Engine workflow are stored in the earthengine_workflow directory and the repository can be accessed on Earth Engine here.
• Analyses should be stored in the analyses directory.
• Use the analysis template in analyses/ as a starting point.

List of Analyses:

• Defoliation Models - Comparison of defoliation models for forest condition assessment
• Using AIC weights to pick a winner - Can AIC model weights be used to pick a consensus best model parameterization?

Harmonic baseline results:

• Results can be viewed interactively via an Earth Engine App
• Mapped results are available as a Zenodo dataset