Lagged climatic drivers of spongy moth (Lymantria dispar dispar) outbreaks revealed by satellite-based defoliation mapping.

These codes are used to generate datasets and figures for our in preparation paper. Below to a complete guide on how to run the code and which scripts need to be run to generate each figure.

Required packages

• earthengine-api
• Ultraplot
• Pandas
• GeoPandas
• Rasterio

Running the code

All python files are designed to be run from the command line. For javascript files, copy the code into the GEE code editor to run it. Figures are made in jupyter notebooks.

Most figures depend on the outputs from the basic pipeline. This can be run using the following sample commands:

1. python 0_maximum_separation.py --project=<project name> --state="New York" --submit
2. python 1_1_trends_theilsen.py --project=<project name> --state="New York" --submit
3. python 2_1_defoliation_theilsen.py --project=<project name> --state="New York" --submit
4. python 3_classify_denoise.py --project=<project name> --state="New York" --submit

This will generate defoliation maps for all of New York. In addition, for specific site (Mt. Pleasant, Arnot Forest, Allegheny Reservoir, and Turkey Hill), a few other pipelines need to be run for the method comparison.

1. python 1_2_trends_harmonic.py --project=<project name> --state="New York" --submit
2. python 2_2_defoliation_harmonic.py --project=<project name> --state="New York" --submit
3. python 2_3_defoliation_means.py --project=<project name> --state="New York" --submit

Main Figures

Figure 1

1. study_site_images.js
2. F1_SampleSiteShowcase.ipynb

Figure 2

1. F2_FullMethodsGraphs.ipynb

Figure 3

1. study_site_images.js
2. F3_SampleSiteDefol.ipynb

Figure 4

1. gridded_defoliation.js
2. F4_YearlyExtent.ipynb

Figure 5

1. gridded_defoliation.js
2. gridded_precipitation.js
3. gridded_temperature.js
4. F5_LagCorrelations.ipynb

Main Tables

Table 3

1. calculate_ROC.js

Table 4

1. defoliated_area.js

Table 5

1. gridded_defoliation.js
2. gridded_precipitation.js
3. gridded_temperature.js
4. T5_LagRegression.ipynb

Supplementary Figures

Figure S1

1. study_site_images.js
2. FS1_LoggingShowcase.ipynb

Figure S2

1. evaluate_defoliaiton.js
2. FS2_ExampleClassification.ipynb

Figure S3

1. aerial_survey_comp_maps.js
2. FS3_2019Map.ipynb

Figure S4

1. aerial_survey_comp_maps.js
2. FS4_AerialSurveyStatewide.ipynb

Figure S5

1. aerial_survey_comp_maps.js
2. FS5_AerialSurveyStudySites.ipynb

Figure S6

1. gridded_defoliation.py
2. gridded_precipitation.py
3. gridded_temperature.py
4. T5_LagRegression.ipynb (to create predictions)
5. FS6_LagMaps.ipynb

Supplementary Tables

Table S1-S4

1. calculate_ROC.js

Table S5

1. transition_matrix.py

Table S6/S7

1. T5_LagRegression.py