mikoontz / remote-sensing-resistance

Does heterogeneity in forest structure make a forest resistant to wildfire? That is, does greater heterogeneity decrease wildfire severity when a fire inevitably occurs? A collaborative effort co-authored by: Michael J. Koontz, Malcolm P. North, Chhaya M. Werner, Stephen E. Fick, and Andrew M. Latimer

Remote sensing resistance

This repository represents the entirety of the "remote sensing resistance" project which seeks to address the question: Does heterogeneity in forest structure make a forest resistant to wildfire? That is, does greater heterogeneity decrease wildfire severity when a fire inevitably occurs?

We rely on Google Earth Engine and R as our GIS. All Earth Engine code is in the ee-remote-sensing-resistance folder, which is itself a repository housed in Google's version control system. We mirror it here such that this repository remains a complete representation of the project.

The written manuscript is in the ms folder. The primary document is a .Rmd file, which is rendered as a .docx and as a .pdf. The .pdf is viewable on GitHub. I'm hoping this setup will allow programming-savy co-authors to interact with the code and the writing through the same version control framework, but also allow some flexibility for a more-typical "track changes on a Microsoft Word document" sort of relationship with the work.

Data sources

Jepson Ecoregion data for delineating "Sierra Nevada"

"Jepson Flora Project (eds.) 2016. Jepson eFlora, http://ucjeps.berkeley.edu/eflora/ [accessed on Mar 07, 2016]"

Contact Dr. David Baxter for a GIS layer.

Fire Return Interval Departure data source for designating "yellow pine/mixed-conifer" (ypmc)

https://www.fs.usda.gov/detail/r5/landmanagement/gis/?cid=STELPRDB5327836

Composite Burn Index (CBI) data sources

Zhu, Z.; C. Key; D. Ohlen; N. Benson. 2006. Evaluate Sensitivities of Burn-Severity Mapping Algorithms for Different Ecosystems and Fire Histories in the United States. Final Report to the Joint Fire Science Program, Project JFSP 01-1-4-12, October 12, 2006. 35pp. link

Sikkink, Pamela G.; Dillon, Gregory K.; Keane,Robert E.; Morgan, Penelope; Karau, Eva C.; Holden, Zachary A.; Silverstein, Robin P. 2013. Composite Burn Index (CBI) data and field photos collected for the FIRESEV project, western United States. Fort Collins, CO: Forest Service Research Data Archive. link

Reproducing the analysis

All scripts are numbered in the order of these steps. Note that some steps involve uploading assets to Earth Engine, so these steps won't have a script associated with them. In this case, there will be a discontinuity in the numbering of the scripts to highlight that there will be a step (of some kind) to complete before moving on.

Raw data are found in "data/data_raw/". Data carpentry (aka munging/wrangling/cleaning) steps can be found in "data/data_carpentry/". The resulting data products from carpentry steps are stored in "data/data_output/".

Analyses scripts are found in "analyses/". Intermediate output (e.g., a summary table from a model, a .rds file representing an R object of a long-running model) can be found in "analyses/analyses_output/"

1. data/data_carpentry/01_convert-jepson-ecoregions.R

2. In Earth Engine, run the 02_create-raster-template.js script to create a template raster co-registered with the Landsat product that will be used for the yellow pine/mixed-conifer mask.

3. data/data_carpentry/03_create-ypmc-mask.R

4. data/data_carpentry/04_subset-frap-perimeter-database.R

5. data/data_carpentry/05_clean-cbi-data.R

6. Upload the CBI data output from 05_clean-cbi-data.R to Earth Engine. The Earth Engine asset is publicly available at: ee.FeatureCollection("users/mkoontz/cbi_sn")

7. In Earth Engine, run the 07_rsr-sn-cbi-calibration.js script to generate (and export) eight .geoJSON files with the CBI plot information along with a number of spectral severity calculations. Each .geoJSON file represents one combination of interpolation type (bicubic or bilinear) and four time windows used to collate the pre- and post-fire imagery (16, 32, 48, and 64 days). The resulting .geoJSON files should be downloaded from your Google Drive and saved in: "data/data_output/ee_cbi-calibration/"

8. analyses/08_cbi-k-fold-cross-validation.R

9. Upload the fire perimeters containing some yellow pine/mixed-conifer (ypmc) pixels output from the 04_subset-frap-perimeter-database.R script to Earth Engine. The Earth Engine asset is publicly available at: ee.FeatureCollection("users/mkoontz/fire18_1_sn_ypmc")

10. Using the best interpolation, spectral severity. and time window from the cross-fold validation, calculate the fire severity, vegetation characteristics, and regional climate characteristics that we will use for modelling by running the 10_rsr-frap-fires-assessment.js script in Earth Engine. The resulting .geoJSON should be downloaded from your Google Drive and saved as: "data/data_output/ee_fire-samples/fires-strat-samples_2018_48-day-window_L4578_none-interp.geojson"

11. Prepare the fire samples for analysis by running "analyses/11_configure-fire-samples.R"

12. Build the primary analysis models for the paper (four separate models-- one for each neighborhood window size) by running "analyses/12_probability-of-high-severity-build-models.R"

13. Summarize the posterior distributions of the estimated coefficients of the models built in step 12 by running "analyses/13_probability-of-high-severity.R"

14. Perform model comparisons between models built at different scales to determine the primary scale of effect of the heterogeneity. Run the "analyses/14_model-comparison.R" script.

15. Calculate summary information for the CBI calibration step by running "analyses/15_cbi-summary-stats.R"

16. Calculate some additional attribute information for the USFS Region 5 GIS dataset-- the current best severity dataset for the Sierra Nevada. Run "data/data_carpentry/16_ypmc-pixel-count-usfs-r5.R"

17. Compare the currently best-available YPMC wildfire dataset (USFS Region 5 Geospatial) to what we have developed by running "analyses/17_fire-perims-summary-stats.R"

18. Get Spearman's correlation between prefire NDVI of central pixel and the prefire neighborhood mean NDVI of surrounding pixels to help support the interpretation of model coefficients.

Recreating figures

19. Generate a rasterized version of the fire perimeters to display on the geographic setting map by running "figures/figures_carpentry/19_rasterize-fire-perimeters.R"

20. Create Figure 1 (geographic extent of study) by running "figures/figures_carpentry/20_geographic-extent-of-study.R"

21. Create Figure 2 (demonstration of calibration of algorithm to ground-based severity) by running "figures/figures_carpentry/21_remote-sensed-severity-calibration.R"

22. In Earth Engine, generate two example severity maps of fires by running 22_rsr-visualize.js. Download the resulting raster files from your Google Drive and store them in "figures/"

23. Generate Figure 3 (example severity maps for Hamm and American fires) by running "figures/figures_carpentry/23_pre-post-rbr-visualization.R"

24. Generate Figure 4 (heterogeneity toy example) by running "figures/figures_carpentry/24_heterogeneity-demo-raster.R"

25. Generate Figure 5 (effects of covariates on probability of high severity fire) by running "figures/figures_carpentry/25_prob-hi-sev_main-effects-with-credible-intervals.R"

26. Generate Supplemental Figure 1 (cartoon of image acquisition algorithm) by running "figures/figures_carpentry/26_image-acquisition-algorithm.R"

27. Generate Supplemental Figure 2 (central pixel/neighborhood NDVI decoupling) toy example by running "figures/figures_carpentry/27_decoupling-center-neighborhood-ndvi.R"

Export full geoTIFF images of each fire (with severity and predictor variables)

28. Run "data/data_carpentry/28_ee-get-fire-ids-and-dates-for-mass-EE-export.R" to get the fire IDs and the fire dates to pass to the Earth Engine python interface in order to avoid needing to use .getInfo() calls to get the same information. A way to make this smoother might be to read in the .csv representing the FRAP perimeters' metadata (including the Earth Engine system index) to the python code directly. This approach is a bit of a kludge...

29. Paste the fire IDs and the fire alarm dates derived from step 28 as a python list in the "data/data_carpentry/29_ee-get-frap-derived-imagery.ipynb" file and run this script to iterate through all those fire ids and calulate severity plus the predictor varibles for the model (including regional climate, vegetation values, raw band values for Landsat before and after the fire, heterogeneity of vegetation, etc.)

30. Connect the original FRAP database of fire perimeters with the Earth Engine- derived metadata from the samples of those fires. Also create a table of the metadata for the rasters.

Demonstrate how to work with rasters available on the Open Science Framework

31. Rasters are available as a "component" to the full project on the OSF at this address: https://osf.io/ke4qj/. The "data/data_carpentry/31_basic-manipulations-of-remote-sensing-resistance-rasters.R" gives a few examples of how to find fire rasters of interest, name the bands, and visualize them.

Generating the manuscript documents

32. The main text of the manuscript is an RMarkdown file that can found in "docs/manuscript/remote-sensing-resistance.Rmd". Knitting this document will create the .pdf version of the main text, assuming the rest of the steps have been completed to generate intermediate data and analyses outputs, as well as to generate figures.

33. The supplemental material of the manuscript is also an RMarkdown file that can be found in "docs/manuscript/remote-sensing-resistance_supp-methods.Rmd". Like the main text, knitting this file will generate the supplemental information document so long as the prior steps have been completed.