readme for Braziunas et al. 2022 Young Forests and Fire manuscript

see also

This github repository includes data and code associated with: Braziunas, K. H., D. C. Abendroth, and M. G. Turner. 2022. Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn. Ecosphere 13:e4096. https://doi.org/10.1002/ecs2.4096

Data and code are deposited at the Environmental Data Initiative (EDI): Braziunas, K.H., M.G. Turner, and D.C. Abendroth. 2022. Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming. ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/4f6864a8c7bb0f712d0443fbb334c73e

purpose

This readme gives an overview of directory structure, files, and steps for recreating outputs and analyses associated with Braziunas et al. 2022.

organization and file descriptions

Directory structure and files:

• analysis/: Results data.
  • fuels_prediction_map/: Summary statistics for field data, fuels models, and final fuels map. Includes field data plots and fuels summary statistics (plot_summary_stats.csv and fuels_summary_stats.csv), final linear models for predicting forest and shrubland fuels (model_final_selection.csv and model_final_preditors.csv), predicted versus observed values for final model fits (forest_predicted_observed.csv and shrubland_predicted_observed.csv), and comparison of final lidar-imagery fusion fuels map and LANDFIRE with field plot data (fuels_map_landfire_comparison.csv).
  • q1_young_old_forest_comparison: Data (young_old_fuels_severity.csv) and summary statistics (young_old_forest_medians.csv) used to answer question 1, How do pre-fire fuels and burn severity copmare between young (~30-year-old) and mature (> 125-year-old) forests that burned under similar fire weather conditions?
  • q2_fuels_fire_severity_models: Data (weather_fuels_severity.csv) used to answer question 2, How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather?
• data/: Input field and weather data.
  • Field_plots_2019: Field data collected for this study in 2019 in Grand Teton National Park. Includes Raw_data folder with GRTE_LiDAR_field_data_2019.xls that includes associated metadata on sheet 1. Also includes Cleaned_data folder, with .csv outputs from R data cleaning script.
  • GRTE_RAWS: RAWS weather station data during the Berry Fire.
• processed_data: Data created during analyses.
  • Berry_Fire_sample_polys: Shapefiles and associated lidar and imagery (NAIP) predictors for random samples of grid cells from the Berry Fire used to answer Q1 (files with age in title) and Q2 (files with wx in title).
  • field_plot_selection: Shapefiles for field plot footprints used in extracting lidar and imagery predictors for create linear regression models to predict fuels.
  • fuels_map_variables: Fuels used as dependence variables in linear regression models (field_plot_2019_fuels.csv). Lidar (lidar_metrics.csv) and imagery (naip_metrics.csv) predictors for field plot footprints used in linear regression models. Also includes fvs folder with inputs and outputs from estimating fuels in Forest Vegetation Simulator software.
  • GRTE_rasters: Final fuels map rasters (in final_fuels_map folder) and rasters used in creating final fuels maps or performing manuscript analyses. These include a digital elevation model, lidar and imagery predictors at 30 m resolution, and vegetation map at 30 m resolution. Also includes a fuels_map_comparison folder with unmasked version of canopy fuels for LANDFIRE comparison. One raster grte_30m_naip_masked.tif is not uploaded because it exceeds Github's file size limits, but this can be created from data included here by running step07_create_fuels_map.R.
  • GRTE_shps: Outline of study region in which fuels were mapped.
• scripts: R scripts used to recreate data and analyses. Scripts are numbered in order they were run. Scripts with .R can be rerun with data provided in deposit. Scripts with .Rmd require additional data not included in deposit such as NAIP imagery files, raw lidar point cloud, LANDFIRE fuels maps, MTBS burn severity rasters, and fire progression maps. Examples of some outputs of these scripts and included in knitted .html files.
  • step01_fuels_plots_data_cleaning_prep.R: Quality check and cleaning of field plot data.
  • step02_field_plot_fuels_calculations.R: Calculate fuel loads for field plots.
  • step03_berry_fire_point_selection.Rmd: Random selection of points for Q1 and Q2 analyses. Also calculates summary information about the Berry Fire on high and low spread days.
  • step04_las_statistics_extraction.Rmd: Extract lidar metrics from lidar point cloud for fitting fuels regressions, predicting fuel loads in Berry Fire sample points (Q1 and Q2), and creating the final fuels map. This script also creates the common grid and masks used for final fuels map.
  • step05_naip_statistics_extraction.Rmd: Extract imagery metrics from NAIP data for fitting fuels regressions, predicting fuel loads in Berry Fire sample points (Q1 and Q2), and creating the final fuels map.
  • step06_fit_fuels_regressions.R: Fit linear regression models to predict field-measured fuels from lidar and imagery predictors.
  • step07_create_fuels_map.R: Create lidar-imagery fusion fuels maps.
  • step08_fuels_fit_analyses.R: Use final linear regression models to predict fuels in field data plots and generate summary data on model fits (predicted versus observed values).
  • step09_fuels_map_landfire_comparison.Rmd: Compare predicted fuels from final lidar-imagery fusion fuels and LANDFIRE fuels maps (rasters) with field data observations (using centroids from field plots to extract corresponding raster values).
  • step10_fuels_severity_analyses.R: Analyses for Q1 (fuels and burn severity in young versus mature forests) and Q2 (how well fuels predict burn severity under extreme versus moderate fire weather).

platforms

• Operating systems and software used for development and implementation
  • OS: Windows 10 Pro
  • R version: 3.6.1
  • Forest Vegetation Simulator Complete Package Software Version: 2020.03.11
  • ArcGIS Desktop 10.6