Post-Wildfire Recovery

This is an Earth Lab Certificate project by Heidi Yoon studying post-wildfire recovery.

Project Motivation and Goal

• Wildland fire is a multifaceted natural phenomenon of increasing importance to both human and ecological communities. In this project, we explore the post-wildfire recovery for the 2016 Chimney Tops 2 Fire by spatially quantifying the vegetation recovery using hyperspectral reflectance data.
• This project highlights how high spatial resolution (1-meter) remote sensing measurements, such as hyperspectral reflectance data, can be used to study fire recovery on the order of the spatial variation on the ground.
• In this repository, we include example notebooks that process and analyze reflectance data and percent ground coverage data to assess post-wildfire recovery.

Project Environment

To run our project workflow, clone this repository: $ git clone https://github.com/AreteY/post-wildfire-recovery.git Then install the python environment described below.

Installing and Running the Environment

1. Download the file neon-environment.yml from this repository, which contains instructions on how to install the environment, into the project directory post-wildfire-recovery.
2. Create the environment by running: $ conda env create -f neon-environment.yml
3. Once the environment is installed, activate it by running: $ conda activate earth-analytics-neon

Tools and Packages Used

• matplotlib
• numpy
• pandas
• requests
• h5py
• geopandas
• shapely
• rasterio
• rioxarray
• xarray
• earthpy
• folium

Project Background

To learn more about the Chimney Tops 2 Fire and the motivation for this project, please see our blog post post_wildfire_blog.ipynb notebook and the fire progression figure in the Reports folder and the Graphics folder (fire_progression.png and grsm_fire_map.png), respectively.

To create the final post_wildfire_blog.html output, start the project environment and make sure you are in the reports directory within post-wildfire-recovery. Then run jupyter nbconvert for the post_wildfire_blog.html output. $ conda activate earth-analytics-neon $ cd reports $ jupyter nbconvert --to html --TemplateExporter.exclude_input=True post_wildfire_blog.ipynb

Data Sources

Raster data

1. NEON Spectrometer Reflectance
2. Reference: National Ecological Observatory Network. Spectrometer orthorectified surface directional reflectance - mosaic (DP3.30006.001), RELEASE-2022. https://doi.org/10.48443/5er3-8n49. Dataset accessed from https://data.neonscience.org on April 15, 2022.
3. Landsat 8 Surface Reflectance
4. Reference: Landsat Level-2 Surface Reflectance Science Product, courtesy of the U.S. Geological Survey. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. Dataset accessed from https://earthexplorer.usgs.gov on June 6, 2022.

Vector data

1. Chimney Tops 2 Fire Perimeter
2. Reference: MTBS Data Access: Fire Level Geospatial Data. (2022, February - last revised). MTBS Project (USDA Forest Service/U.S. Geological Survey). Available: https://mtbs.gov/direct-download. Data accessed April 3, 2022.
3. Available for download in this repository as Release v1.0.0 chimtops2-boundary
4. Great Smoky Mountains National Park Perimeter
5. Reference: National Park Service- Land Resources Division. Great Smoky Mountains National Park Boundary. (December 30, 2019 - last revised). Available: https://grsm-nps.opendata.arcgis.com. Data accessed March 28, 2022.
6. Available for download in this repository as Release v1.0.1 grsm-boundary
7. NEON Terrestrial Observation System Sampling Locations
8. Reference: NEON Document Library: Spatial Data. (June 29, 2020 - last revised). Available: https://data.neonscience.org/documents. Data accessed April 18, 2022.
9. Available for download in this repository as Release v1.0.2 neon-tos-plot-centroids

Tabular data

1. NEON Plant Presence and Percent Cover
2. Reference: NEON (National Ecological Observatory Network). Plant presence and percent cover (DP1.10058.001), RELEASE-2022. https://doi.org/10.48443/pr5e-1q60. Dataset accessed from https://data.neonscience.org on April 18, 2022.

Project Workflow

The project workflow is a post-wildfire vegetation recovery analysis in which the vegetation recovery of an 1-km² area within the burn perimeter is characterized using vegetation indices and evaluated with a spectral analysis. First, vegetation indices (NBR: normalized burn ratio, NDVI: normalized difference vegetation index, MSAVI: modified soil adjusted vegetation index) are calculated using Landsat 8 Surface Reflectance and NEON Spectrometer Reflectance Measurements. Second, we have begun the spectral analysis by building the spectral library with the reflectance spectra and percent cover for NEON Terrestrial Observation System sampling locations within the fire boundary. Finally, multiple endmember spectral band analysis will be used to spectrally unmix the NEON Spectrometer Reflectance Measurements and evaluate the vegetation recovery at a sub-pixel level.

Run Workflow

• Run the notebook vegetation_indices.ipynb with the module reflectance.py to calculate the vegetation indices using a downloaded NEON reflectance file and to plot the results using matplotlib and earthpy.
• Run the notebook landsat_vegetation.ipynb with modules landsat.py and reflectance.py to calculate the vegetation indices, using downloaded Landsat 8 files, for a fire boundary and for a 1-km² area that corresponds to a NEON reflectance tile. In the notebook, we generate the shapefile tile_274000_3947000.shp to crop the Landsat data to the 1-km² area. All the results are plotted using matplotlib and rasterio.
• Run the notebook vegetation_subplots.ipynb with the modules plots.py and reflectance.py to determine which NEON Terrestrial Observation System plots are within a fire boundary and which plots have been sampled by the NEON TOS Plant Presence and Percent Cover Data Product. In this notebook, find the coordinates of the subplots using the NEON API, extract the percent cover results into a pandas dataframe, and plot the results using a pivot table in matplotlib.
• Run the notebook vegetation_spectra.ipynb with the module reflectance.py to plot the reflectance spectrum for each NEON Terrestrial Observation System subplot using the output grsm_plots_coords.csv generated by notebook vegetation_subplots.ipynb.

To run any of the notebooks in this repository:

• Start the project environment and make sure you are in the notebooks directory within post-wildfire-recovery. Then use Jupyter Notebook to open notebook.ipynb in your default web browser. As an example, we have opened the notebook vegetation_indices.ipynb below. $ conda activate earth-analytics-neon $ cd notebooks $ jupyter notebook vegetation_indices.ipynb
• All data used in this workflow is accessible. Please see the notebooks for details.

Example Usage

• The vegetation indices of any NEON Airborne Observation Platform Hyperspectral Reflectance hdf5 file can be calculated using notebook vegetation_indices.ipynb. This could include the NEON spectrometer orthorectified surface directional reflectance - mosaic data product used in this workflow and the NEON spectrometer orthorectified surface directional reflectance - flightline data product.
• The vegetation indices of any Landsat 8 scene with surface reflectance that overlaps a fire boundary (shapefile) can be calculated using notebook landsat_vegetation.ipynb.
• Any xarray.DataArray can be cropped to a 1-km² area in UTM coordinates using notebook landsat_vegetation.ipynb.
• The overlap of any fire boundary (shapefile) and any NEON Terrestrial Observation System sampling location (shapefile) can be determined using notebook vegetation_subplots.ipynb. This could include other fires from the United States from 1984 to present available from the MTBS project and other NEON Terrestrial locations and other plant data products of interest.
• The reflectance spectrum for any location in UTM coordinates for any NEON Airborne Observation Platform Hyperspectral Reflectance hdf5 file can be determined using notebook vegetation_spectra.ipynb. This could include the NEON spectrometer orthorectified surface directional reflectance - mosaic data product used in this workflow and the NEON spectrometer orthorectified surface directional reflectance - flightline data product.

License

The post-wildfire-recovery project is under the MIT License.

Citation

@software{Yoon_Post-Wildfire_Recovery_2021, author = {Yoon, Y. Heidi and Ilangakoon, Nayani}, doi = {10.5281/zenodo.6574445}, month = {5}, title = {{Post-Wildfire Recovery}}, url = {https://github.com/AreteY/post-wildfire-recovery}, version = {1.1.0}, year = {2021} }