This is an Earth Lab Certificate project by Heidi Yoon studying post-wildfire recovery.
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.
neon-environment.yml
from this repository, which contains instructions on how to install the environment, into the project directory post-wildfire-recovery
.$ conda env create -f neon-environment.yml
$ conda activate earth-analytics-neon
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
chimtops2-boundary
grsm-boundary
neon-tos-plot-centroids
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.
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.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.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.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
.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
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.landsat_vegetation.ipynb
.landsat_vegetation.ipynb
.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.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.The post-wildfire-recovery project is under the MIT License.
@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}
}