  Given the increasing prevalence of wildland fires in the Western US, there is
a critical need to develop tools to understand and accurately predict burn
severity. We develop a machine learning model to predict post-fire burn
severity using pre-fire remotely sensed data. Hydrological, ecological, and
topographical variables collected from four regions of California - the sites
of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy
fire (2021), and the KNP Fire (2021) - are used as predictors of the difference
normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that
accounts for spatial autocorrelation using Vecchia's Gaussian approximation
will accurately model burn severity. In all combinations of test and training
sets explored, the results of our model showed the SL algorithm outperformed
standard Linear Regression methods. After fitting and verifying the performance
of the SL model, we use interpretable machine learning tools to determine the
main drivers of severe burn damage, including greenness, elevation and fire
weather variables. These findings provide actionable insights that enable
communities to strategize interventions, such as early fire detection systems,
pre-fire season vegetation clearing activities, and resource allocation during
emergency responses. When implemented, this model has the potential to minimize
the loss of human life, property, resources, and ecosystems in California.
