Monitoring the Impact of Wildfires on Tree Species with Deep Learning
This work addresses the problem of tracking wildfire effects on forest ecosystems for environmental monitoring, but it is incremental as it applies an existing deep learning method to a new dataset.
The researchers tackled the problem of monitoring wildfire impacts on tree species by customizing a deep learning model to classify land covers from aerial imagery before and after wildfires, achieving 92% accuracy on test data and applying it to three wildfires from 2009 to 2018 to delineate damage, species changes, and rebound areas.
One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning model is customized to classify land covers from four-band aerial imagery before and after wildfires to study the prolonged consequences of wildfires on tree species. The tree species labels are generated from manually delineated maps for five land cover classes: Conifer, Hardwood, Shrub, ReforestedTree and Barren land. With an accuracy of $92\%$ on the test split, the model is applied to three wildfires on data from 2009 to 2018. The model accurately delineates areas damaged by wildfires, changes in tree species and rebound of burned areas. The result shows clear evidence of wildfires impacting the local ecosystem and the outlined approach can help monitor reforested areas, observe changes in forest composition and track wildfire impact on tree species.