EGU21-6980
https://doi.org/10.5194/egusphere-egu21-6980
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

1-Step ahead 5-Day Forecast of Normalized Burn Ratio using a Combination of Sentinel-2 and Machine Learning

Kevin Achieng1 and Ellyn Enderlin2
Kevin Achieng and Ellyn Enderlin
  • 1University of Georgia, Athens, United States of America (kevin.achieng@uga.edu/kachiengz@yahoo.co.uk)
  • 2Department of Geosciences, Boise State University, Boise, ID, USA

Wildfires have become one of the world’s most destructive extreme events. In the US west-coast, for example, wildfire has caused severe loss of property, lives, and vegetation. Timely burn severity estimation is useful for planning and management of after-fire rehabilitation.  This study investigates plausibility of forecasting the normalized burn ratio (NBR), using machine learning models  – recurrent neural network (RNN), long-short term memory (LSTM), and Gated Recurrent Unit (GRU) – and Sentinel-2 imagery, for Campfire, California, U.S. The Campfire is the deadliest and most destructive wildfire in history of California’s wildfires. Sentinel-2 is used in this study because it captures remotely sensed images at high spatial and temporal resolutions of 10 m and 5 days, respectively. The resulting NBR time-series has a 5-hour interval. One-interval look-back in the ML algorithm results in a one-step 5-day prediction. To estimate NBR at the current time step, the machine learning method uses output from previous time-step and input of the current time-step as input variables to the model.  Results of this study show that combining machine learning and Sentinel-2 images produces plausible NBR 1-step ahead 5-day forecasts. 

How to cite: Achieng, K. and Enderlin, E.: 1-Step ahead 5-Day Forecast of Normalized Burn Ratio using a Combination of Sentinel-2 and Machine Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6980, https://doi.org/10.5194/egusphere-egu21-6980, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.