Long-term Burned Area Reconstruction through Deep Learning

Lampe, S. (Speaker), Bertrand Le Saux (Contributor), Vanderkelen, I. (Contributor), Thiery, W. (Contributor)

Activiteit: Talk or presentation at a conference


Wildfire impact studies are significantly hampered by the absence of a global long-term burned area dataset. This prevents conclusive statements on the role of anthropogenic activity on wildfire impacts over the last century. Here, we propose a workflow to construct a 1901-2014 reanalysis of monthly global burned area at a 0.5° by 0.5° scale. A neural network will be trained with weather-related, vegetational, societal and economic input parameters, and burned area as output label for the 1982-2014 time period. This model can then
be applied to the whole 1901-2014 time period to create a data-driven, long-term burned area reanalysis. This reconstruction will allow to investigate the long-term effect of anthropogenic activity on wildfire impacts, will be used as basis for detection and attribution studies and could help to reduce the uncertainties in future predictions.
Periode23 jul 2021
EvenementstitelInternational Conference on Machine Learning