  Due to recent climate changes, we have seen more frequent and severe
wildfires in the United States. Predicting wildfires is critical for natural
disaster prevention and mitigation. Advances in technologies in data processing
and communication enabled us to access remote sensing data. With the remote
sensing data, valuable spatiotemporal statistical models can be created and
used for resource management practices. This paper proposes a distributed
learning framework that shares local data collected in ten locations in the
western USA throughout the local agents. The local agents aim to predict
wildfire grid maps one, two, three, and four weeks in advance while online
processing the remote sensing data stream. The proposed model has distinct
features that address the characteristic need in prediction evaluations,
including dynamic online estimation and time-series modeling. Local fire event
triggers are not isolated between locations, and there are confounding factors
when local data is analyzed due to incomplete state observations. Compared to
existing approaches that do not account for incomplete state observation within
wildfire time-series data, on average, we can achieve higher prediction
performance.
