Remote sensing data from global observing satellites, combined with
data from ecosystem models, offers an unprecedented opportunity for
predicting and understanding the behavior of the Earth's ecosystem.
This data consists of a sequence of global snapshots of the Earth,
and includes various atmospheric, land and ocean variables such as
sea surface temperature (SST), pressure, precipitation and
Net Primary Production (NPP). However, due to
the large amount of data that is available, data mining techniques are
needed to facilitate the automatic extraction and analysis of
interesting
patterns from the Earth Science data.
Mining patterns from Earth Science data is a difficult task due to the
spatio-temporal nature of the data. This talk will discuss some of the
challenges involved in modeling, preprocessing and analyzing the data,
and also present our early work on the design of efficient algorithms
for finding spatio-temporal patterns from such data.
We will discuss techniques for removing seasonality, clustering and
different types of association analysis for discovering interesting
relationships among ecological variables at various parts of the Earth.
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