The cluster proposal is in a google doc here, ask Edzer for access.
Due to current software limitations and the complexity of spatio-temporal data, analysis of space-time phenomena often proceeds conditionally by first treating space as static maps or snapshots and then showing their evolution over time, or by first collecting time series at individual locations and then analyzing their variation over space.
In this work package we will develop methods and tools for unconditional analysis of spatio-temporal processes. We will try to find the necessary representations and operations for integral analysis of spatio-temporal phenomena, based on the spatio-temporal data currently observed that include earth observation imagery, tracking data, meteorological data and space-time point patterns.
Secondly, we will explicitly address the human role in changing environmental systems. Humans and institutions are the major drivers of environmental change today, but models that take into account their motivations and the way they make decisions or reason about their environment are still in a very early stage. To improve on this, we will develop the necessary methods and tools for taking these drivers into account in modeling land use change.
Thirdly, we will deal with calibrating and validating models. To optimize the correspondence between modeled representations and observed processes, the most influential model parameters need to be calibrated; calibration in dynamic, agent-based and cell-based models is a new research frontier. Validation methods will be extended to assess the degree to which the model approximates reality, for state as well as change in the modeled system.
So, this group is interested in three main questions:
At INPE, scale is the resolution
and extend
of space-time-behavior resolution in a model. Contrary, the area / volume which is represented in a measurement is its support
which again is not similar to the uncertainty
of the mesurements.
Application domain: environmental integrated modelling (in the wide sense), modelling deforestation in Brazil (in the narrow sense)
Motivation for modelling:
{- modelling as a means to convince people}
What are the steps in building models?
How do we organize all these steps? (sequentially/cyclical/spiral–increasing complexity/random)
After lunch:
What do we have right now, what can we do?
What are the missing aspects?
Deforestation monitoring: collecting, combining, classifying all available imagery without clouds; look into the past. Methodology is fixed. Deforestation estimates take cloud cover into account.
(classes for monitoring: water, forest, deforested area, no forest, clouds)
Once a pixel is deforested, it is masked out for the next year(s).
In case of false positives, there will be feedback from local sources.
some changes are detected years after they happened (clouds)
modelling: for INPE means looking into the future (-goal), including all possible explanatory factors.
Space-time considerations:
Other things missing in TerraME
Focus?
This group decide to split in two sub-groups: