Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected. Although data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective: • geospatial data have to be accurately co-registered in a common grid; • time gaps between the various data layers have to be known and accounted for; • systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account. Data can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on. Data integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play. In general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.
Completed (GI-N2K)