Brivio et al. provides a case study where the integration of GIS and RS is able to compensate for limitations that may exist in each technology. The study provides a good example of how these two closely related fields can combine together to produce a more realistic representation of various phenomenon. While this case study specifically used additional GIS data as a supplementary component to improve on the RS classification of flooded areas, RS data can similarly be used to as a tool to produce GIS data (ex. land cover classification dataset derived from remote sensing data). However while there are many advantages in integrating the two, several issues come to mind. RS data is pixel based, while spatial data can be vector or raster based. To have to convert one to the other in order to do analysis would compound issues of accuracy and uncertainty. We know RS is already well acquainted with their own issues related to scale, noise and technological limitations, but these issues can quickly get amplified, and I can imagine that recognizing these sources of uncertainty will be difficult once the data thoroughly entangled in one another. Also, what kind of data models is required for this integration? Spatial data is generally represented in 2D, while RS hyperspectral cubes are in several dimensions. For the researcher whose interested in integrated such technologies, they have to be well versed in the inherent issues that each type of data presents to provide a comprehensive analysis – definitely no small feat.
-tranv