Geospatial analysis can be no better than the original inputs, much like a computer is only as smart as its user. In the field of remote sensing, this ideology may be on its way to becoming obsolete. Brivio et. al show from a case study of catastrophic inundation in Italy that they can compensate for the temporal disparity in the capturing of remotely sensed data and the peak point of the flood, a few days before.
The analysis, however, was not completed with the sole use of synthetic aperture radar images. Had it not been for the integration of topological data, it is unlikely that one would be able to obtain similarly successful results.
With any data input, temporal or spatial resolution are limiting factors. Brivio highlights this by acknowledging the use of NOAA thermal infrared sensors, which have a finer temporal resolution, while lacking in spatial resolution. Conversely, the SAR images used in the case study analysis have a relatively higher spatial resolution, but produces longer temporal intervals.
Given Brivio et. al’s successful prediction of flooding extent, it may mean that, if need be, it is advantageous to choose an input with a finer spatial resolution in exchange for a coarser temporal resolution, complementing the temporal delay with additional inputs to compensate.
Break remote sensing down into it’s two main functions: collection and output. One will inevitably lag behind the other, but eventually the leader will be surpassed by the follower. Only for it to happen again some time down the road. Much like two racers attached by a rubber band.
What all of this means for GIS; eventually the output from remote sensing application will surpass the computing power of geographic information systems. At which point, the third racer, processing, will become relevant, if he isn’t already.
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