GIS and RS: how do we account for variability?

Brivio et al.’s article “Integration of remote sensing data and GIS… for mapping of flooded areas” presents the very common process of using RS data and GIS  to map flooding and flood plains. Although the article presents how the integration of RS and GIS can accurately map a flood with a concluded method  accuracy of 96%, it only looks at a single event and study site. From my experience, this is not always the case, as  integration methods, even if they are the same, often vary in accuracy from one location to another. Furthermore, event duration, intensity and geologic substates often interfere with flood area prediction from RS data and GIS, as variations can modify water location within minutes to hours. To clarify, one area may be flooded at certain points during the flood period while during other periods dry (i.e. it may transition from wet to dry to wet), which interferes with accuracy of the RS data and GIS prediction. Fundamentally, water changes how the surrounding environment reacts, modifying where floods are. As floods react to the environment, often areas become flooded for only minutes and as such, are never recognized as a flooded area, in both GIS predictions and RS data, as well as human reports (although they were flooded; but only for minutes).

To better predict flood area, TWIs (topographical wetness index) and DEMs (digital elevation models) when compared to flow paths (cost-distance matrix), may in fact, better predict flooded areas when used in conjunction with RS data then just the integration of RS data to cost-distance matrixes. In addition, more data sets and studies would further help to create a more general integration protocol and predictive area estimates for floods. To elaborate, the techniques in the article work well on the study area by may not work on other floods, therefore by adding more data from more types of floods, the technique could be adapted to other situations. The result of multiple integrations with multiple data sets would also reduce error and produce greater accuracy. The “Big” question, however that will still remain unanswered from this article is: how can we account for ecosystem and flood variability within GIS and RS data sets?


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