Conclusion
By accounting for several different landslide risk factors, we were able to determine areas of Vancouver Island that are most likely to experience landslides, namely areas in the Insular Mountains. These conclusions do not necessarily predict exactly where or when landslides will occur; our results are based on older, averaged precipitation data and cannot take into account high-intensity precipitation events, which in reality are one of the biggest risk factors for landslides on Vancouver Island. Furthermore, more advanced analyses of landslide risk take into account more factors than we were able to access or were familiar with, including slope aspect, geology, distance to faults, lithology, stream power, topographical moisture index, etc. (Gemitzi, 2010, and Ozdemir, 2009).
Frank Landslide, Alberta. Source: Canadian Landslide Article
Human development needs to
take into account these different risks, and while this GIS-based approach is
not fully accurate, it gives an idea of areas that should be more carefully
considered. The threat of these hazards needs to be better evaluated in order
to prevent fatalities and property damage.
This research could be
expanded by identifying towns or populated areas that are in areas at risk of
landslides; or perhaps communities that are at risk due to roads that would
become inaccessible if there were to be a destabilization. Furthermore, the use
of more factors, especially the addition of fault lines, and more accurate data
(ie up to date precipitation data) could greatly improve the accuracy of the
results. It would be interesting to study the effects of deforestation on the
instances of landslides; or perhaps to study the changing risks based on months
or seasons as precipitation and temperature changes. An even more detailed
study could look at the effect of freezing on landslide risk, and expand on the
types of landslides assessed (debris flows, rockslides, etc). Combining GIS
analysis with a modelling approach involving past landslide records could be
even more effective at predicting high-risk areas.