Applying object-based segmentation in the temporal domain to characterise snow seasonality

In the context of a changing climate it is important to be able to monitor and map descriptors of snow seasonality. Because of its relatively low elevation range, Australia’s alpine bioregion is a marginal area for seasonal snow-cover with high inter-annual variability. It has been predicted that snow-cover will become increasingly ephemeral within the alpine bioregion as warming continues. To assist the monitoring of snow seasonality and ephemeral snow-cover, a remote sensing method is proposed. The method adapted principles of object-based image analysis that have traditionally be used in the spatial domain and applied them in the temporal domain. The method allows for a more comprehensive characterisation of snow seasonality relative to other methods. Using high-temporal resolution (daily) MODIS image time-series, remotely sensed descriptors were derived and validated using in situ observations. Overall, moderate to strong relationships were observed between the remotely sensed descriptors of the persistent snow-covered period (start r = 0.70, p < 0.001; end r = 0.88, p < 0.001 and duration r = 0.88, p < 0.001) and their in situ counterparts. Although only weak correspondence (r = 0.39, p < 0.05) was observed for the number of ephemeral events detected using remote sensing, this was thought to be related to differences in the sampling frequency of the in situ observations relative to the remotely sense observations. For 2009, the mapped results for the number of snow-cover events suggested that snow-cover between 1400 and 1799 m was characterised by a high numbers of ephemeral events.