A functional data analysis approach for characterizing spatial-temporal patterns of landscape disturbance and recovery from remotely sensed data

Contemporary landscape regionalization approaches, frequently used to summarize and visualize complex spatial patterns and disturbance regimes, often do not account for the temporal component which may provide important insight on disturbance, recovery, and change in ecological processes. The objective of this research was to employ novel statistical approaches in functional data analysis to quantify and cluster spatial-temporal patterns of landscape disturbance and recovery in 223 watersheds using a Landsat disturbance time series from 1985 – 2011 in western Alberta, Canada. Cumulative spatial patterns of disturbance, representing the proportion, arrangement, size, and number of disturbances per watershed, were modelled as functions and scores from a functional principal component analysis were clustered using a Gaussian finite mixture model. The resulting eight watershed clusters were mapped with mean functions representing unique temporal trajectories of disturbance and recovery. There was considerable variability in disturbance amplitude among the clusters which increased markedly in the mid-1990’s while remaining low in parks and protected areas. The regionalization highlights unique temporal trajectories of disturbance and recovery driven by anthropogenic and natural disturbances and enables insight regarding how cumulative spatial disturbance patterns evolve through time.