Canadian Forest Service Publications
A functional data analysis approach for characterizing spatial-temporal patterns of landscape disturbance and recovery from remotely sensed data. Bourbonnais, M. L., Wulder, M. A., Coops, N. C., Nelson, T. A., White, J. C., Nathoo, F., Stenhouse, G. B., Hobart, G. W., Darimont, C. T., & Hermosilla, T. (2019). CEUR Workshop Proceedings, 2323, [4].
Year: 2019
Issued by: Pacific Forestry Centre
Catalog ID: 40434
Language: English
Availability: PDF (request by e-mail)
Abstract
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.
Plain Language Summary
Landscape regionalization approaches are frequently used to summarize and visualize complex spatial patterns, environmental factors, and disturbance regimes. However, landscapes are dynamic and contemporary regionalization approaches based on spatial patterns often do not account for the temporal component that may provide important insight on disturbance, recovery, and how ecological processes change through time. The objective of this research was to quantify spatial patterns of disturbance and recovery over time for use as inputs in a regionalization that characterizes unique spatial-temporal trajectories of disturbance in western Alberta, Canada. Cumulative spatial patterns of disturbance, representing the proportion, arrangement, size, and number of disturbances, and adjusted annually for spectral recovery, were quantified in 223 watersheds using a Landsat time series dataset where disturbance events are detected and classified annually from 1985 to 2011. Using a functional data analysis approach, disturbance patterns metrics were modelled as curves and scores from a functional principal components analysis were clustered using a Gaussian finite mixture model.
