TY - JOUR
T1 - Characterizing spatial-temporal patterns of landscape disturbance and recovery in western Alberta, Canada using a functional data analysis approach and remotely sensed data
AU - Bourbonnais, Mathieu L.
AU - Nelson, Trisalyn
AU - Stenhouse, Gordon B.
AU - Wulder, Michael A.
AU - White, Joanne C.
AU - Hobart, Geordie W.
AU - Hermosilla, Txomin
AU - Coops, Nicholas C.
AU - Nathoo, Farouk
AU - Darimont, Chris
N1 - Funding Information:
We thank the Pacific Forestry Centre and Natural Resources Canada for providing access to the Landsat disturbance data. We also thank an anonymous reviewer for their helpful suggestions and comments. This work was supported by the Natural Sciences and Engineering Research Council of Canada through a Collaborative Research and Development Grant (grant number CRDPJ 486175-15) and a Canadian Graduate Scholarship, and by the Foothills Research Institute Grizzly Bear Project and it's many funding partners.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - 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. The resulting eight watershed clusters were mapped with mean curves representing the temporal trajectory of disturbance. The cumulative mean disturbance pattern metric curves for each cluster showed considerable variability in curve amplitude which generally increased markedly in the mid-1990's, while curve amplitude remained low in parks and protected areas. A comparison of mean curves by disturbance type (e.g., fires, harvest, non-stand replacing, roads, and well-sites) using a functional analysis of variance showed that anthropogenic disturbance contributed substantially to curve amplitude in all clusters, while curve amplitude associated with natural disturbances was generally low. These differences enable insights regarding how cumulative spatial disturbance patterns evolve through time on the landscape as a function of the type of disturbance and rates of recovery.
AB - 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. The resulting eight watershed clusters were mapped with mean curves representing the temporal trajectory of disturbance. The cumulative mean disturbance pattern metric curves for each cluster showed considerable variability in curve amplitude which generally increased markedly in the mid-1990's, while curve amplitude remained low in parks and protected areas. A comparison of mean curves by disturbance type (e.g., fires, harvest, non-stand replacing, roads, and well-sites) using a functional analysis of variance showed that anthropogenic disturbance contributed substantially to curve amplitude in all clusters, while curve amplitude associated with natural disturbances was generally low. These differences enable insights regarding how cumulative spatial disturbance patterns evolve through time on the landscape as a function of the type of disturbance and rates of recovery.
KW - Disturbance
KW - Functional data analysis
KW - Landsat
KW - Recovery
KW - Regionalization
KW - Spatial-temporal
UR - http://www.scopus.com/inward/record.url?scp=85018414415&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018414415&partnerID=8YFLogxK
U2 - 10.1016/j.ecoinf.2017.04.010
DO - 10.1016/j.ecoinf.2017.04.010
M3 - Article
AN - SCOPUS:85018414415
SN - 1574-9541
VL - 39
SP - 140
EP - 150
JO - Ecological Informatics
JF - Ecological Informatics
ER -