Characterizing spatial-temporal patterns of landscape disturbance and recovery in western Alberta, Canada using a functional data analysis approach and remotely sensed data

Mathieu L. Bourbonnais, Trisalyn Nelson, Gordon B. Stenhouse, Michael A. Wulder, Joanne C. White, Geordie W. Hobart, Txomin Hermosilla, Nicholas C. Coops, Farouk Nathoo, Chris Darimont

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)140-150
Number of pages11
JournalEcological Informatics
Volume39
DOIs
StatePublished - May 1 2017

Fingerprint

Functional Data Analysis
Alberta
data analysis
Recovery
Disturbance
Canada
disturbance
trajectories
Watersheds
Trajectories
Landsat
Curve
Functional analysis
anthropogenic activities
Regionalization
Spatial Pattern
roads
time series analysis
conservation areas
principal component analysis

Keywords

  • Disturbance
  • Functional data analysis
  • Landsat
  • Recovery
  • Regionalization
  • Spatial-temporal

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Ecological Modeling
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Characterizing spatial-temporal patterns of landscape disturbance and recovery in western Alberta, Canada using a functional data analysis approach and remotely sensed data. / Bourbonnais, Mathieu L.; Nelson, Trisalyn; Stenhouse, Gordon B.; Wulder, Michael A.; White, Joanne C.; Hobart, Geordie W.; Hermosilla, Txomin; Coops, Nicholas C.; Nathoo, Farouk; Darimont, Chris.

In: Ecological Informatics, Vol. 39, 01.05.2017, p. 140-150.

Research output: Contribution to journalArticle

Bourbonnais, Mathieu L. ; Nelson, Trisalyn ; Stenhouse, Gordon B. ; Wulder, Michael A. ; White, Joanne C. ; Hobart, Geordie W. ; Hermosilla, Txomin ; Coops, Nicholas C. ; Nathoo, Farouk ; Darimont, Chris. / Characterizing spatial-temporal patterns of landscape disturbance and recovery in western Alberta, Canada using a functional data analysis approach and remotely sensed data. In: Ecological Informatics. 2017 ; Vol. 39. pp. 140-150.
@article{883238157118413ba87a4e0546d2deb1,
title = "Characterizing spatial-temporal patterns of landscape disturbance and recovery in western Alberta, Canada using a functional data analysis approach and remotely sensed data",
abstract = "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.",
keywords = "Disturbance, Functional data analysis, Landsat, Recovery, Regionalization, Spatial-temporal",
author = "Bourbonnais, {Mathieu L.} and Trisalyn Nelson and Stenhouse, {Gordon B.} and Wulder, {Michael A.} and White, {Joanne C.} and Hobart, {Geordie W.} and Txomin Hermosilla and Coops, {Nicholas C.} and Farouk Nathoo and Chris Darimont",
year = "2017",
month = "5",
day = "1",
doi = "10.1016/j.ecoinf.2017.04.010",
language = "English (US)",
volume = "39",
pages = "140--150",
journal = "Ecological Informatics",
issn = "1574-9541",
publisher = "Elsevier",

}

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

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

VL - 39

SP - 140

EP - 150

JO - Ecological Informatics

JF - Ecological Informatics

SN - 1574-9541

ER -