Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation

Cristina Gomez, Joanne C White, Michael A Wulder (Corresponding Author)

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

Discrete changes in forest abundance, distribution, and productivity are readily detectable using a number of remotely sensed data sources; however, continuous changes such as growth and succession processes are more difficult to monitor. In this research we explore the potential of spectral trajectories generated from a 35-year (1973–2008) time-series of Landsat imagery to characterize change processes in a dynamic forest environment in northwestern Alberta, Canada. We propose a method of hierarchical spatio-temporal segmentation that enables the characterization of change processes that are spatially diffuse and temporally imprecise. Calibrated imagery from Landsat sensors are radiometrically normalized and two metrics derived from the Tasseled Cap Transformation components, greenness and brightness, are used to generate the Tasseled Cap Angle (TCA). The TCA is a measure of the proportion of vegetation to non-vegetation (the occupation state), and its derivative, the Process Indicator (PI), is a measure of change in this proportion through time. These indices condense information from the visible and near-infrared wavelengths, and facilitate lengthy time series analysis of forest landscape change using data from all Landsat sensors.

A combination of the original TCA and its derivative sequence are input to a three level hierarchical segmentation process with the highest and lowest levels defining homogeneous objects at the initial and final date, and the intermediate level identifying trajectories with similar change processes. The development through time of the TCA and PI are described, and the spatial and temporal associations of processes are statistically assessed using the Moran's Index.

A full range of change types were identified on the landscape, from stand replacing disturbances to more subtle growth and succession processes. Results indicate that the study area is in a constant state of change, and maintains a high average proportion of vegetation to non-vegetation. The amount of total landscape modified per decade increased from 18% and 14% in the 1970s and 1980s respectively, to more than 30% and 33% in the 1990s and 2000s. On average, the proportion of vegetation to non-vegetation was increasing prior to 1981, decreasing between 1981 and 1997, and increasing post-1997. There was a high degree of spatial autocorrelation amongst change processes, with a maximum Moran's I of 0.79 in 1973; landscape change became more spatially disperse and widespread after 1981. Temporal correlation of change processes was observed locally, with the period 1990–1995 having the most persistent change.
Original languageEnglish
Pages (from-to)1665-1679
Number of pages15
JournalRemote Sensing of Environment
Volume115
Issue number7
Early online date6 Apr 2011
DOIs
Publication statusPublished - 15 Jul 2011

Fingerprint

forest dynamics
segmentation
Landsat
landscape change
vegetation
imagery
trajectory
Trajectories
sensor
trajectories
Derivatives
time series analysis
Time series analysis
Sensors
Autocorrelation
autocorrelation
occupation
Time series
Luminance
near infrared

Keywords

  • landsat
  • spectral trajectory
  • Tasseled Cap Angle
  • TCA
  • process indicators
  • PI
  • forest
  • change
  • hierarchical spatio-temporal segmentation
  • monitoring
  • landscape pattern
  • landscape process

Cite this

Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. / Gomez, Cristina; White, Joanne C; Wulder, Michael A (Corresponding Author).

In: Remote Sensing of Environment, Vol. 115, No. 7, 15.07.2011, p. 1665-1679.

Research output: Contribution to journalArticle

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