Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation

  • Authors:

  • Trisalyn Nelson;Barry Boots;Mike Wulder;Rob Feick

  • Affiliations:

  • Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Ontario, N2L 3C5, Canada nels2122@machl.wlu.ca;Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Ontario, N2L 3C5, Canada bboots@wlu.ca;Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada mwulder@pfc.forestry.ca;University of Waterloo, Department of Geography, Waterloo, Ontario, N2L 3G1, Canada rdfeick@fes.uwaterloo.ca

  • Venue:
  • Geoinformatica
  • Year:
  • 2004

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Abstract

Efficient identification of forest age is useful for forest management and ecological applications. Here we propose a user-assisted method for determining forest age using high spatial resolution remotely sensed imagery. This method requires individual trees to be extracted from imagery and represented as points. We use a local maximum filter to generate points that are converted to Voronoi polygons. Properties of the Voronoi polygons are correlated with forest age and used to aggregate points (trees) into areas (stands) based on three forest age classes. Accuracy of the aggregation ranges from approximately 68% to 78% and identification of the mature class is more consistent and accurate than the younger classes.