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Spatial statistical techniques for aggregating point objects extracted from high spatial resolution remotely sensed imagery

Author

Listed:
  • Trisalyn Nelson

    (Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Ontario, N2L 3C5, Canada)

  • K. Olaf Niemann

    (University of Victoria, Department of Geography, P.O. Box 3050, Victoria, BC, V8W 3P5, Canada)

  • Michael A. Wulder

    (Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada (e-mail: mwulder@pfc.forestry.ca))

Abstract

. Using a local maximum filter, individual trees were extracted from a 1 m spatial resolution IKONOS image and represented as points. The spatial pattern of individual trees was determined to represent forest age (a surrogate for forest structure). Point attributes, based on the spatial pattern of trees, were generated via nearest neighbour statistics and used as the basis for aggregating points into forest structure units. The forest structure units allowed for the mapping of a forested area into one of three age categories: young (1–20 years), intermediate (21–120 years), and mature (>120 years). This research indicates a new approach to image processing, where objects generated from the processing of image data (rather than pixels or spectral values) are subjected to spatial statistical analysis to estimate an attribute relating an aspect of forest structure.

Suggested Citation

  • Trisalyn Nelson & K. Olaf Niemann & Michael A. Wulder, 2002. "Spatial statistical techniques for aggregating point objects extracted from high spatial resolution remotely sensed imagery," Journal of Geographical Systems, Springer, vol. 4(4), pages 423-433, December.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:4:d:10.1007_s101090300092
    DOI: 10.1007/s101090300092
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