Canadian Forest Service Publications

Pixels to objects to information: Spatial Context to aid in forest characterization with remote sensing. 2008. Wulder, M.A.; White, J.C.; Hay, G.J.; Castilla, G. Pages 345-363 (Chapter 3.5) in T. Blaschke, S. Lang, and G.J. Hay, editors. Object-based image analysis - spatial concepts for knowledge-driven remote sensing applications. Springer, Berlin, Germany.

Year: 2008

Available from: Pacific Forestry Centre

Catalog ID: 31315

Language: English

CFS Availability: PDF (request by e-mail)

Abstract

Forest monitoring information needs span a range of spatial, spectral and temporal scales. Forest management and monitoring are typically enabled through the collection and interpretation of air photos, upon which spatial units are manually delineated representing areas that are homogeneous in attribution and sufficiently distinct from neighboring units. The process of acquiring, processing, and interpreting air photos is well established, understood, and relatively cost effective. As a result, the integration of other data sources or methods into this work-flow must be shown to be of value to those using forest inventory data. For example, new data sources or techniques must provide information that is currently not available from existing data and/or methods, or it must enable cost efficiencies. Traditional forest inventories may be augmented using digital remote sensing and automated approaches to provide timely information within the inventory cycle, such as disturbance or update information. In particular, image segmentation provides meaningful generalization of image data to assist in isolating within and between stand conditions, for extrapolating sampled information over landscapes, and to reduce the impact of local radiometric and geometric variability when implementing change detection with high spatial resolution imagery. In this Chapter, we present application examples demonstrating the utility of segmentation for producing forest inventory relevant information from remotely sensed data.

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