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Home / Photogrammetric Engineering & Remote Sensing, Number 3 / March 2011

Open Access Characterizing Forest Succession in Central Ontario using Lidar-derived Indices

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This study investigates the potential of discrete return light detection and ranging (lidar) data to characterize forest succession in a mixed mature forest in central Ontario using indices applied to the lidar point cloud. Derived indices include statistical indices, predicted Lorey’s height (R2 = 0.86; RSME = 2.36 m) and quadratic mean diameter-at-breast-height (R2 = 0.68; RMSE = 1.21 cm), canopy density indices and an information theory based complexity index. To assess how well these indices are able to capture the vertical structure of forest stands, they are compared to Oliver and Larson’s (1996) four stages of forest stand development. Best subsets regressions indicated that no single index is able to separate all four stages adequately. However, the predicted Lorey’s height index is optimal for separating early from mid succession stages (p <.0001) and a combination of height and complexity indices performed best to discriminate between mid- and late-succession stages (p <.0001).

Document Type: Research Article

Publication date: 01 March 2011

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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