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Forests 2015, 6(10), 3704-3732; doi:10.3390/f6103704

Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment

1
Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada
2
Bavarian State Institute of Forestry (LWF), Department of Information Technology, Research Group: Remote Sensing, Hans-Carl-von-Carlowitz-Platz 1, Freising D-85354, Germany
3
Department of Forest Resources Management, Forest Science Centre, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Eric J. Jokela
Received: 4 August 2015 / Revised: 29 September 2015 / Accepted: 8 October 2015 / Published: 15 October 2015
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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Abstract

Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point cloud generated using semi-global matching (SGM) for a complex, coastal forest in western Canada. Plot-level estimates of Lorey’s mean height (H), basal area (G), and gross volume (V) were modelled using an area-based approach. Metrics and model outcomes were evaluated across a series of strata defined by slope and canopy cover, as well as by image acquisition date. We found statistically significant differences between ALS and SGM metrics for all strata for five of the eight metrics we used for model development. We also found that the similarity between metrics from the two data sources generally increased with increasing canopy cover, particularly for upper canopy metrics, whereas trends across slope classes were less consistent. Model outcomes from ALS and SGM were comparable. We found the greatest difference in model outcomes was for H (ΔRMSE% = 5.04%). By comparison, ΔRMSE% was 2.33% for G and 3.63% for V. We did not discern any corresponding trends in model outcomes across slope and canopy cover strata, or associated with different image acquisition dates. View Full-Text
Keywords: digital aerial images; image matching; airborne laser scanning; forest inventory; photogrammetry; lidar digital aerial images; image matching; airborne laser scanning; forest inventory; photogrammetry; lidar
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

White, J.C.; Stepper, C.; Tompalski, P.; Coops, N.C.; Wulder, M.A. Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment. Forests 2015, 6, 3704-3732.

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