Forests 2013, 4(3), 518-536; doi:10.3390/f4030518
Review

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

1 Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia V8Z 1M5, Canada 2 Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland 3 Integrated Remote Sensing Studio, Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada 4 Canadian Wood Fibre Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste. Marie, Ontario, P6A 2E5, Canada 5 Southern Science & Information Section, Ontario Ministry of Natural Resources, North Bay, Ontario P1A 4L7, Canada
* Author to whom correspondence should be addressed.
Received: 17 May 2013; in revised form: 17 June 2013 / Accepted: 19 June 2013 / Published: 26 June 2013
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Abstract: Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.
Keywords: airborne laser scanning (ALS); LiDAR; forest inventory; area-based approach; point cloud; digital photogrammetry; semi-global matching (SGM); digital surface model (DSM)

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MDPI and ACS Style

White, J.C.; Wulder, M.A.; Vastaranta, M.; Coops, N.C.; Pitt, D.; Woods, M. The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning. Forests 2013, 4, 518-536.

AMA Style

White JC, Wulder MA, Vastaranta M, Coops NC, Pitt D, Woods M. The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning. Forests. 2013; 4(3):518-536.

Chicago/Turabian Style

White, Joanne C.; Wulder, Michael A.; Vastaranta, Mikko; Coops, Nicholas C.; Pitt, Doug; Woods, Murray. 2013. "The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning." Forests 4, no. 3: 518-536.

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