Canadian Forest Service Publications
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. 2013. White, J.C.; Wulder, M.A.; Varhola, A.; Vastaranta, M.; Coops, N.C.; Cook, B.D.; Pitt, D.G.; Woods, M. The Forestry Chronicle 89(6): 722-723.
Available from: Pacific Forestry Centre
Catalog ID: 35328
CFS Availability: PDF (request by e-mail)
Available from the Journal's Web site. †
† This site may require a fee.
A best practices guide for the use of airborne laser scanning data (ALS; also referred to as Light Detection and Ranging or LiDAR) in forest inventory applications is now available for download from the Canadian Forest Service bookstore (White et al., 2013; http://cfs.nrcan.gc.ca/publications?id= 34887). The guide, produced by the Canadian Forest Service, Natural Resources Canada, brings together state-of-the-art approaches, methods, and data to enable readers interested in using ALS data to characterize large forest areas in a cost-effective manner. The best practices presented in the guide are based on more than 25 years of scientific research on the application of ALS data to forest inventory. The guide describes the entire process for generating forest inventory attributes from ALS data and recommends best practices for each step of the process—from ground sampling through to metric generation and model development. The collection of ground plot data for model calibration and validation is a critical component of the recommended approach and is described in detail in the guide. Appendices to the guide provide additional details on ALS data acquisition and metric generation.
Plain Language Summary
Airborne Laser Scanning (ALS) data—also known as Light Detection and Ranging (LiDAR)—enables the 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 digital elevation models and the estimation of a range of forest inventory attributes (e.g., height, basal area, and volume). A best practices guide brings together state-of-the-art approaches, methods, and data to provide non-experts more detailed information about complex topics. With this guide, our goal is to inform and enable readers interested in using ALS data to characterize, in an operational forest inventory context, large forest areas in a cost-effective manner. The best practices presented in the guide are based on more than 25 years of scientific research. In this document we describe the process required to generate forest inventory attributes from ALS data from start to finish, recommending best practices for each stage, from ground sampling and statistics, through to sophisticated spatial data processing and analysis. As the collection of ground plot data for model calibration and validation is a critical component of the recommended approach, we have placed appropriate emphasis on this section of the guide.
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