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.; Woods, M. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC. Information Report FI-X-010

Year: 2013

Available from: Pacific Forestry Centre

Catalog ID: 34887

Language: English

Series: Information Report (CWFC - CFS)

CFS Availability: PDF (download)

Abstract

A best practice 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 airborne laser scanning (ALS; also referred to as Light Detection and Ranging [LiDAR]) data to characterize, in an operational inventory context, large forest areas in a cost-effective manner. To meet this goal, we outline an approach to using ALS data that is based on (1) theoretical and technical applicability; (2) published or established heritage; (3) parsimoniousness; and (4) clarity. The best practices presented herein are based on more than 25 years of scientific research on the application of ALS data in forest inventory. 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. Although many readers will not have the capacity—or need—to undertake all of the stages of this process themselves, we feel it is important for all readers to have some understanding of the various stages of the process. Such an understanding is necessary to make informed decisions when determining whether ALS is an appropriate data choice for a forest management area. Moreover, a minimum level of knowledge is useful when outsourcing or establishing collaborations for data acquisition, processing, or analysis, and when evaluating deliverables. To this end, we also provide some background information on ALS.

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

Also available under the title:
Guide des meilleures pratiques pour générer des attributs d’inventaire forestier provenant de données obtenues par balayage laser aéroporté en utilisant une approche par zones (French)