Canadian Forest Service Publications
A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach. 2017. White, J.C.; Tompalski, P.; Vastaranta, M.; Wulder, M.A.; Saarinen, S.; Stepper, C.; Coops, N.C. CWFC Information Report FI-X-018, 38 pp.
Available from: Pacific Forestry Centre
Catalog ID: 38945
Language: English / French
Series: Information Report (CWFC - CFS)
CFS Availability: PDF (download)
In 2013, the Canadian Forest Service, Natural Resources Canada, released a best practices guide for generating forest inventory attributes from airborne laser scanning (ALS) data1. The guide was designed to bring together state-of-the-art approaches, methods, and tools to inform, enable, and empower readers to use ALS data to characterize large forest areas in a robust and cost-effective manner. The guide covered the range of topics required to use ALS data for forest inventory, including ALS data acquisition, ground plot measurements, and modelling requirements. Available for download in both English and French language versions from the Canadian Forest Service publications website, the guide was well received by both the Canadian and international forestry communities. In this subsequent guide, we offer practical and relevant recommendations specific to the modelling and mapping of key forestry attributes. This guidance is based on our collective experience, and informed by the relevant scientific literature. Our intended audience is the forest inventory or geomatics professional (or student) who is seeking to better understand the mechanics of implementing an inventory that incorporates ALS data. This guide is not intended to be prescriptive since forest environments vary considerably and technology evolves quickly, and users must select a modelling approach and a sample design that is appropriate for their particular situation and suitable for their information needs. Moreover, logistical considerations, such as computational resources, available statistical expertise, and, most critically, ground sampling costs, must also be considered when selecting a modelling approach. We describe the tradeoffs associated with each decision in the implementation process, thereby enabling practitioners to make informed choices. The additional detail provided in this document is intended to be complementary to the more general overview provided in the best practices guide. In combination, these two documents offer comprehensive guidelines for generating a forest inventory using ALS data and an area-based approach.
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 data have 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). Good practice guidance synthesizes current knowledge from the scientific literature and practical experience 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. This guide focuses specifically on the data requirements and different modelling approaches associated with implementing an area-based approach to estimate forest inventory attributes using ALS data combined with ground plot measurements. The guide is not intended to be prescriptive, as forest environments vary considerably and technology is evolving rapidly. Rather, the guide is intended to support the reader in making informed decisions regarding the various modelling approaches available. The additional detail provided in this document is intended to be complementary to the more general overview provided in a previous best practices guide published in 2013 , and combined these two documents offer comprehensive guidelines for generating a forest inventory using ALS data and an area-based approach.
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