Using airborne laser scanning to assist in substantial forest management decisions for Sechelt's community forest on British Columbia's Sunshine Coast

• Main Author: Yuill, Anna
• Language: English
• Published: University of British Columbia 2016
• Online Access: http://hdl.handle.net/2429/59514

As of 2016, there were 57 community forestry organizations in British Columbia apart of various community forest agreements (CFA). Community forests allow for the development of multi-use management plans to reflect a diverse set of values. The availability of detailed information of the forested area is vital to maximizing a community’s benefits and profits. Airborne laser scanning (ALS) can provide estimates of conventional forest attributes, advance inventory attributes along with spatially describing ecosystem services (ES). This thesis combines ALS data, ground sampling data and vegetation resource inventory (VRI) data for the Sunshine Coast Community Forest (SCCF) located near Sechelt, British Columbia in a case study of the application of ALS data to benefit a community forest. Primary attributes (height, diameter at breast height, stem number, quadratic mean diameter, Lorey’s height, volume and biomass) were calculated using an area-based-approach. A secondary attribute (stem size distribution) was calculated using a two-parameter Weibull probability density function. Finally, a tertiary attribute - site indices - was calculated using maximum height from ALS. The reliability of primary attributes predictions varied, with stem number being the poorest (R²=0.51, p-value<0.001) and Lorey’s height (R²=0.92, p-value<0.001) the most precise. Stem size distribution was predicted with reasonable accuracy using the two-parameter Weibull approach (R²=0.43 and 0.65 for shape and scale, respectively). Site index (RMSE%=35.09), derived from ALS and VRI data was used to predict growth and yield for a timber supply analysis. ALS derived estimates of site indices increased the predicted amount of harvestable timber on the landscape. The spatial description of ES has been identified as a key area where information is lacking, hampering efforts to better manage ES. This thesis describes the ability of ALS to map and monitor ES by reviewing existing ALS research and discussing the applications, limitations, and knowledge gaps for spatially describing ES. I conclude with recommendations for SCCF for using ALS data to map ES. The research in this thesis advances the use of ALS in community forest agreements and demonstrates the feasibility of using ALS data to augment traditional forestry inventory, conduct a timber supply and map a variety of ES. === Forestry, Faculty of === Graduate