Connecting Forestry Learning Objectives to Urban Forest Types

Abstract

Applied educational opportunities in forestry undergraduate curricula are essential for a complete postsecondary degree program. Walking distance to local urban forests present a way to teach forestry students in applied settings, while reducing the time, cost, and travel logistics. A case study at a Canadian university (University of British Columbia) was used to connect urban forest canopy cover to forestry learning objectives and walking time to the main teaching building. Individual tree canopies were identified with light detection and ranging data and aggregated to 0.05 ha grid sections. Using canopy cover and forest arrangement, the urban forest was classified into closed, open, small, sparse, or non- forest classifications. Forestry learning objectives were matched with each forest classification in conjunction with walkability to identify critical local location for forestry education. Results identified key areas suitable for teaching forestry and for linking forestry educational values with easily accessible high value locations. Study Implications: Applied educational opportunities for undergraduate forestry students are critical for ensuring hands-on, real world experiences and essential in postsecondary forestry degrees. Local urban forests present an opportunity to allow students access to these experiences regularly. Connecting forestry learning objectives with local urban forest types allowed for the identification of key, high-value learning locations. The information and methodology from this research provide insight into explicitly classifying areas for forestry educational purposes with the goal of promoting outdoor applied educational opportunities for forestry undergraduate students.

Combining aerial photos and LiDAR data to detect canopy cover change in urban forests

“…A sample unit size of 0.05 ha was selected. The sample unit size was selected to stay consistent with previous research by Coupland et al [ 34 ]. A calibration overlay of 10x10 dots was used to ensure accuracy and consistency of TCC values.…”

“…LiDAR TCC was calculated using the R-package “Forest Tools” [ 33 ], and followed the forest cover classification methods from Coupland et al’s research connecting urban TCC with forestry learning objectives [ 34 ]. Forest tools delineated tree canopies from CHM produced from high-density point clouds by identifying local maxima and creating a window radius to look for the surrounding local minimum.…”