Use of Individual Tree Detection when Quantifying Forest Structure
Presenter(s): Ryan Blackburn, Andrew Sánchez Meador
Determining the success of forest restoration projects requires methodologies that can quantify forest conditions at landscape-scales while simultaneously providing fine-scale metrics (e.g., tree and group attributes). Individual tree detection (ITD) algorithms, as applied to lidar, have displayed the versatility needed to take on this challenge. Several algorithms exist, each with their own nuances. To evaluate ITD algorithms, we estimated basal area and tree density across a range of restoration treatment intensities (i.e., thinning and repeated burning) using three different algorithms. We then used the most accurate ITD algorithm and patch metrics to quantify structural differences among treatments. We found applications of the default Li et al. algorithm best explained variation in tree height and tree density. ITD allowed us to measure tree density per patch which decreased with treatment intensity. Overall, increasing treatment intensity resulted in more open conditions.
Key Takeaway: The audience will take away an understanding of how lidar can be used to detect individual trees and how to apply this when quantifying forest structure.
Intended Audience: Forest managers, GIS analysts, restoration ecologists, people that are generally interested in the use of lidar for forest applications
About the presenter(s):
Ryan is a PhD student from Northern Arizona University. His current work focuses on the quantification of forest structure and composition using lidar and other remote sensing techniques. By assessing forest conditions, he hopes to provide valuable information which can improve decision making within areas of ecology and land management.
Andrew Sánchez Meador is an associate professor in the School of Forestry at Northern Arizona University.