Using Airborne LiDAR to Monitor Spatial Patterns in South Central Oregon Dry Mixed-Conifer Forest

Abstract

Abstract A common forest restoration goal is to achieve a spatial distribution of trees consistent with historical forest structure, which can be characterized by the distribution of individuals, clumps, and openings (ICO). With the stated goal of restoring historical spatial patterns comes a need for effectiveness monitoring at appropriate spatial scales. Airborne light detection and ranging (LiDAR) can be used to identify individual tree locations and collect data at landscape scales, offering a method of analyzing tree spatial distributions over the scales at which forest restoration is conducted. In this study, we investigated whether tree locations identified by airborne LiDAR data can be used with existing spatial analysis methods to quantify ICO distributions for use in restoration effectiveness monitoring. Results showed fewer large clumps and large openings, and more small clumps and small openings relative to historical spatial patterns, suggesting that the methods investigated in this study can be used to monitor whether restoration efforts are successful at achieving desired tree spatial patterns. Study Implications: Achieving a desired spatial pattern is often a goal of forest restoration. Monitoring for spatial pattern, however, can be complex and time-consuming in the field. LiDAR technology offers the ability to analyze spatial pattern at landscape scales. Preexisting methods for evaluation of the distribution of individuals, clumps, and openings were used in this study along with LiDAR individual tree detection methodology to assess whether a forest restoration project implemented in a Southern Oregon landscape achieved desired spatial patterns.