The need for fuel reduction treatments and the restoration of ecosystem resilience has become widespread in forest management given fuel accumulation across many forested landscapes and a growing risk of high-intensity wildfire. However, there has been little research on methods of assessing the effectiveness of those treatments at landscape scales. Most research has involved small-scale opportunistic case studies focused on incidents where wildland fires encountered recent restoration projects. It is important to assess whether restoration practices are successful at a landscape scale so improvements may be made as treatments are expanded and their individual effectiveness ages. This study used LiDAR acquisitions taken before and after a large-scale forest restoration project in the Malheur National Forest in eastern Oregon to broadly assess changes in fuel structure. The results showed some areas where treatments appeared effective, and other areas where treatments appeared less effective. While some aspects could be modified to improve accuracy, the methods investigated in this study offer forest managers a new option for evaluating the effectiveness of fuel reduction treatments in reducing potential damage due to wildland fire.
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.
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