LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California’s Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as “tree-approximate objects” including one dominant tree and up to a few subordinate tree respects LiDAR’s strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.

Forest-restoration efforts are increasing in the western United States in response to realized and expected changes in climate and disturbance regimes. Managers are challenged to find practical and defensible targets to shift forest composition, structure, and spatial pattern to a more resistant and resilient state. The Northeast Washington Forest Vision 2020 project on the Colville National Forest presented an opportunity to map and use previously uncaptured mesic stand-level historical reference conditions to a large restoration project. We reconstructed historical forest conditions in 12 plots across a range of plant-association groups and mapped five restoration treatment units after implementation. We evaluated treatment effectiveness both in terms of meeting the prescriptions’ stated objectives and by similarity to observed reference conditions using metrics of density, species composition, clump-size patterns, and open-space patterns. We found that dry plant associations were historically dominated by distributed clumps of large shade-intolerant trees, whereas cold mesic plant associations were structured as a gap-matrix spatial pattern. Treatments were effective at meeting prescribed density and species-composition targets, but generally resulted in stands that were overly uniform or clumped compared to historical reference conditions.

Implementing treatments to create structural complexity and spatial heterogeneity within forest stands can be difficult and time consuming. We asked if real-time implementation monitoring with an Android OS tablet application can facilitate successful implementation of such treatments. We compared two tree-marking methods—free selection (FS) and individuals, clumps and openings (ICO)—which were used to implement the same silvicultural prescription. ICO marking guidelines differed from FS in one way: inclusion of targets describing the number of tree clumps of different sizes to be left, with real-time monitoring of progress towards these targets using the tablet app. ICO trials were more successful at producing desired conditions. FS trials resulted in stand densities below the target and lacked large and very large tree clumps. Implementation efficiency (trees marked per person-hour) was similar between the two systems. Real-time implementation monitoring of quantitative targets can increase the likelihood of treatment success.

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.