Life-cycle analyses, energy analyses, and a range of utilization efficiencies were developed to determine the carbon dioxide (CO₂) and fossil fuel (FF) saved by various solid wood products, wood energy, and unharvested forests. Some products proved very efficient in CO₂ and FF savings, while others did not. Not considering forest regrowth after harvest or burning if not harvested, efficient products save much more CO₂ than the standing forest; but wood used only for energy generally saves slightly less. Avoided emissions (using wood in place of steel and concrete) contributes the most to CO₂ and FF savings compared to the product and wood energy contributions. Burning parts of the harvested logs that are not used for products creates an additional CO₂ and FF savings. Using wood substitutes could save 14 to 31% of global CO₂ emissions and 12 to 19% of global FF consumption by using 34 to 100% of the world’s sustainable wood growth. Maximizing forest CO₂ sequestration may not be compatible with biodiversity. More CO₂ can be sequestered synergistically in the products or wood energy and landscape together than in the unharvested landscape. Harvesting sustainably at an optimum stand age will sequester more carbon in the combined products, wood energy, and forest than harvesting sustainably at other ages.

Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies.

Functional restoration of forests can be addressed as a combined socioeconomic and ecological complex system that involves both spatial and temporal hierarchies. Neither socioeconomic nor ecological systems are stable or have any central control or organization. Instead, they can change at different scales in many ways and be more or less vibrant—and more or less useful to people for different values. Restoration can direct these changes by determining what to control at each hierarchical level and what to allow to change on its own at a given time. Restoration at longer times and broader spatial scales involves much imprecision and uncertainty and is done with a few, careful controls. These controls are passed to each smaller spatial and temporal scale and other, more specific controls are added. Much has changed since most United States forest policies were instituted, so many of these policies need to be reevaluated. Rather than repeat policies and practices that worked in the past, we can look for new restoration solutions that are in line with our current understanding of complex systems. Many old forestry policies in the United States need changing to restore the forest functions in ways helpful to people.

The forest stand structure class “old-growth” has previously been qualitatively described as having several distinct “sub-structures.” Species composition, diameter distribution, and other structural features commonly associated with old-growth in the Pacific Northwest are quite variable. We determined which quantitative stand structure variables are commonly found together using the Spearman correlation and non-metric multidimensional analysis. Some features were more commonly found together than others, indicating different old-growth stand types, or sub-structures. Cluster analysis classified the old-growth forests into four groups: Douglas-fir dominance, shade tolerant species dominance, and intermediate groups. The intermediate groups were split by the density of large logs and large shade tolerant trees. The old-growth sub-structures appear to change from one to another as the old forest develops.