The development of forestry as a scientific and management discipline over the last two centuries has mainly emphasized intensive management operations focused on increased commodity production, mostly wood. This “conventional” forest management approach has typically favored production of even-aged, single-species stands. While alternative management regimes have generally received less attention, this has been changing over the last three decades, especially in countries with developed economies. Reasons for this change include a combination of new information and concerns about the ecological consequences of intensive forestry practices and a willingness on the part of many forest owners and society to embrace a wider set of management objectives. Alternative silvicultural approaches are characterized by a set of fundamental principles, including avoidance of clearcutting, an emphasis on structural diversity and small-scale variability, deployment of mixed species with natural regeneration, and avoidance of intensive site-preparation methods.

A large research effort was initiated in the 1990s in western United States and Canada to investigate how the development of old-growth structures can be accelerated in young even-aged stands that regenerated following clearcut harvests, while also providing income and ecosystem services. Large-scale experiments were established to compare effects of thinning arrangements (e.g., spatial variability) and residual densities (including leave islands and gaps of various sizes). Treatment effects were context dependent, varying with initial conditions and spatial and temporal scales of measurement. The general trends were highly predictable, but most responses were spatially variable. Thus, accounting for initial conditions at neighborhood scales appears to be critical for efficient restoration. Different components of stand structure and composition responded uniquely to restoration thinnings. Achieving a wide range of structures and composition therefore requires the full suite of silvicultural treatments, from leave islands to variable density thinnings and creation of large gaps. Trade-offs among ecosystem services occurred as result of these contrasting responses, suggesting that foresters set priorities where and when different vegetation structures are most desirable within a stand or landscape. Finally, the results suggested that foresters should develop restoration approaches that include multiple treatments.

Uncertainty surrounding global change impacts on future forest conditions has motivated the development of silviculture strategies and frameworks focused on enhancing potential adaptation to changing climate and disturbance regimes. This includes applying current silvicultural practices, such as thinning and mixed-species and multicohort systems, and novel experimental approaches, including the deployment of future-adapted species and genotypes, to make forests more resilient to future changes. In this chapter, we summarize the general paradigms and approaches associated with adaptation silviculture along a gradient of strategies ranging from resistance to transition. We describe how these concepts have been operationalized and present potential landscape-scale frameworks for allocating different adaptation intensities as part of functionally complex networks in the face of climate change.

Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.