Publications

Purpose of Review: Producing wood of the right quality is an important part of forest management. In the same way that forest growth models are valuable decision support tools for producing desired yields, models that predict wood quality in standing trees should assist forest managers to make quality-influenced decisions. A challenge for wood quality (WQ) models is to predict the properties of potential products from standing trees, given multiple possible growing environments and silvicultural adjustments. While much research has been undertaken to model forest growth, much less work has focussed on producing wood quality models. As a result, many opportunities exist to expand our knowledge. Recent Findings: There has been an increase in the availability and use of non-destructive methods for wood quality assessment in standing trees. In parallel, a range of new models have been proposed in the last two decades, predicting wood property variation, and as a result wood quality, using both fully empirical (statistical) and process-based (mechanistic) approaches. Summary: We review here models that predict wood quality in standing trees. Although other research is mentioned where applicable, the focus is on research done within the last 20 years. We propose a simple classification of WQ models, first into two broad groupings: fully empirical and process-based. Comprehensive, although not exhaustive, summaries of a wide range of published models in both categories are given. The question of scale is addressed with relevance to the range of possibilities which these different types of models present. We distinguish between empirical models which predict stand or tree-level wood quality and those which predict within-tree wood quality variability. In this latter group are branching models (variation up the stem) and models predicting pith-to-bark clear-wood wood property variability. In the case of process-based models, simulation of within-tree variability, and specifically, how that variability arose over time, is always necessary. We discuss how wood quality models are, or should increasingly be, part of decision support systems that aid forest managers and give some perspectives on ways to increase model impact for forest management for wood quality. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R2 = 0.95, mean difference = 3.8 t ha−1), while for JRC-BIO and ESA-BIO, the estimates show R2 of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha−1 with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R2 = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy. © 2022 by the authors.

PRISM, a new forest management decision support system, was developed for United States national forest planning at the same time existing tools were deprecated. PRISM has a friendly graphical user interface to facilitate model development, database management, sensitivity analysis, and data visualization. It integrates SQLITE engine for database storage and exploration. It supports both classic forest planning Model I and II to schedule management activities and achieve multiple management objectives through the lifecycle of a forest plan. Multiple models may be created through the PRISM interface and solved in batch mode for convenient sensitivity analysis. Various options to visualize model outputs are available through the embedded JFREECHART library. The open-source nature of PRISM makes it free to use and allows for long-term software development and collaboration. This paper presents the architecture, core functionality, and selected applications of PRISM in the United States national forest planning. © 2022 Elsevier Ltd

Rapid environmental changes challenge the resilience of wildlands. The western portion of the Lake Tahoe Basin in California is an important ecological and cultural hotspot that is at risk of degradation from current and future environmental pressures. Historical uses, fire suppression, and a changing climate have created forest landscape conditions at risk of drought stress, destructive fire, and loss of habitat diversity. We prospectively modeled forest landscape conditions for a period of 100 years to evaluate the efficacy of 5 unique management scenarios in achieving desired landscape conditions. Management scenarios ranged from no management other than fire suppression to applying treatments consistent with historical fire frequencies and extent (i.e., regular and broadscale biomass reduction). We developed a decision support tool to evaluate environmental and social outcomes within a single framework to provide a transparent set of costs and benefits. Results illuminated underlying mechanisms of forest resilience and provided actionable guidance to decision makers. Sixteen attributes were assessed in the model after assigning weights to each. We found that removing forest biomass across the landscape, particularly when accomplished using extensive fire-based removal techniques, led to highly favorable conditions for environmental quality and promoted overall landscape resilience. Environmental conditions resulting from extensive fire-based biomass removal also had nominal variation over time, in contrast with strategies that had less extensive and/or used physical removal techniques (e.g., mechanical thinning). Our analysis provides a transparent approach to assess large datasets with complex and interacting variables. Ultimately, we aim to provide insights into the complexities of maintaining optimal conditions and managing landscapes to promote ecosystem resilience in a changing world. © 2022, Resilience Alliance. All rights reserved.

The proper choice of the tree species to be grown in a specific forest site requires a good knowledge of the tree species autecology and a comprehensive description of the local environmental conditions. In Belgium (Western Europe), ecological forest site are classified according to three major gradients: climate, soil nutrient (fertility) and soil moisture regimes. Understory indicator species are used by practitioners to determine nutrient and moisture regimes, but requires a significant expertise of forest ecosystems. The present work aims in a first instance at modelling the nutrient and moisture regimes based on species composition. Secondly, a practical decision support tool is developped and made available in order to predict forest nutrient and moisture regime starting from a floristic relevé. To do so, we collected floristic relevés representing understory vegetation diversity in Belgium and covering all the nutrient and moisture gradient. The combination of soil and topographic measurements with the indicator plants presence/absence support forest scientists in inferring a nutrient and moisture regime to each relevé. The resulting dataset was balanced along the different nutrient or moisture regimes and Random Forest classification models were trained in order to predict the forest site characteristic from indicator species presence (or absence). One model was fitted for the prediction of the nutrient regime, exclusively based on the floristic information. A second one was trained to classify the moisture regime. Accurate predictions confirms the appropriate use of indicator species for the Belgian forest site classification. The two models are intregrated in a web application dedicated to forest practionners. This website enables the automatic determination of nutrient and moisture regimes from the species list of a floristic relevé. © 2022

Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elabo-ration of appropriate methods and tools. This paper proposes a novel interactive multi-scenario multiobjective approach to support decision-making and trade-off analysis in sustainable forest landscape planning under multiple sources of uncertainty. It includes new preference simulation models aimed at reducing the decision-maker’s cognitive load and supporting the preference elicitation process. The proposed approach is applied in a case study of long-term forest landscape planning with four sustainability objectives in 12 scenarios and a forestry expert as the decision-maker. The approach is demonstrated to be efficient in exploring trade-offs in different scenarios, helping the expert gain deep insights into the problem, understand the consequences of alternative strategies, and find the most preferred robust strategy. © 2022, Canadian Science Publishing. All rights reserved.

Forest biomass is an appealing bioenergy feedstock due its renewability, availability and potential to stimulate local economies. It is, however, voluminous, with heterogenous fuel characteristics and uncertainties in its supply. The feasibility of a bioenergy facility is contingent on a secure supply of uniform feedstock; a terminal in the supply chain can be useful in this regard. Biomass can be treated in the terminal to meet quality specifications and stored to overcome seasonality and supply disruptions. Nonetheless, such terminals require a significant capital investment; thus, the decision to use a terminal needs to be made judiciously. The decision process must account for a diverse set of factors that influence the terminal’s effectiveness. These include both quantitative and qualitative factors. The objective of this study is to develop a multi-criteria decision-making framework that takes quantitative and qualitative factors into consideration while selecting a terminal. The framework consists of analytical hierarchy process to analyze qualitative information, and a mixed-integer programming model to evaluate quantitative information including fuel quality (moisture content and thermal value). This hybrid framework was implemented in a case study. It proved to be an effective tool for identifying terminals with the highest potential to generate value for the bioenergy supply chain. © 2022 by the authors.

Owing to abnormal climate phenomena worldwide, forests are becoming dry and heat waves have started to occur, increasing the damage caused by wildfires. In addition to causing significant human and material damage, wildfires are also a major cause of critical pollutant emissions, in which fine dust generated by incomplete combustion pollutes the atmosphere, soil, and water. Early detection and monitoring are some of the main ways for minimizing wildfire damage, and a topic of research interest in various fields of artificial intelligence and computer vision. However, the lack of wildfire occurred image datasets is still challenge. Training deep learning model in this environment, can lead mis-detection when burning point is far from the camera or according to objects similar to flame and smoke. Our study attempted to create synthetic wildfire images in various shapes by inserting damage into a free-wildfire image using generative adversarial network (GAN) and Weakly supervised object localization (WSOL). The synthesized image can used as training data for object detection by applying the WSOL method with gradient-weighted activation map (Grad-CAM). Additionally, the YOLOv5s model was improved by adding a channel attention module; sequence-and-excitation (SE) layer and replace loss function as CIoU to address the issue of wildfire false detection in fire-like object and miss detection in small size smoke. Our proposed method, produced results as high as 7.19% in F1-score and 6.41% in average precision (AP) when compared to the existing traditional method. To use a deep learning model in practice, a lightweight model should be applied to the embedded models while maintaining high performance. The developed AI model was applied to the established drone and CCTV-based wildfire monitoring system, and a virtual experiment was conducted by generating virtual wildfires near forests in Korea. © 2022 The Author(s)

The temperate forest understorey is rich in terms of vascular plant diversity and plays a vital functional role. Given the sensitivity of this forest layer to forest management and global environmental change and the limited knowledge on its long-term dynamics, there is a need for decision support systems that can guide temperate forest managers to optimize their management in terms of understorey outcomes. In this study, using understorey resurvey data collected from across temperate Europe, we developed Generalized Additive Models (GAM) to predict four understorey properties based on forest management and environmental change data, and implemented this model in a web-based tool as a prototype understorey Decision Support System (DSS). Using seventy-two combined climate change, nitrogen(N) deposition and forest management scenarios, applied to two case study regions in Europe, we predicted temperate forest understorey biodiversity dynamics between 2020 and 2050. A sensitivity analysis subsequently allowed to quantify the relative importance of canopy opening, N deposition and climate change on understorey dynamics. Our study showed that, regardless of regions, understorey richness and the proportion of forest specialists generally decreased among most scenarios, but the proportion of woody species and the understorey vegetation total cover increased. Climate warming, N deposition, and increases in canopy openness all influenced understorey dynamics. Climate warming will shift composition towards a selection of forest generalists and woody species, but a less open canopy could mitigate this shift by increasing the proportion of forest specialists. The case studies also showed that these responses can be context-dependent, especially in terms of responses to N deposition. © 2022 Elsevier B.V.

Climate change mitigation trade-offs between increasing harvests to exploit substitution effects versus accumulating forest carbon sequestration complicate recommendations for climate beneficial forest management. Here, a time dynamic assessment ascertains climate change mitigation potential from different rotation forest management alternatives across three Swedish regions integrating the forest decision support system Heureka RegWise with a wood product model using life cycle assessment data. The objective is to increase understanding on the climate effects of varying the forest management. Across all regions, prolonging rotations by 20% leads on average to the largest additional net climate benefit until 2050 in both, saved emissions and temperature cooling, while decreasing harvests by 20% leads to the cumulatively largest net climate benefits past 2050. In contrast, increasing harvests or decreasing the rotation period accordingly provokes temporally alternating net emissions, or slight net emission, respectively, regardless of a changing market displacement factor. However, future forest calamities might compromise potential additional temperature cooling from forests, while substitution effects, despite probable prospective decreases, require additional thorough and time explicit assessments, to provide more robust policy consultation. © 2022, The Author(s).