Publications

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model ‘NextStand’ (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables. Copyright: © 2023 Kaasik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

A well-managed forestry operation must ensure efficient workflows right from the planning stage. However, especially in topographically challenging terrain or in stepped forests, it is not always easy to combine operational execution on the site with silvicultural planning and control. With the automated selection of management units, planners have a powerful method at hand to reflect the individual needs of the forestry operation in an optimal area division. © 2023, Schweizerischer Forstverein. All rights reserved.

Including biodiversity indicators into forest planning is increasing in importance as it is a supporting service for other ecosystem services. To forecast biodiversity potential, forest planners use models that simulate forest growth and other biological and ecological processes. As models are simplifications of reality, they may ignore components of biodiversity's multi-scales and multi-facets. To address this issue, we explored if current models used in forest planning can characterize biodiversity in a similar way as it is defined in ecology. We performed a narrative review of ecological papers to identify the main aspects of biodiversity defined in ecology. We then reviewed 64 forest planning articles to identify the indicators they use and what aspects of biodiversity they represent. We compared the aspects identified in ecology and forest planning to evaluate the discrepancies between the two fields and suggest improvements for future biodiversity studies in forest planning. We identified spatial and temporal connectivity, structure, and abiotic factors as the main biodiversity drivers defined in ecology and genetic, species, and functional diversity as the main responses. Based on this classification, we found that biodiversity models used in forest planning mainly focus on structure and species elements, with minor focus on connectivity and functions and none on genetic diversity. We found that most studies base their choice of biodiversity indicators on the outputs available from traditional forest simulators. Additionally, many studies do not frame biodiversity rigorously or acknowledge its complexity. This trend is explained by the traditional focus of forest planning on the economic value of the forest and maximization of timber volumes rather than its ecological value and the presence of diverse habitats. Our results describe and quantify the importance given to the different biodiversity aspects in forest planning studies and highlight the current limitations. We anticipate that improvements can be achieved through the inclusion of connectivity and we suggest paths to improve future biodiversity models. © 2023 The Author(s)

The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high-resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km2 subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre-thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand-scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration. © 2023 The Authors. Ecohydrology published by John Wiley & Sons Ltd.

Stand density management diagrams (SDMDs) are robust decision-support tools available to forest managers under limited information. SDMDs which are based on empirical models at stand level, graphically represent the temporal relationships among stand density, and different stand variables such as quadratic mean diameter, dominant height, and mean tree volume. They are used to define initial planting spacing or thinning interventions, to meet various management objectives. Nowadays, there is a growing interest in mixed-species forests as an option for adaptive forest management, where they are considered a guarantor to safeguarding a wide variety of ecosystem services within the framework of sustainability. But there is still a lack of knowledge and efficient tools and models for mixed stands such as SDMDs. This study aims to develop an SDMD for Pinus sylvestris L. and Pinus pinaster Ait. mixed stands in the Sierra de la Demanda (Spain) using data from the third Spanish National Forest Inventory. Both species are two of the most important conifers in Europe and the western Mediterranean basin. Different variables can be used to develop an SDMD. In this case, quadratic mean diameter, dominant height, total stand volume, number of trees per hectare, and stand density index were used. These equations were fit by simultaneous fitting including a new variable representing the proportion of both species in the mixed stand. The results of the simultaneous fitting showed the new variable representing the proportion of both species was not significant. Based on that, the SDMD was constructed without including mixture degree. This SDMD can be used by forest managers as an efficient tool to plan thinning operations. © SISEF.

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. © 2023 by the authors.

Plot based forest/vegetation data is used to establish current conditions and inform natural resources management decisions. The Florida Department of Environmental Protection – Division of Recreation and Parks (DRP) manages 175 state parks. As part of its mission, DRP strives to restore landscapes and natural communities by reintroducing dynamic natural processes such as fire. DRP also uses other methods of active management to achieve desired future conditions (DFCs) in multiple communities including those characterized by longleaf pine (Pinus palustris Mill.) and native groundcover (GC) species. To meet the challenge of managing natural resources across the State, DRP initiated an objective and repeatable forest/vegetation inventory system. The primary objective of this paper was to analyze and summarize the resultant data and compare current community type (ComType) vegetation conditions. Current conditions were quantified using nested plots distributed within sample areas across 73 state parks and 15 ecoregions via the line-plot method. Over 37,000 plots were inventoried; 36% were in pine flatwoods. Ten actively managed and dominant ComTypes were central to this paper and when aggregated by ecoregion, 76 ComType by ecoregion (CTER) groups were examined. Descriptive statistics for various measurements, e.g., diameter at breast height, height, and density, were calculated for overstory, midstory and understory (vegetation layer) separately per CTER. Mean importance value index (IVI) was calculated by species or species-group per vegetation layer and CTER. Community classification was conducted via hierarchical clustering of CTERs per vegetation layer using mean IVI scores. Across CTERs, pine overstory and midstory abundance/stocking levels were generally low, non-pine overstory and midstory stocking levels were high, pine regeneration was sparse, and GC was dominated by leaf litter, pine straw, and non-pine woody seedlings. Results strongly suggest that some ComTypes were similar within certain ecoregions. Various pine species are considered dominant or one of the prominent overstory species for eight of the ten ComTypes. The ability to manage upland pine ComTypes using natural regeneration systems will become increasingly challenging in the near- to medium-term given relatively low overstory pine stocking for most CTERs, and the virtual lack of young pines within midstory and understory layers. Further investigations could help identify potential causal agents concerning low young pine stocking levels, e.g., timing and frequency of prescribed fires, frequency of logging, and/or increased competition from high densities of midstory non-pines and appropriate natural regeneration systems, e.g., seed tree, shelterwood, or group selection, or underplanting longleaf pine, that would accelerate understory pine recruitment. © 2023 Elsevier B.V.

Common understory vegetation species such as the ericaceous shrubs bilberry (Vaccinium myrtillus), cowberry (V. vitis-idaea) and heather (Calluna vulgaris), are key forage plant species for moose and other large herbivores, as well as fulfilling many additional ecosystem functions and services. Here we developed models to predict above-ground biomass of these ericaceous species in coniferous forests, using data on their percentage cover, height, and different stand characteristics. We also built models to understand how the aforementioned variables affect the proportion of the shrubs commonly utilized as forage by large herbivores. We found that the percentage cover of shrubs was the most important explanatory variable when predicting above-ground biomass, explaining 51%, 47% and 71% of the variation (marginal R2) in bilberry, cowberry and heather biomass, respectively. By adding ramet height to the model with percentage cover, the variation explained increased to 77% for bilberry, 75% for cowberry and 87% for heather. The best outcome for candidate models was obtained by adding stand site index and spruce basal area to the model, improving the variation explained in bilberry to 83%, to 81% for cowberry, and 91% for heather. When modelling the proportion of the shrubs commonly utilized as forage by large herbivores, stand site index and spruce basal area often played important roles. Some of the best fitting models for forage biomass explained 51% of the variation in bilberry, 59% in cowberry and 30% in heather. Site location did not have a major role in improving the variability explained in either type of model, which indicated the applicability of the models regardless of study location. Our models therefore have a high potential to be implemented in forestry decision support systems. Their inclusion should provide better large-scale estimations of forage resources, aiding forest management, and thereby taking an important step forward to determine the ecosystem carrying capacity of large herbivores. © 2023 The Author(s)

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen’s flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper. © 2023 by the authors.

The transition in the global energy matrix to sources of renewable electric generation has been the main demand of countries in the face of a state of climatic emergency. Forest biomass is a potential alternative to meet the growing demand. The expansion of thermoelectric projects based on forest residues highlights the need to assess raw materials' availability and land suitability. This is usually a problem of spatial decision and geographic intelligence under a holistic perspective since, in planning these projects, it is essential to understand the economic, environmental, and social feasibility. In this article, we propose a decision support system based on multi-criteria modeling on a GIS structure associated with a mathematical optimization model for zoning the suitability and availability of land for constructing thermoelectric power plants. The geospatial model for determining priority sites integrated various environmental, social, and economic criteria and constraints. The AHP method was used to weigh and estimate the relative importance weights of the criteria for modeling territorial aptitude. The results of the GIS stage formed the basis of a mixed-integer linear programming model that incorporated technical and economic aspects for a better allocation of financial resources. The minimized cost function included transport costs, harvesting, forestry, purchasing land/leasing land/forest outgrower schemes, and purchasing wood from the market to supply the biomass. We used a sizeable hydrographic basin in southeastern Brazil as a case study for the demonstration. The mapping of suitability and constrained zones identified the classes of preference and availability of land. The proposed mathematical model recognized sets of supply locations (new plantations or existing areas) and minimized the project's final cost to supply biomass to meet the demand of potential thermoelectric plants that are candidates for implementation. Despite the problem's large scale and the large data set, our findings indicated that we could provide a broad and multidimensional view of the potential of developing electrical generation systems based on forest biomass. The methodology and the model proposed in this study can be replicated in other global regions and modified to assess other resources from different biomasses and bioenergy systems. Our study promotes the UN's sustainable development goals, particularly SDG 7 (Affordable and clean energy) and SDG 13 (Climate Action). © 2023 Elsevier Ltd