Forest Decision Support Systems Research Articles

Forestry Scenario Modelling: Qualitative Analysis of User Needs in Lithuania

This paper aims to demonstrate the use of qualitative research methods, specifically in-depth interviews, to explore the intangible and often difficult-to-quantify needs for forestry scenario modelling in Lithuania, which are frequently not adequately perceived. The study involved informants representing key actors in forest policy, forest management, research, and education. A total of 21 informants from 11 different institutions, which hold significant power and expertise in forest decision making, were interviewed. The purpose of these interviews was to gather their perspectives on the potential forest decision support system in the country, aiming to address most of their needs. The interview questions explored various aspects, including the requirements for forestry scenario modelling, the desired level of detail and information content for decision making, and both functional and nonfunctional requirements for the scenario modelling system. It is worth noting that the expected functionality of the planned forest DSSs aligns with modern international standards. Nevertheless, the diversity of perspectives, wishes, visions, and intentions of key Lithuanian forestry actors regarding the aims, objectives, and essential functionality of forestry scenario modelling tools were identified. The understanding of the requirements for modern forest DSSs was greatly influenced by the current forestry paradigms in the country and the professional experiences of individual informants. In conclusion, our findings demonstrate that the utilization of qualitative research, particularly through in-depth interviews, has proven to be a highly effective tool for accurately specifying the requirements of a modern forest DSS. It helped mitigate preconceived notions and address gaps in the envisioned product, specifically by developing a framework of core solutions for the national forestry and land-use scenario modelling system.

Adapted forest management to improve the potential for reindeer husbandry in Northern Sweden

In northern Sweden, improvements of grazing conditions are necessary for the continuation of traditional, natural pasture-based reindeer husbandry. Ground and tree lichen constitute the main fodder resource for reindeer during winter but have reached critically low levels. Using a forest decision support system, we prescribe adapted forest management to improve the preconditions for reindeer husbandry and compare outcomes with the continuation of current forest management. We found that adapted management increases the forest area with ground lichen habitat by 22% already within 15 years, while a continuation of current management would result in a further decrease in ground lichen. Tree lichen habitat can be retained and increased in all scenarios, which is important in a changing climate. Compared to a continuation of current practices, adapted management with significantly improved conditions for lichen resulted in a decrease in net revenues from wood production by 11–22%.

The multi-faceted Swedish Heureka forest decision support system: context, functionality, design, and 10 years experiences of its use

For several decades, computerized forest decision support systems (DSS) have helped managers and decision makers to analyze different management options and supported the search for preferred management alternatives. In Sweden, a country rich in forests and with a long tradition in intensive forest management, such systems have been developed and available since the 1970s. Changes in societal as well as in forest owners’ preferences and objectives in the 1990s led to a need for forest DSS handling broader perspectives compared to precedent single-objective timber-oriented systems. In Sweden, this led to the initiation of a research programme in the beginning of the 2000s aiming at developing a versatile and multi-objective forest DSS, resulting in the first version of the Heureka forest DSS released in 2009. The system handles several forest values, such as timber and biofuel production, carbon sequestration, dead wood dynamics, habitat for species, recreation and susceptibility to forest damages (spruce bark beetle, wind-throw and root rot). It contains a suite of software for different problem settings and geographical scales and uses simulation as well as optimization techniques. Three software handle projections of the forest using a common core of growth and yield models for simulating forest dynamics. A fourth software, built for multi-criteria decision analysis and including a web-version, enables also group decision making and participatory planning. For more than 10 years, the Heureka system has been used in teaching, environmental analysis, research and as decision support in practical forestry. For example, several research groups using the system for analyses in different problem areas have so far published more than 80 scientific papers. The system is used for nation-wide forest impact analysis for policy support and all large and many medium-sized forest owners use it for their long-term forest planning, meaning that it directly influences forest management decisions and activities on more than 50% of the Swedish forest area. Besides presenting the present system and its use, we also discuss lessons learned and potential future development.

The effect of spatial and temporal planning scale on the trade-off between the financial value and carbon storage in production forests

BackgroundIncreasing carbon stock in standing forests is one of the proposed ways to mitigate climate change. However, in production forests, this typically would lead to reduced harvesting possibilities and thus reduced financial gain for the forest owners. The size of this reduction should depend on the chosen target level of the carbon stock as well as the required speed of accumulation. Furthermore, due to landscape heterogeneity, the size of the loss can be expected to vary the planning scale, often related to forest property size. AimThis study aimed to quantify the effects of spatial and temporal planning scales on the severity of the trade-off between Net Present Value (NPV) of future timber sales and carbon storage in production forests in Southern Sweden. MethodsWe used the Heureka PlanWise forest decision support system with built-in Linear Programming functionality. We created six Production Possibility Frontiers (PPF) that quantified the trade-off for the combinations of two scenarios for timing of carbon accumulation (either by 2100 or by 2100 with an intermediate target by 2045) and three spatial management scales (∼3300 ha, ∼300 ha, and ∼60 ha; 1068 stands). ResultsThere was a strong effect of temporal scale, with consistently lower NPV, with the same carbon stock in 2100, when the intermediate target for 2045 was applied. The effect of the spatial scale was only apparent between the smallest (50 ha) scale and the larger scales (300 and 3300 ha), with consistently lower NPV with the same carbon stock at the smallest scale. ConclusionWe conclude that both the effects of spatial management scale and temporal scale on the cost of carbon storage should be considered in relation to potential climate policies.

Development and implementation of a spruce bark beetle susceptibility index: A framework to compare bark beetle susceptibility on stand level

The spruce bark beetle (spruce bark beetle) (Ips typographus) is one of the major disturbance agents in European forests. Damage by spruce bark beetle is expected to increase in the future, as a result of e.g. increased temperatures. However, not all forest stands are equally vulnerable. Therefore, describing the relative difference in susceptibility of different forest stands to spruce bark beetle infestation and to try to estimate changes in susceptibility under different management or climate scenarios is necessary to support decision making on forest management. We present a spruce bark beetle susceptibility index, which describes the relative susceptibility of forest stands to spruce bark beetle infestation. The index is based on empirical findings and expert opinion, and takes both climatic and stand variables into account. The index can be implemented in forest simulation programs. The susceptibility index was implemented in Heureka, a forest decision support system. To demonstrate the use of the index, simulations were run for three management scenarios: baseline; even-aged management focused on conifers, longer rotation: same as baseline but with longer rotation periods and mixed forest: same as baseline but retaining a higher share of broadleaves. For this purpose, an area of 2451 ha consisting of 751 stands was used. The index value per stand per five-year time period was obtained from the simulations. The index was calculated individually, per management strategy, for all 751 stands and thereafter mean index and harvest volume was obtained for the whole area. Mean susceptibility was higher, and harvest slightly lower, in the longer rotation scenario, compared with the baseline, while there were no differences between baseline and mixed. At individual stand level, the differences are more nuanced and, for example, certain stands have lower susceptibility in the mixed compared with the baseline scenario. The ability to simulate forest development and simultaneously get a measure of spruce bark beetle susceptibility will enable forest owners to identify vulnerable stands and evaluate effects of different management decisions to reduce the risk for future economic losses.

A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study.

Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients' needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient's work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.

Pre-Commercial Thinning Increases the Profitability of Norway Spruce Monoculture and Supports Norway Spruce–Birch Mixture over Full Rotations

Pre-commercial thinning (PCT) is a common measure in Norway spruce (Picea abies L. Karst.) stands but the profitability of doing PCT and timing of PCT has not been fully investigated over a full rotation. Further, limited knowledge is available for mixed forest management compared to monocultures. In this study, different PCT strategies were tested to investigate the effect of PCT and timing of PCT on the production and profitability of Norway spruce monocultures and mixed Norway spruce–birch stands. A forest decision support system was used to simulate stand development during the whole rotation. Our study findings show that there is a positive effect of PCT on Norway spruce plantations’ long-term profitability but that the timing of PCT has little effect on profitability. However, site variation might influence the effect of PCT timing on the profitability of Norway spruce stands. Moreover, we also found that retaining 1000 Norway spruce ha−1 and 1000 birch ha−1 after PCT supports a mixture of Norway spruce and birch over a full rotation, with little or no economic loss compared to pure Norway spruce stands. Therefore, such a mixture can lead to profitable production while still providing other ecosystem services.

Nordic forest management towards climate change mitigation: time dynamic temperature change impacts of wood product systems including substitution effects

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.

A NEW MODEL FOR DETERMINING TREE SPECIES COMPATIBLE WITH THE ECOLOGICAL CONDITIONS OF THE AREAS TO BE AFFORESTED

Afforestation is an indispensable practice for the sustainability of forests in the absence of sufficient forest. Planting tree species compatible with local environmental factors will contribute to the strengthening and protection of forests. This study aims to develop a statistical model to determine the optimal growing areas of tree species compatible with local environmental factors using GIS. While creating this model, nine main environmental factors (lithology, landform, elevation, slope, aspect, temperature, precipitation, soil type, soil depth) that affect the distribution of tree species were determined. These factors were analyzed along with their sub-criteria. Analyzes were done using the AHP method. According to the analysis results, the distribution of tree species in the study area is affected by temperature, precipitation, elevation, slope, landform, and soil depth. The optimal growth areas of each tree species are quite different from each other. The results show that this method is easy to apply in forest planning and offers forest decision support systems opportunities to create a wide variety of alternative plans.

Specified resilience value of alternative forest management adaptations to storms

ABSTRACT Resilient ecosystems provide natural insurance value, or resilience value, to the landowner and to society at large. In response to global calls for integrating biodiversity in sector policy and planning, we analysed the specified resilience value by simulating three storm regimes and five management scenarios: Business As Usual/BAU (spruce-dominance), Spruce Monoculture, More Broadleaves, Continuous Cover Forestry (CCF), and No Thinnings. The forest decision support system Heureka RegWise was used to simulate the effects of storms on forest dynamics and Net Present Value (NPV). No Thinnings, CCF and More Broadleaves were more resilient to storms (reduced damage cost) compared to BAU. BAU had the highest NPV only if storms are ignored, a common assumption in today’s forest planning. Given storms, No Thinnings maximises NPV on landscape level. On the 20% most vulnerable plots the NPV was much higher for No Thinnings and slightly higher for CCF and More Broadleaves, compared to BAU. CCF and More Broadleaves also provide nature-based solutions (co-benefits) including public goods. However, forestry adaptations to storms are slow in Sweden, in contrast to e.g. German state forestry which emphasises maximising tree growth and resilience to several stresses and disturbances rather than NPV optimisation.

Role and potential of decision support systems in forestry

Decision Support Systems (DSS) are essential tools for forest management practitioners to help take account of the many environmental, economic, administrative, legal and social aspects in forest management. This paper is concerned with the technique to develop DSS for forest management system to evaluate models and methods considering all the important factors to categorize the problem. The problem is based on temporal and spatial parameters, number of objectives, decision makers and goods and services. Some of these problem dimensions are inter-related, and we also found a significant relationship between various methods and problem dimensions, all of which have been analysed using contingency tables. The results showed that 63% of forest DSS use simulation modeling methods and these are particularly related to the spatial context and spatial scale and the number of people involved in taking a decision. The analysis showed how closely Multiple Criteria Decision Making is linked to problem types involving the consideration of the number of objectives, also with the goods and services. On the other hand, there was no significant relationship between optimization and statistical methods and problem dimensions, although they have been applied to approximately 60% and 16% of problems solved by DSS for forest management, respectively. Metaheuristics and spatial statistical methods are promising new approaches to deal with certain problem formulations and data sources. Nine out of ten DSS used an associated information system, but the availability and quality of data continue to be an important constraining issue, and one that could cause considerable difficulty in implementing DSS in practice. Very often DSS is used largely based to study market economy. The results suggest a strong need to improve the capabilities of DSS in this regard, developing and applying MCDM models and incorporating them in the design of DSS for forest management in coming years.

Challenges and opportunities in developing decision support systems for risk assessment and management of forest invasive alien species

Biological invasions represent an increasing threat to ecosystems worldwide, with negative ecological and socio-economic impacts, whereas risk assessment and management remain challenging. The development of decision support systems (DSS) has the potential to help decision-makers and managers mitigate invasive species, but few DSS exist for forest invasive alien species (FIAS). The use of DSS in forestry is not new but they represent an asset in decision making in times of increasing complexity of issues foresters face and factors to consider. Yet, few forest DSS address the problem of FIAS. In this review, we identify key elements of the FIAS risk-assessment and management decision-making process, discuss these elements with a model-based DSS development perspective, and summarize outstanding challenges and opportunities for FIAS DSS development. FIAS DSS should not only estimate the probability of FIAS invasion but also consider forest vulnerability and quantify exposure (i.e., value at risk), while allowing different threat scenarios and possible solutions to be compared. Such a complete risk assessment and management calls for integrative modelling approaches that explicitly link different components of FIAS invasion, management, and impact assessment into a DSS. Such integrative modelling is challenging and may require collaboration among experts of different domains. International collaboration is also needed to facilitate data exchange, as the lack of data is one of the main challenges. In many cases, data and ecological knowledge of invasive species are too limited (in quantity or quality) to constitute useful input to DSS or their components (e.g., species distribution model). Another challenge is to better consider the multiple sources of uncertainties inherent to modelling invasions (e.g., host preferences and behavior, forest vulnerability, potential impacts, and cost and benefits of mitigation actions) when assessing FIAS risk and communicating results from risk assessment. Communication with stakeholders and DSS end-users, in fact, appears as one of the keys to successful DSS development and appropriation, not only to ensure that they correspond to end-users’ needs but also to ensure ease of use, functionality, and good visualization of DSS outputs.

Spatial Pattern of Climate Change Effects on Lithuanian Forestry

Research Highlights: Validating modelling approach which combines global framework conditions in the form of climate and policy scenarios with the use of forest decision support system to assess climate change impacts on the sustainability of forest management. Background and Objectives: Forests and forestry have been confirmed to be sensitive to climate. On the other hand, human efforts to mitigate climate change influence forests and forest management. To facilitate the evaluation of future sustainability of forest management, decision support systems are applied. Our aims are to: (1) Adopt and validate decision support tool to incorporate climate change and its mitigation impacts on forest growth, global timber demands and prices for simulating future trends of forest ecosystem services in Lithuania, (2) determine the magnitude and spatial patterns of climate change effects on Lithuanian forests and forest management in the future, supposing that current forestry practices are continued. Materials and Methods: Upgraded version of Lithuanian forestry simulator Kupolis was used to model the development of all forests in the country until 2120 under management conditions of three climate change scenarios. Selected stand-level forest and forest management characteristics were aggregated to the level of regional branches of the State Forest Enterprise and analyzed for the spatial and temporal patterns of climate change effects. Results: Increased forest growth under a warmer future climate resulted in larger tree dimensions, volumes of growing stock, naturally dying trees, harvested assortments, and also higher profits from forestry activities. Negative impacts were detected for the share of broadleaved tree species in the standing volume and the tree species diversity. Climate change effects resulted in spatially clustered patterns—increasing stand productivity, and amounts of harvested timber were concentrated in the regions with dominating coniferous species, while the same areas were exposed to negative dynamics of biodiversity-related forest attributes. Current forest characteristics explained 70% or more of the variance of climate change effects on key forest and forest management attributes. Conclusions: Using forest decision support systems, climate change scenarios and considering the balance of delivered ecosystem services is suggested as a methodological framework for validating forest management alternatives aiming for more adaptiveness in Lithuanian forestry.

Forest decision support systems for the analysis of ecosystem services provisioning at the landscape scale under global climate and market change scenarios

Sustainable forest management is driving the development of forest decision support systems (DSSs) to include models and methods concerned with climate change, biodiversity and various ecosystem services (ESs). The future development of forest landscapes is very much dependent on how forest owners act and what goes on in the wider world; thus, models are needed that incorporate these aspects. The objective of this study is to assess how nine European state-of-the-art forest DSSs cope with these issues. The assessment focuses on the ability of these DSSs to generate landscape-level scenarios to explore the output of current and alternative forest management models (FMMs) in terms of a range of ESs and the robustness of these FMMs in the face of increased risks and uncertainty. Results show that all DSSs assessed in this study can be used to quantify the impacts of both stand- and landscape-level FMMs on the provision of a range of ESs over a typical planning horizon. DSSs can be used to assess how timber price trends may impact that provision over time. The inclusion of forest owner behavior as reflected by the adoption of specific FMMs seems to be also in the reach of all DSSs. Nevertheless, some DSSs need more data and development of models to estimate the impacts of climate change on biomass production and other ESs. Spatial analysis functionality needs to be further developed for a more accurate assessment of the landscape-level output of ESs from both current and alternative FMMs.

Implementing Climate Change and Associated Future Timber Price Trends in a Decision Support System Designed for Irish Forest Management and Applied to Ireland’s Western Peatland Forests

Research Highlights: Predicting impacts on forest management of Climate Change (CC) and dynamic timber prices by incorporating these external factors in a Forest Management Decision Support System (FMDSS). Background and Objectives: Forest managers must comply with Sustainable Forest Management (SFM) practices, including considering the long-term impacts that CC and the bioeconomy may have on their forests and their management. The aims of this study are: (1) incorporate the effects of CC and Dynamic Prices (DP) in a FMDSS that was developed for Ireland’s peatland forests, (2) analyse the impact of global climate and market scenarios on forest management and forest composition at the landscape level. Materials and Methods: Remsoft Woodstock is a strategic planning decision support system that is widely used for forest management around the world. A linear programming model was developed for Ireland’s Western Peatland forests while using Woodstock. Data from Climadapt, which is an expert-based decision support system that was developed in Ireland, were used to include CC effects on forest productivity and species suitability. Dynamic market prices were also included to reflect the changing demands for wood fibre as part of the European Union (EU) and global effort to mitigate CC. Results: DP will likely have more impact on harvest patterns, volumes, and net present value than CC. Higher assortment prices, especially for pulpwood, stimulate the harvesting of forests on marginal sites and off-set some of the negative CC growth impacts on forest profitability. Conclusions: Incorporating CC and bioeconomy prices in a forest decision support system is feasible and recommendable. Foresters should incorporate the expected global changes in their long-term management planning to mitigate the negative effects that un-informed management decisions can have on the sustainability of their forests.

Incorporating a Global Perspective Into Future-Oriented Forest Management Scenarios

This research serves to integrate the concept of an “ecological footprint” into future-oriented forest management scenarios. Scenarios are commonly used to explore stakeholder perceptions of possible forest futures, and are typically focused on the local impacts of different management choices. This article illustrates how global footprint analysis can be incorporated into scenarios to enable local forest stakeholders in the EU to consider the impacts of their local decisions at national and global levels. This illustration could be helpful to the construction of a forest decision support system that includes wood trade information and social processes (simulation of management decisions under changing political/economic conditions). It finds that different future forest management scenarios involving a potential increase or decrease of the harvested timber, or potential increase or decrease of subsidies for forest protection, combined with various possible changes in local consumption patterns, might have impact on both “internal” (local) and “external” (non-local) forest footprints.

Balancing landscape-level forest management between recreation and wood production

Although many forested landscapes are used for both wood production and outdoor recreation, intensive forest management can negatively impact the recreational value of forests, including in Sweden, a country with rich forest resources and a strong forest industry. In Sweden, urbanization has increased the importance of, and demand for, urban and peri-urban recreational green areas such as forests. It is the responsibility of the local government – i.e., the municipalities – to provide a good living environment for its inhabitants, including recreational areas. However, most of the forest areas in Sweden are owned by private individuals and companies, which have a large degree of freedom in their forest management decisions. Municipalities can make formal agreements with forest owners to protect forests with high recreational values, but this requires financial resources, which are often scarce. Thus, tools are needed to identify the forest areas that should be prioritized for the use of forest management strategies that maintain or increase the recreational value of forests. In this study, we elaborate an approach that balances economic and recreational forest values within a forest decision support system (DSS) and test the approach for a case study area in southern Sweden. The recreation model included in the forest DSS links locational aspects, such as population density and proximity to water, with forest structure aspects, which are simulated over time under different management strategies. Our results suggest that the model could be useful for more efficient planning of the recreational potential of forests at the landscape level. The results from the case study indicate that substantial increases in the recreational value of a forest landscape can be achieved with relatively small overall economic losses, for example, by extending rotation periods in forests close to densely populated areas.

Incorporating stand level risk management options into forest decision support systems

Aim of study: To examine methods of incorporating risk and uncertainty to stand level forest decisions.Area of study: A case study examines a small forest holding from Jönköping, Sweden.Material and methods: We incorporate empirically estimated uncertainty into the simulation through a Monte Carlo approach when simulating the forest stands for the next 100 years. For the iterations of the Monte Carlo approach, errors were incorporated into the input data which was simulated according to the Heureka decision support system. Both the Value at Risk and the Conditional Value at Risk of the net present value are evaluated for each simulated stand.Main results: Visual representation of the errors can be used to highlight which decision would be most beneficial dependent on the decision maker’s opinion of the forest inventory results. At a stand level, risk preferences can be rather easily incorporated into the current forest decision support software.Research highlights: Forest management operates under uncertainty and risk. Methods are available to describe this risk in an understandable fashion for the decision maker.

Forest owner behavioral models, policy changes, and forest management. An agent-based framework for studying the provision of forest ecosystem goods and services at the landscape level

In this paper, we develop an interdisciplinary agent-based framework for studying the provision of forest ecosystem goods and services (ES) at the landscape level. It combines forest owner types and their behavioral models with forest decision support systems (DSS). For this, we draw on mainstream decision-making theories in social sciences and relevant empirical evidence from Europe and beyond to suggest how economic, sociological and psychological rationalities can be combined to build a typology and behavioral models of forest owner decision-making including responsiveness to policy and socioeconomic changes. We then discuss how the theory-driven and evidence-based behavioral models in combination with forest owner and forest management insights can be used to analyze forest management behavior and responsiveness to policy and socioeconomic developments. By comparing standard forest DSS modeling results to exemplary results using the agent-based framework in terms of long-term simulations of timber production and biodiversity conservation in a case study in Sweden, we illustrate how the framework accounts for more differentiated and more realistic forest management practices at the forest landscape level. We discuss how the agent-based framework also helps explore and explain the impacts of future policy and socioeconomic changes on the provision of forest ES. We then suggest directions for future research that can further develop the framework and its use in forest modeling and DSS to better analyze the impact of future developments on forest management and the provision of forest ES at the landscape level.

Modelli di stima del volume e delle fitomasse del soprassuolo arboreo delle principali formazioni forestali della Calabria mediante dati LiDAR

Abstract: Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy). The AlForLab project is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region. Estimating the main dendrometric variables of Calabrian forests using models based on publicly available remote sensed data is one of the main purposes of the project. This paper describes the procedures used to develop several thematic maps (raster and vector) of timber volume and phytomass to be used in planning and management activities at both regional and forest property scale, as well as for felling plans, logging projects etc. We used public LiDAR data at medium-low resolution (1.6 pts m-2), acquired on about 90% of Calabrian territory in the frame of a national remote sensing programme of the Italian Ministry of the Environment. Field data from the second National Forest Inventory (INFC 2005) on 311 sample points were used for model calibration, as well as new field data acquired specifically for AlForLab project on 143 angle count samples. A series of regression models to predict volume and its corresponding aboveground biomass (dry and fresh weight) were developed and digital maps at different spatial resolutions were produced, as well as their estimate uncertainties. These models and their mapping products are also an important part of the new-establishing forest Decision Support System CFOR. The adopted models, though based on the same mathematical equation, have specific coefficients for different species and groups of species, according to a forest type classification system compatible with the fourth level of Corine Land Cover. In this way it is possible to apply the models without accessing more detailed forest type maps. All estimation methods and procedures are consistent with national forest inventory models, and with the other new tools proposed by AlForLab project to estimate timber volume, such as the regional tariffs and the field sampling inventory procedures. R2 adjusted values (for models at the highest typological detail) are between 60% and 85%, whereas uncertainties of timber volume estimate (ESS%) range from 25% (for main forest species) up to 50% (for less spread forest types). All processing steps to produce digital maps were performed on open-source environment (R and QGIS).