Orals: Thu, 7 May, 08:30–10:15 | Room -2.62
Decision Support Systems (DSS) are increasingly important for modern forest management, offering tools to plan, implement, and evaluate strategies that balance production, conservation, and climate adaptation. Integrative Forest Management (IFM) emphasizes multifunctionality safeguarding biodiversity, mitigating climate risks, and sustaining ecosystem services, yet the extent to which current DSS meet these demands remains unclear.
This study presents findings from Deliverable 5.4 of the TRANSFORMIT Horizon Europe project, which assessed DSS capacity to support IFM principles. We developed a Catalogue of DSS, informed by a survey of 42 DSS managers across Eurasia and North America, to evaluate functionalities against 40 IFM-related variables. These variables span forest production, protection, and conservation, including indicators for ecosystem services, disturbance regimes, and biodiversity.
Results reveal a mixed picture. DSS are robust in traditional forestry domains, like estimating timber yield, stand development metrics, and carbon accounting, yet they exhibit significant gaps in IFM-critical areas. Representation of non-wood forest products, recreational values, hydrological services, and soil carbon remains limited, constraining multifunctional forest planning. Similarly, while some DSS simulate abiotic disturbances (storms, wildfires), few address biotic threats (insects, pathogens), reducing their utility for resilience-based management under climate change. Biodiversity support is weakest: most tools rely on structural proxies (e.g., deadwood) rather than species-level indicators or habitat connectivity, limiting their capacity to inform conservation-oriented decisions. Despite these shortcomings, DSS have advanced considerably, enabling multi-objective analyses and holistic assessments that were unattainable a generation ago. They increasingly integrate ecosystem services and climate-related risks, supporting IFM aspirations at multiple spatial scales. However, usability challenges and a research-practice gap persist, as many tools remain tailored to scientific rather than operational contexts.
To fully realize DSS potential for IFM, enhancements are needed in three areas: (i) ecological complexity, i.e., better modeling of biodiversity and habitat dynamics; (ii) disturbance representation, i.e., improved simulation of climate-driven risks; and (iii) user experience, i.e., intuitive visualization and stakeholder-oriented design. Aligning DSS functionality with policy objectives and practitioner needs will be critical for fostering adaptive, multifunctional forestry.
European initiatives like the TRANSFORMIT Horizon project facilitate progress toward this goal, bridging science and practice to develop DSS that enable balanced, evidence-based decisions. By addressing current limitations, DSS can become key enablers of climate-smart, biodiversity-friendly forest management, supporting resilience and sustainability in an era of rapid environmental change.
How to cite: Mazziotta, A., Kurttila, M., and Vacik, H.: Development of Decision Support Systems for Integrative Forest Management: Insights from a Eurasian and North American survey, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1346, https://doi.org/10.5194/egusphere-egu26-1346, 2026.
Societal demands for forest biodiversity and ecosystem services (BES) are growing and diversifying, which necessitates careful decision-making in forest management. Decision support systems (DSS) are a valuable tool to compare different management strategies and model the trade-offs between BES objectives, and they are successfully applied for forest management at the resolution of forest stands and landscapes. However, there is a growing interest in developing DSS at an even finer resolution: the individual-tree level.
We present a systematic review of tree-level decision support systems in forest management, which take individual-tree data as input, apply an optimisation algorithm, and prescribe a management decision for every tree as the output. Tree-level DSS directly include relevant tree attributes in the planning process rather than relying on aggregated proxies at the stand level. This enables a greater flexibility and precision in forest management, which complements the developments in close-to-nature forestry, remote sensing and autonomous forest machines. Our review identified 47 studies that describe a tree-level DSS. These studies use diverse optimisation techniques such as heuristic algorithms, mathematical programming and machine learning to generate the decisions. Several management targets have been addressed in the studies, such as economic value, biodiversity, forest fire risk mitigation and the amenity of the landscape. Thanks to advances in remote sensing, rich information about individual trees can be derived, although the attributes typically gathered during field inventory, like species, tree height and diameter at breast height, are still the most commonly used in decision-making.
Important challenges for the further development of tree-level DSS are to include natural disturbance risk predisposition in the management decisions; to design generalisable approaches that accommodate diverse forest BES, rather than focusing only on specific case studies; to connect tree-level decisions with management plans at larger spatial scales; and to enable the real-world implementation of the optimised decisions. Informed by the findings of our review, we will present our ongoing work on a new tree-level DSS designed to address these challenges.
How to cite: Perry, N., Schweier, J., Bont, L. G., Kushwaha, S. K. P., Peltola, H., Eyvindson, K., Astrup, R., Chapman, M., and Blattert, C.: Tree-Level Decision Support Systems for Forest Management: a Systematic Review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7922, https://doi.org/10.5194/egusphere-egu26-7922, 2026.
Forest decision-making is becoming increasingly complex due to shifting environmental conditions, rising uncertainty, and evolving societal demands linked to climate change, stakeholder preferences, and forest multifunctionality. To support sustainable forest management effectively, Decision Support Systems (DSS) must integrate diverse information and knowledge sources, objectives, and decision contexts, which poses a number of challenges in their conceptual design.
We developed a shared knowledge base for integrated forest DSS by formalizing a domain ontology, building on a decade-long knowledge repository developed in the context of European network activities (e.g., Community of Practice ForestDSS, COST FORSYS, DSS4ES). Through an expert-driven revision and validation process, we refined concepts and definitions, improved structural coherence, and identified missing elements relevant to both current and future decision contexts.
The resulting ForestDSS ontology highlights central components and design elements of forest DSS, with particular focus on climate sensitivity, natural disturbances, ecosystem services, and landscape-scale interactions. By explicitly representing these components (e.g. data, models, methods, user interface) and their relationships, the ontology provides a structured framework to design, document, compare, and evaluate DSS for multifunctional forest management.
This ontology-based knowledge structuring supports improved characterization of existing DSS and accelerates the development of next-generation tools. It enables transparent concept reuse, more consistent integration of models, data, and stakeholder inputs, and clearer communication across disciplines. The ForestDSS ontology thus serves as a collaborative knowledge resource for research, education, and practice, supporting sustainable forest management at the landscape scale.
How to cite: Reuter, S., Griess, V. C., Mazziotta, A., Rosset, C., Vacik, H., Vinogradovs, I., and Díaz-Yáñez, O.: Proposing an Ontology for the Innovative Design of Future-Ready Forest Decision Support Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12626, https://doi.org/10.5194/egusphere-egu26-12626, 2026.
Integrated decision support systems are fundamental for addressing complex issues related to forest ecosystems and the land use sector, such as climate, biodiversity or disturbances, and their impact on industry and society. Therefore, it is important to develop and use tools that can better incorporate potential challenges to forest ecosystems, socio-economic trends and political choices, and show their consequences for multiple natural resources. SiTree is a flexible, cross-platform and open-source individual-tree simulator framework written in R. Simulations produced using SiTree are currently and actively being used to inform policy decisions and in research, from carbon uptake under different management options to the provision of different forest ecosystem services, such as timber production and biodiversity. An overview of the current state with practical examples where SiTree simulation tool is being used will be presented. Future possibilities and capabilities for the development of SiTree will be discussed, with a focus, among others, on better linking land use to social trends and policy-making, predicting large-scale disturbances in forests and estimating the provision of forest ecosystem services.
How to cite: Sevillano, I. and Antón-Fernández, C.: SiTree - A framework to implement single-tree simulators and its potential as a decision support system for ecosystem services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21362, https://doi.org/10.5194/egusphere-egu26-21362, 2026.
How to cite: Skudra, A., Vinogradovs, I., and Sisenis, L.: Cross-scale integration of ecosystem services into forest planning: structural tensions in developing an integrated DSS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7233, https://doi.org/10.5194/egusphere-egu26-7233, 2026.
Forest management across Europe is confronted with a broad range of uncertainties, including the ecological and economic implications of silvicultural adaptation strategies. Especially in regions with limited forest accessibility, silvicultural constraints, and challenging topographic conditions, the economically viable potential for multifunctional management of forest ecosystem services is determined by its costs. In Swiss mountain forests, for example, costs for timber harvesting and extraction regularly exceed timber revenues; nevertheless, forest management is considered essential to sustain the forests’ protective function against gravitational hazards. Especially under future forest dynamics, it remains unexplored to what extent timber production alone is sufficient to cover forest management costs. Thus, for long-term assessments of management costs across various biogeographic conditions, structured frameworks that integrate both dynamic forest modelling and operational considerations are required to assess potential economic barriers for future forest management.
To close this gap, we present a comprehensive framework to assess socio-economically best suitable timber harvesting methods (BEST) and corresponding harvesting costs in response to long-term forest dynamics. Across the Swiss National Forest Inventory (NFI), we integrated (i) dynamic simulations of alternative management strategies under climate change using the forest model MASSIMO, (ii) technical assessments of state-of-the-art harvesting methods, and (iii) timber harvesting productivity models allowing the estimation of associated harvesting costs.
Our results revealed considerable temporal shifts in BEST portfolios that were mediated by an interplay of varying topographic conditions, differences in forest accessibility, as well as current forest composition and corresponding forest trajectories – for example, with higher shares of air- and cable-based harvesting methods being assigned within the Alpine regions. Further, considerable shifts in harvesting costs in response to long-term forest dynamics were observed. For example, in the Jura, the proportion of managed NFI plots with harvesting costs of < 50 CHF m-3 decreased from approx. 80 % (year 2023) to 50 % (year 2113) under a management strategy aiming for constant growing stocks. Over the simulation period, mean timber harvesting costs remained comparatively stable in the Swiss Prealps and Alps, whereas long-term increases were modelled for both the Jura and Plateau. Notably, harvesting costs under BEST were consistently lower than those estimated under the continuation of currently applied methods (i.e. as documented within the NFI), highlighting the potential for increased cost efficiency through shifts in harvesting methods.
We conclude that climate- and management-induced shifts in forest dynamics may affect the economically viable potential for forest ecosystem service provision. Especially in regions where management costs outweigh timber revenues, economic assessments and decision-support tools need to adopt a supply-cost perspective by accounting for shifts in harvesting methods and associated costs. Further, the development of strategies aiming for forests’ adaptation to climate change needs to consider their long-term economic and technical implications to proactively identify real-world barriers to successful implementation.
How to cite: Mutterer, S., Schweier, J., Stadelmann, G., Fuchs, J. M., Flury, R., Griess, V. C., Thürig, E., and Bont, L. G.: The costs of providing tomorrow’s forest ecosystem services: A framework for assessing harvesting methods and management costs under future forest dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13424, https://doi.org/10.5194/egusphere-egu26-13424, 2026.
Forested landscape is a highly complex socio-ecological system, wherein timber production and trade is a very important factor in local economies as well as in the well-being of the region. The management of forested landscapes requires multi-stakeholder interventions and decision support tools that can address ecosystem services, risks, and uncertainties at the landscape scale. This study requires the use of a National level database to support the development of a Decision Support System (DSS) linking forest growth, timber supply, and wood quality with marketing mechanisms across nine Italian regions. Data collected were to evaluate first timber market methods across four dimensions, which comprise the following metrics for economic efficiency: prices, net revenue to forest owners, transaction costs, price variability and payment timing. Market access and demand efficiency are assessed through bidder participation, geographical distribution of buyers, timber volumes, administrative constraints, extent of market access, and the allocation of timber for energy use, industrial, functional use, and premium use according to the cascade use principle. The indicators of operational efficiency include sale duration, harvesting start and duration, logistics of sale responsibility, quality of information, and sustainability certification. Governance and transparency are assessed through regulatory clarity, e-platforms of sales, traceability and EUTR traceability and compliance, conflict incidence, systems of control and enforcement of timber trades, and limitations of protected zones and land use. These findings are used for developing an integrated DSS that is capable of performing multi-criteria analysis, assessment of disturbance scenarios, and visualizing trade-offs among timber production, timber market and biodiversity. This study emphasizes that institutional support and market development will contribute to increasing the value and sustainability of timber and its carbon sequestration capacity, whereas organizational constraints continue to limit market development for central, southern, and island regions. In conclusion, these observations provide support for further development of DSS on Italian forest landscapes, which focuses on dealing with issues of sustainable timber production efficiency, sales, and market efficiency, as well as ecosystem services provision.
How to cite: Jabre, J. and Carbone, F.: Timber Production and Landscape-Scale Decision Support: Evidence from a Nationwide Assessment of Italian Wood Markets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6691, https://doi.org/10.5194/egusphere-egu26-6691, 2026.
We present NEXSOL, the TRANS-SAHARA WEFE Nexus Agroforestry Intervention Design Tool for Climate Resilience, developed within TRANS-SAHARA, an EU-funded Horizon project. NEXSOL is a decision support system (DSS) that translates model outputs into actionable guidance for agroforestry planning in Living Labs in Tunisia, Ghana, and Ethiopia. Positioned within the WEFE (Water–Energy–Food–Ecosystems) Nexus, it aims to support researchers, policymakers, and local authorities in exploring intervention options and assessing trade-offs and synergies that affect ecosystem services, water security, and community resilience across the Greater Northern African Region.
This contribution reports work in progress. NEXSOL is being developed to integrate three complementary modelling strands produced in TRANS-SAHARA: (i) a WEFE Nexus framework that computes cross-sectoral KPIs and applies multi-objective optimization to identify “best” solutions across water, energy, food supply, emissions, and economic dimensions; (ii) an optimization-based agroforestry/land-use allocation model that evaluates socio-economic and environmental costs and benefits under plausible future market, productivity, and policy scenarios; and (iii) climate and species-distribution projections that indicate current and future land-use suitability and related ecosystem-service implications.
Specifically, the methodology comprises: (1) the classification of model families, associated data requirements, and remaining gaps; (2) the compilation and harmonization of multi-source datasets and key performance indicators (KPIs); (3) the implementation of a WEFE modelling environment tailored to Living Lab contexts; (4) the development of optimization-based land-use allocation methods for baseline assessment and scenario exploration; (5) the integration of climate projections and species distribution modelling outputs; (6) the establishment of a reproducible data pipeline encompassing ingestion, quality assurance/quality control (QA/QC), metadata management, and version control; (7) the design of a decision-support system (DSS) user interface featuring dashboards, maps, time series, heatmaps, and structured scenario workflows; and (8) calibration and validation using spatially explicit observations. By coupling WEFE modelling with data-driven prediction and visual analytics, the tool may provide climate-robust, actionable guidance on where and how agroforestry interventions are most effective, thereby advancing multi-objective and multi-risk optimization (e.g., profitability, biodiversity, equity) and incorporating carbon-market and payments-for-ecosystem-services mechanisms. The resulting modular system is co-designed with stakeholders and validated against real-world datasets and decision processes.
This research is funded by the framework of the TRANS-SAHARA project, funded by European Union under the Horizon Europe Framework Program Grant Agreement Nº: 101182176.
How to cite: Chuprikova, E., Berlanda, M., Fröhlich, N., Gardner, E., Yeboah Asamoah, K., Monsorno, R., Bouguerra, S., and Keilmann-Gondhalekar, D.: NEXSOL: A WEFE Nexus Decision Support System for Climate-Resilient Agroforestry Planning in TRANS-SAHARA Living Labs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20611, https://doi.org/10.5194/egusphere-egu26-20611, 2026.
Natural Resource Managers (NRMs) rely on sectoral modelling approaches that are system specific such as agriculture, forests or rivers. Though the tools provide insights for individual natural systems, they are limited by lack of a holistic evaluation approach, that understands the nexus (trade-offs) between natural ecosystems. As a result, NRMs are limited by lack of integrated evidence on how and why adaptation strategies fail to deliver the intended outcomes.
This study developed an integrated decision support framework, that explicitly links agricultural, forests and rivers systems, to support regional NRMs in Southwest Victoria. This multi-framework foundation ensures that outputs align with real planning processes used by NRMs. The framework evaluates agricultural productivity, habitat distribution and water availability under changing climatic conditions. Using geospatial tools, AI-augmented climate modelling and integrating a multi-framework approach – the tool provides a robust streamlined analysis. The pilot workflow integrates an ensemble of machine learning models to map the impacts. Downscaled climate projections (ACCESS-CM2 SSP585, 2020–2100) were combined with biophysical and land-use data to model land suitability for canola, habitat probability for Kangaroo Grass, and stream yield for the Moorabool River. A rigorous preprocessing pipeline of normalisation, correlation check (IrI>0.7), Variance Inflation Factor (VIF<10), and ML ensemble-based feature selection, improved predictive accuracy. System-specific outputs were combined using an index-based overlay approach using Shapely packages. The analytical workflow progresses from Vulnerability zones for each ecosystem > Trade-offs/Synergies > and arriving at Adaptation Tipping Points. The decision support system (DSS) translates fragmented systems into a comprehensive model to support evidence-based decision making.
The DSS is conceptually anchored in an integrated decision-making framework, that adopts key aspects of decision-making frameworks from Integrated Catchment Management, scenario testing from Resilience Thinking framework, and Adaptation Tipping points from Dynamic Adaptive Policy Pathways, enabling outputs to align with decision processes used by regional authorities. It identifies vulnerable zones, trade-off zones, and possible adaptation tipping points under changing climatic and development pressures.
By translating complex model outputs into accessible spatial layers and scenario-based decision products, the DSS lowers technical barriers for NRMs and strengthens evidence-based planning. The framework is scalable and transferable, providing a replicable pathway for integrating ecosystem service assessments into climate adaptation policy and land-use planning across diverse regions.
How to cite: Gampa, R.: AI-Augmented Decision Support System for Evidence-Based Climate Adaptation in Regional Victoria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4144, https://doi.org/10.5194/egusphere-egu26-4144, 2026.
The expansion of global forest cover is a critical component of preserving biodiversity, promoting climate resilience, and improving community well-being worldwide. Yet, the benefits that forests provide vary significantly depending on factors such as structural complexity, species composition, and geographic context. Forest restoration and tree-planting programs therefore present a unique opportunity to intentionally shape the forest's future conditions to achieve target management objectives and deliver specific ecosystem services. However, implementing such programs at the landscape-scale becomes more complicated, and requires balancing the needs of diverse stakeholders and competing management goals. Urbanizing landscapes introduce additional layers of complexity in the form of high population densities and heterogenous land use mosaics, which intensify trade-offs between ecological and socioeconomic priorities. Furthermore, the inherent variability in each landscape's spatial configuration may present unique challenges or opportunities to balance these trade-offs, which may affect the benefits generated by planted forests. The difficulty in balancing this multitude of context-specific factors emphasizes the need for a systematic, data-driven approach which identifies strategic locations for increasing forest cover.
In this study, we present a spatial decision support system (SDSS) designed to locate optimal planting sites within urbanizing landscapes for strategically increasing tree cover and ecosystem service provisioning. The SDSS analyzes high-resolution geospatial data to identify and rank available planting locations, simulate potential implementation strategies, and integrate external models to quantify potential outcomes. Upon completion, a detailed inventory of identified sites is generated, which provides actionable information including the size and geographic coordinates of each site. The inventory also provides a concrete foundation for quantifying specific ecosystem services, such as carbon sequestration and storage potential or pollution removal. These estimates can then be evaluated alongside stakeholder priorities and management goals to identify areas where forest expansion will yield the greatest benefits. Overall, the SDSS's scalable nature aids decision-making by considering services generated locally by individual trees as well as collectively by entire forests—thus offering comprehensive, actionable insights for sustainable and effective landscape management.
This presentation will highlight a case study from the United States that explores the impacts of different forest expansion scenarios, and the SDSS's capacity to strategically inform forest restoration and expansion efforts and enhance ecosystem service provisioning worldwide.
How to cite: Smolensky, A. and Halsey, S.: Context-Driven Optimization of Ecological and Socioeconomic Benefits through Urban Forest Expansion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14268, https://doi.org/10.5194/egusphere-egu26-14268, 2026.
Carbon farming is increasingly promoted as a climate mitigation instrument in agriculture and land use, requiring additional, measurable, and sustainable increases in carbon sequestration. In forestry, meeting these requirements is more challenging due to strict conditions related to additionality, permanence, sustainability, and regulatory compliance. In Lithuania, forest management is strongly governed by legislation, which limits the potential for generating additional carbon benefits without deviating from established management rules. Consequently, additional carbon sequestration in forestry is most commonly associated with afforestation and postponement of final fellings – measure that requires robust justification of additionality.
Demonstrating such additionality requires decision support systems (DSS) capable of modelling carbon stock changes under alternative forest management scenarios and comparing them with baseline management. This study aims to demonstrate the role of DSS in supporting carbon farming at the level of private forest estate by integrating forest inventory data with carbon accounting tools. It shows how enhanced forest inventory data, combined with carbon accounting, can support scenario modelling, improve the transparency of additionality claims, and inform both management decisions and policy design for carbon farming schemes in forestry.
More specifically, the study introduces newly developed DSS tools for predicting future carbon stock changes under conventional and alternative forest management models designed to increase carbon sequestration at the scale of relatively large private forest estates. In addition, it presents new inventory approaches based on the integration of optical remote sensing data with airborne and drone-based laser scanning, aimed at supporting carbon farming–oriented forest management planning.
How to cite: Bikuvienė, I. and Narmontiene, V.: Role of DSS to support carbon farming at the level of private forest estate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13666, https://doi.org/10.5194/egusphere-egu26-13666, 2026.
Decision Support Systems are seen as particularly useful for unstructured, ill-structured and semi-structured problems where human judgement is relevant for problem solving and limitations in human information processing may impede the decision making process. Decision making situations that involve many stakeholders and different natural resources require therefore tools that facilitate the inclusion of stakeholder preferences on different management objectives in the decision making process. On a European scale the reduction in net emissions of greenhouse gases, the sustainable use of forest resources and provision of forest ecosystem services as well as the integration of different economies and societal values are demanded from different stakeholders and policy. The OptFor-EU project “OPTimising FORest management decisions for a low-carbon, climate resilient future in Europe“ designs a Decision Support System that provides forest managers with options for climate resilent forests, decarbonisation and many other forest ecosystem services. The DSS will help stakeholders to select, understand, and undertake appropriate actions to increase forest carbon sinks under changing climate conditions, whilst ensuring other important ecosystem services are maintained or enhanced. The process is decomposed in four basic steps: (1) problem identification and diagnosis, (2) searching and designing for options to overcome the problem, (3) screening and estimation of the effects of different treatment options, (4) evaluation and analysis of various alternative courses of actions. Based on this process, the decision maker can choose an alternative forest management option for a low-carbon, climate resilient future, and analyse the effect of different preferences for the mangagement objectives or climate change projections. The DSS is designed as a “toolbox” by integrating database management systems with analytical and operational research models, graphic display, tabular reporting capabilities to support decision making. A set of climate sensitive forest models were used to predict the effects of different forest management practices (FMP) under various climate change scenarios. The forest stands are characterized based on a European wide classification of European forest types (e.g. beech forest, alpine forest). Users can select from a novel set of Essential Forest Mitigation Indicators (EFMI) and explore their performance for a particular temporal (e.g. 10, 20 years) and spatial (national, regional) scale. For the evaluation of FMPs the preferences for selected EFMIs can be defined and the synergies or trade-offs among the alternatives evaluated. In this contribution the basic components of the DSS (Explorer, Evaluator, Data Client and Database) and their functionality are demonstrated for one of the eight case studies in Europe.
How to cite: Vacik, H., Purcarea, R., Crihan, F., Lindau, A., Linser, S., Neumann, M., Constantin, N., and Cheval, S.: Decision Support for a low-carbon climate resilient future in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19890, https://doi.org/10.5194/egusphere-egu26-19890, 2026.
The emergence of carbon trading mechanisms is increasing the need for transparent, reproducible, and policy-relevant tools to quantify carbon stock changes in the forest sector. In this context, this study presents an Excel-based carbon calculation tool developed in accordance with IPCC principles for estimating carbon stock changes within the LULUCF sector. The tool supports scenario-based assessments of land-use change and afforestation planning using readily available spatial and statistical inputs and enables evaluation of the potential impacts of legislative initiatives on carbon sequestration from afforestation of non-forest land, serving as an analytical instrument for policy formulation, legislative decision-making, and scientific analysis of forest cover expansion.
To test the applicability of the tool, afforestation scenarios were developed for Jonava municipality (944 km²), Lithuania, using GIS-based identification of suitable areas. Two
contrasting cases were applied: afforestation limited by current land-use regulations and an extended scenario including drainage bund areas. The regulation-aligned case identified 2,862 ha suitable for afforestation, while the extended case increased the afforestable area to 20,189 ha, raising potential forest cover from 41.1% to 62.6%.
When processed with the Excel-based IPCC-consistent tool, the extended scenario demonstrated a substantially higher carbon sequestration potential, with up to 14.1 million tons of additional CO₂ equivalent over a 50-year period – approximately six times the annual sequestration estimated under the regulation-aligned scenario. These results demonstrate the tool’s capacity to quantify long-term carbon stock changes under contrasting land-use assumptions, supporting its use for scenario testing, land-use planning, and carbon accounting.
How to cite: Narmontienė, V.: A Tool to Assess the Impact of Forest Land Expansion on Greenhouse Gas Sequestration and Emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8090, https://doi.org/10.5194/egusphere-egu26-8090, 2026.
Onshore wind deployment is expanding rapidly, yet project timelines are frequently delayed by environmental conflicts and repeated information requests during environmental impact assessment (EIA). To support early-stage planning and reduce downstream uncertainty, we present a GIS-based Environmental Siting Consulting Decision Support System (DSS) for onshore wind development. The DSS operationalizes the national environmental assessment guidance for onshore wind and is designed to identify key EIA issues in advance while maintaining procedural continuity from pre-screening to formal assessment.
Unlike conventional pre-screening that focuses on simple overlaps with protected areas, our approach implements a stepwise logic that evaluates avoidance, adjustment, and mitigation feasibility. It integrates an expanded pre-siting geodatabase covering ecological value and protected-species indicators (e.g., ecological zoning, vegetation conservation grades), terrain and geohazards (e.g., ridge-core zones, slope, landslide risk, faults), landscape and cultural receptors, noise-sensitive facilities, and water-environment constraints.
Users delineate candidate sites as polygons and linear features, including multi-line layouts that better represent access roads and infrastructure corridors. The system performs dual-scale analysis: a 10 m “core” zone for quantifying land-use composition within the project boundary and a 500 m buffer for screening surrounding sensitive layers and potential indirect impacts. Results are delivered as a standardized, map-rich report that mirrors the structure of the official review/notification document, enabling transparent “why/where” explanations of constraints and priority review items.
This DSS improves predictability and transparency for developers and reviewers, supports iterative design adjustments before formal EIA, and provides a scalable pathway for evidence-based, conflict-aware renewable energy siting.
How to cite: Yi, Y. J., Kim, T., and Kim, D.: A GIS-Based Decision Support System for Environmental Siting Consulting of Onshore Wind Projects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12742, https://doi.org/10.5194/egusphere-egu26-12742, 2026.
Geosystem services (GS) play an important role in the energy and climate transition. Aquifer thermal energy storage, geothermal energy, (seasonal) gas storage and even storage of nuclear waste are all activities derived from GS that can help humanity move towards climate neutrality. As with all ecosystem services, GS need to be used sustainably and fairly. Overuse of GS to speed towards climate neutrality could exhaust these essential services and place a debt on the future.
The interdisciplinary DIAMONDS project: Dynamic Integrated Assessment Methods fOr the sustainable Development of the Subsurface [Compernolle et al., 2023] aims for holistic planning of the subsurface with researchers looking at sustainability from (hydro)geological, engineering, economics, sociological and environmental perspectives. A Principles, Criteria & Indicators (PC&I) framework is developed as a decision support system to incorporate these different views, different tools to deal with uncertainty and the different values at play regarding the sustainable use of GS.
A PC&I is a hierarchical framework consisting of three levels. The first level, the principles, encompasses the universal values that determine sustainability. These are established via a two-round Delphi survey, consulting experts until consensus is reached. The second level consists of criteria which are measurable conditions for the level of applicability of the principle. In this project, the criteria are derived through expert interviews and a focused literature study. Afterwards they are validated and given weight to with an expert survey. To describe the characteristics of the real situation and benchmark against the criteria, indicators are defined at the third level. The information from the involved disciplines is used to create the integrated framework and the framework feeds back into the research by setting some boundaries and specific subjects to measure, model and analyse. The information flows back and forth between the disciplines in the shape of stakeholder workshops, (hydro)geological models, techno-economic assessments, life cycle assessments, real options games and causal loop diagrams.
With this comprehensive decision support system, we hope to guide decision makers towards a sustainable development of the subsurface, helping the energy and climate transition without mortgaging the possibilities for future generations to make use of GS.
Compernolle, T.; Eswaran, A.; Welkenhuysen, K.; Hermans, T,; Walraevens, K.; Camp, M.; Buyle, M.; Audenaert, A.; Bleys, B.; Schoubroeck, S.; Bergmans, A.; Goderniaux, P.; Baele, J.; Kaufmann, O.; Vardon, P.; Daniilidis, A.; Orban, P.; Dassargues, A.; Serge, B.; Piessens, K. Geological Society Special Publication (2023) 528, 101-121, DOI: 10.1144/SP528-2022-75
How to cite: Peeters, H., Bleys, B., and Compernolle, T.: A decision support system for geosystem services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2601, https://doi.org/10.5194/egusphere-egu26-2601, 2026.
Forest management is confronted with deep uncertainties related to trajectories of future forest development, as climate change induces critical transitions in forest ecosystems. Decision support systems (DSSs) that combine climate-sensitive forest modeling with assessments of biodiversity and forest ecosystem services (BES) have the potential to systematically reduce uncertainties regarding the consequences of various climate and management pathways. However, in order to assess the reliability of DSS outputs, systematic analyses of sources of uncertainty across individual DSS components are crucial. This applies in particular to the initialization of DSSs, which remains a key challenge due to constrained data availability from traditional sources such as forest management plans and forest inventories, and thus may constitute a key source of uncertainty within DSSs. In particular, advances in close-range remote sensing, such as high-resolution LiDAR, provide detailed information on the current state and condition of forests and offer new opportunities for DSS initialization. However, the extent to which initialization with high-resolution LiDAR inventory affects DSS outputs and contributes to uncertainty remains unexplored. Therefore, this study aims to quantify the sensitivity of a DSS framework to initialization with LiDAR-based forest inventory data.
Our approach involved (1) terrestrial and airborne laser scanning (TLS/ULS) sampling, (2) initialization of the forest gap model ForClim, (3) simulation under alternative management and climate change trajectories, and (4) evaluation regarding BES. The combined ULS and TLS inventory served as reference data, from which sampling variants with different sample sizes were generated to represent varying levels of forest inventory detail. The DSS sensitivity to initial stand resolution was assessed over a 70-year simulation period under three management intensities, three climate change scenarios, and 15 stand-specific indicators, which were further aggregated into partial utilities for biodiversity and ecosystem services.
Our results revealed that low sample sizes of inventory data resulted in higher deviations from the reference simulation. This effect decreased with progressing simulation time and higher management intensity for most BES indicators. While sample size was the primary source of uncertainty in the early stages of the simulation, climate-related uncertainty increased over time. Our findings establish a 20-40 year tactical window where high-resolution initialization is the primary determinant of DSS reliability, after which climate uncertainty becomes the dominant constraint for strategic planning. Further research should aim to leverage the full potential of high-resolution LiDAR data for DSSs by extracting additional information on forest composition and state. This would enable more informed decision support for long-term forest planning under deep uncertainty and the demand for BES provision.
How to cite: Nögel, J., Blattert, C., Mutterer, S., Karppinen, M., Hiltner, U., Frey, J., Kushwaha, S. K. P., de Crousaz, C., Zürcher, R., Pepek, I., Seifert, T., and Schweier, J.: From point clouds to forest management: Quantifying the sensitivity of a decision support framework to initialization data using close-range remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18387, https://doi.org/10.5194/egusphere-egu26-18387, 2026.
Forest decision support systems (DSS) increasingly require growth-modeling solutions that remain robust when forest stands are structurally complex. This abstract describes a machine-learning workflow that models annual increments in height and diameter at breast height (DBH) for stand-forming elements, using dendrometric data and forest site type as predictors.
The main focus is multi-structural representation and ease of deployment inside the DSS. The workflow supports use of separate models for stand elements and combining their predictions into stand-level outputs, covering stands with few elements as well as stands with many elements.
The workflow is suitable for both operational use and research. In a DSS, the prepared model system can be loaded, inputs can be read from a database, and stand-level outputs can be produced for decision support. The component can also be linked to a database and combined with other analytical models. Outputs can then be presented as decision-relevant tables and visualizations.
A Lithuanian forest inventory dataset was used for model development and validation, and an initial performance summary and a brief workflow check are reported. The framework allows accuracy improvements through model updates and provides a simple path for reusing updated models in a DSS.
How to cite: Narmontas, M.: A Stand-Element Increment Modelling Framework for Forest Decision Support Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8045, https://doi.org/10.5194/egusphere-egu26-8045, 2026.
The quantification of ecosystem service demand value serves as a critical bridge connecting human well-being with ecological management. Addressing the current academic gap in valuation frameworks that precisely couple with supply classification systems and are difficult to integrate into Decision Support Systems (DSS), this study develops an ecosystem service demand analytical model. Based on ecological characteristics and administrative divisions, mainland China was divided into six management zones. Guided by Human Need Theory, a demand classification system was constructed. By integrating socio-economic big data with symbolic regression algorithms, we decoded the quantitative relationships between population scale and various demand values across regions, satisfying the requirements of DSS for rapid computation and real-time simulation. Results show that: (1) Spatial Distribution Characteristics: Within the population interval below 5 million, the demand values for various services in the Yellow River Basin Ecological Restoration Coordination Zone are higher than those in other regions under the same population base. (2) Evolutionary Patterns of Demand: The simulation curves reveal distinct environmental carrying capacity thresholds across all regions. Beyond these critical points, the marginal fulfillment costs of ecosystem services surge, driving a rapid upward trend in demand value. (3) Model Accuracy and Application: With the introduction of a time-factor correction, the average model error is controlled within 10%, and the accuracy is improved by 20%. This study establishes a classification and accounting framework that balances computational simplicity with realistic alignment, achieving multi-scale quantitative assessment of demand value and providing core algorithmic support for ecosystem service decision support systems.
How to cite: Wang, J. and Fu, M.: Research on a Symbolic Regression-Based Model for Valuing China's Population-Ecosystem Service Demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2420, https://doi.org/10.5194/egusphere-egu26-2420, 2026.
Climate change poses one of the most significant threats to forest ecosystems in the twenty-first century, intensifying the natural processes that drive forest degradation. The Food and Agriculture Organization (FAO) reports that forest degradation is increasing globally and is now outpacing deforestation, underscoring the need for robust methods to quantify degradation and support effective management strategies. The United Nations Strategic Plan for Forests (UNSPF) 2030, urges the need to increase efforts to prevent forest degradation and contribute to the global effort of addressing climate change. Recent advancements in forest degradation research highlight the potential of ecosystem integrity as a more comprehensive framework for assessing degradation. However, current applications of this framework still fall short, as they do not adequately evaluate the resilience of the forested ecosystem in the Anthropocene. Researchers also highlight the importance to go beyond the naturalness in ecosystem integrity concept and adapt a usable concept of level-2 ecological integrity based on the ‘new normals’ or ‘shifting baselines’. The need to address forest degradation both as a ‘process’ and a ‘state’ is also a key requirement to understand the current and critical stages of forest degradation as well as its variation in time. Since the ‘Water Budget’ controls the resilience of any ecosystem restoration, it is also important to analyze the changes in forest hydrological components while assessing its degradation. This study proposes a globally applicable, level-2 ecosystem integrity based framework for forest degradation assessment, incorporating the responses and resilience of forest systems for estimating the ‘process’ and ‘state’ of forest degradation. This will help to identify the pre-degrading, degrading and degraded stages of forests and will help to track the changes at a convenient time step. The framework integrates six forest integrity criteria and multiple associated indicators and evaluators, each representing critical forest characteristics. It also supports the identification of essential forest functions that are undergoing degradation, as well as those that remain intact—information vital for effective forest management. An Analytic Hierarchy Process (AHP) is employed to develop an integrated forest degradation index, which is then validated in a tropical forested river basin of the Western Ghats, India. The study area comprises 152 landscape units within the basin, maintaining approximately 80% forest cover. The assessment results indicate that in 2005, 47% of landscape units were classified as healthy–resilient, 42% as slightly stressed, and 11% as early-degrading. By 2020, these proportions shifted to 18%, 65%, and 17%, respectively. The trend indicates a steady rise in forest degradation, primarily due to the deterioration of ecosystem processes. This emphasizes the need to implement timely monitoring and climate adaptation measures in forest management, and this framework can form a vital part of such decision support systems (DSS).
How to cite: Pallipadan Johny, J., Pavizham, A., and Kulamulla Parambath, S.: Forest Degradation Assessment Framework based on Level-2 Ecosystem Integrity Concept, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1009, https://doi.org/10.5194/egusphere-egu26-1009, 2026.
