Orals: Wed, 30 Apr | Room -2.33
Forest structure is shaped by forest management practices, land-use changes and forest disturbances including droughts, fires, storms and insect outbreak. It plays an important role in climate by modifying the carbon-water-energy exchanges with the atmosphere, and affects the capability of forests to undergo future disturbances in a changing climate. Given the importance of forest structure for the climate, land surface models are moving towards explicit representations of forest structure and management strategies. We present a new procedure to initialize forest diameters over Europe and document its implications for simulations of future forest carbon sinks. The simulated diameters for each grid cell covered by forests are initialized toward the diameter from a forest inventory. To this end, a 300-years semi-analytical spinup was carried out to bring the soil carbon and nitrogen pools into equilibrium until the European forests were clearcut. Then, a 150-years biosphere simulation over Europe was performed to build a look-up-table of simulated diameters. For each grid point, the year associated with the simulated diameter that is the closest to the observation is selected, enabling the production of new initial state files over Europe. The new initialization procedure makes the initial state of forest more realistic and therefore is expected to have significant influence on the evolution of the forest carbon sink. In this work, we will assess the effect of the initialization procedure on the simulated land carbon sink and we will evaluate the representation of the diameters in the ORCHDEE LSM. The method could be further extended to initialize other forest state variables such as height or aboveground biomass.
How to cite: Remaud, M., Jeong, J., Marie, G., Flores, O., Naudts, K., and Luyssaert, S.: On the assessment and initialization of European forest state variables in Land Surface Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2268, https://doi.org/10.5194/egusphere-egu25-2268, 2025.
Knowledge of forest ecosystem pattern and process responses to climate change and anthropogenic pressure requires innovative tools that combine monitoring and modelling of tree growth dynamics to account for a more sustainable management of forest resources and ecosystem services. In this context, Digital Twins (DTs) emerge as powerful tool to allow a better interpretation of complex models, summarizing a large amount of data and knowledge into a comprehensive 3D visualization. A Digital Twin is an evolving and comprehensive representation of a physical object, in our case trees, which involves three key elements: a digital representation of the object, an evolving set of data and a dynamic adjustment of the object data. However, due to the structural complexity of the forest stand, and the lack of adequate growth historical data series useful to build and validate the simulations, the full potential of Digital Twin frameworks has yet to be realized in forest field. Our work aims to develop a system that simulate forest growth and spatial patterns through a process-based single tree model and represent the outputs into a 3D immersive and interactive environment, able to reproduce the stand structure of Mediterranean forests. An individual based spatially explicit model has been developed to simulate the biomass growth within a time step of one year and while an immersive 3D dynamic environment enables the user to interact with trees (e.g. tree marking, logging). Competition among trees has been modelled computing the tree influence on surroundings space using a distance-biomass dependent approach. We implemented a set of allometric equations to convert tree biomass into size attributes (e.g. stem diameter, total height) to appropriately represent the modelled forest stand in the 3D environment. The use of DTs can assist forest experts and policymakers in managing complex systems like Mediterranean forests, by simulating several management scenarios and analysing their long-term impacts on forest ecosystem dynamics. Furthermore, process-based models coupled with an immersive 3D representation could help to better understand the forest ecosystem functioning.
How to cite: Fornaro, R., Giannino, F., Heatfield, D. N., Minopoli, V., Aquino, A., Rita, A., Saracino, A., and Saulino, L.: Forest digital twin: coupling field data, mathematical modelling and 3D representation of a Mediterranean forests , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6985, https://doi.org/10.5194/egusphere-egu25-6985, 2025.
Insect disturbances significantly impact multiple functions of forest ecosystems, yet their representation in terrestrial biosphere models remains limited. To address this gap, we developed an insect impacts module in the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system model). The new module represents bark-beetle and defoliator impacts by introducing standing dead biomass pools and insect-mediated nutrient cycling pathways, and effectively capturing key processes such as snag decay, larvae pool dynamics, compensatory leaf growth, and carbon starvation due to over-defoliation. Model validation against multiple forest sites registering insect disturbances demonstrated good agreement with observed trends in forest dynamics, carbon fluxes, water and energy exchanges, and nutrient transformations during insect disturbances. Long-term simulations revealed that severe insect outbreaks can reduce ecosystem carbon storage by up to 6% for a horizon of 50 years, primarily due to accelerated nutrient leaching through litter decomposition. These results emphasize the critical role of insect disturbances in shaping vegetation carbon dynamics and highlight the importance of integrating these processes into global vegetation models. Our results further underscore the need for observational datasets, including field and satellite-based measurements, to constrain and improve model representations of insect disturbances. By advancing understanding of insect impacts and their interactions with climate, our study contributes to reducing uncertainties in projections of vegetation dynamics and the terrestrial carbon sink under future climate change.
How to cite: Ma, Y., Zaehle, S., Jornet-Puig, A., and Bastos, A.: Incorporating Insect Disturbances into Terrestrial Biosphere Model: Impacts and Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12297, https://doi.org/10.5194/egusphere-egu25-12297, 2025.
Climate change poses a challenge for European forestry, requiring the selection of tree species adapted to future conditions. Analyzing this for a large country like Germany requires considering diverse regional environmental conditions in climate, soil, and management history. A promising approach is to utilize simulation models to derive potential natural vegetation (PNV) under climate change, which can help to identify robust candidate species for regions.
We employed the process-based forest landscape model iLand to investigate: (i) the impact of climate change on PNV species composition and carbon stocks across regions in Germany, and (ii) regional adaptation deficits by comparing future PNV composition with current forest composition (derived from national inventory data). We defined 12 representative ecoregions via cluster analysis of climate, soil, and vegetation data. For each, we created generic landscapes (20-30k ha) reflecting regional environmental gradients. We used these landscapes to simulate PNV with iLand under historical and nine climate change scenarios. Changes in equilibrium species composition and attainable carbon stocks were calculated relative to historical climate simulations. Finally, we created high-resolution maps of future PNV in Germany by mapping the stands of our simulated landscapes to country scale.
Our landscapes cover 95% of Germany’s forested climate and soil space (defined by the ratio of forest pixels, after removing outliers). Simulations identified regions particularly vulnerable to climate change, as well as those with the greatest mismatch between expected PNV and current forests. To account for regional differences in species suitability is crucial for developing climate change adaptation policies at the national level within Germany.
How to cite: Kerber, J., Seidl, R., and Rammer, W.: Mapping the Future of Germany’s Forests: Modelling Potential Natural Vegetation under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16504, https://doi.org/10.5194/egusphere-egu25-16504, 2025.
Forest ecosystems are vital for a multitude of ecosystem services including timber provision, climate change mitigation, local climate regulation, and provision of habitat for biodiversity. However, previous studies have primarily focused on individual ecosystem service indicators, with limited attention to the underlying biophysical mechanisms. Investigating multiple services under diverse strategies is critical for assessing their impacts on forest ecosystems. Therefore, in this study, we used the global dynamic vegetation model LPJ-GUESS to simulate the temperate forests in China under scenarios of natural succession and forest management strategies. The natural succession refers to forest regeneration without any human intervention. We analyzed multiple ecosystem services, including carbon sequestration, timber provision, water retention, and biodiversity. We found that (1) under management, forests exhibited short-term higher timber yields and economic benefits, but natural succession maintained higher long-term carbon sequestration; (2) density-based management strategies increased timber production and accelerated the forest regeneration in the short term. However, these activities temporarily increased evapotranspiration and reduced biodiversity due to habitat disturbance, which then affected ecosystem services, especially at the initial stages of harvesting; (3) integrated optimization strategies, focusing on tree species, density, and age structure, can optimize forest structure and enhance the multifunctional ecosystem services in the long term. Our study provides valuable insights into the diverse impacts of the management strategies on ecosystem service provision, offering guidance to policymakers and local stakeholders in balancing ecological conservation and economic priorities through sustainable forestry practices.
How to cite: Liu, L., Gregor, K., Gu, Q.-L., Liu, Y., Wang, A., and Rammig, A.: The Impact of Forest Management Strategies on Ecosystem Services in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18432, https://doi.org/10.5194/egusphere-egu25-18432, 2025.
The intensity, frequency, and spatial distribution of forest disturbance regimes across Europe are shifting due to climate change. This raises concerns about the vulnerability of forest ecosystems and the impacts on the goods and services that they provide. Protection against natural hazards is a key service provided by forests in the Alps but current protective effects are threatened by the growing incidence of disturbance events such as windstorms, heavy snowfalls and drought. For planning effective management interventions, detailed information on the patterns of recent forest disturbances and quantifications of their impacts on protection forests are necessary. In our study we focused on a region in the Italian Alps (South Tyrol) with the aims of: i) providing a wall-to-wall disturbance map by agent type (wind, snow, beetles) and an analysis of the spatial patterns of disturbance agents and their interaction, and ii) quantifying the loss of protective effects in protection forests and areas with residual protection given by standing dead trees due to bark beetle.
We analyzed Sentinel-2 timeseries to map disturbances covering the period 2019-2023. We then applied a supervised machine learning classifier leveraging multisource predictors to attribute a disturbance agent to each disturbed patch. Afterwards, we explored the correlation between the areas disturbed by different agents and assessed the areas of protection forest which were affected by disturbances. For these areas, we performed a pixel-based classification to identify areas with residual protective effects given by standing dead trees (i.e., pixels with dead canopy but not downed or salvaged yet) due to recent bark beetle outbreaks.
Our results showed that, over a period of five years, disturbances affected 5.9% of the forests of the study area. Damages due to windthrow (1.6%) and snow (1.3%) had a comparable cumulated impact, while bark beetle caused much larger damages (3%). Snow-damaged areas correlated more strongly with bark beetle damage than wind disturbances. Notably, 5.6% of protection forests in the area were disturbed, with bark beetles causing disproportionately higher impacts compared to the other two agents. Overall, about 1.3% of protection forests still provide some level of protection because they are covered by standing dead trees. These are forests that will soon lose their protective function and should be given high priority in management planning. These findings provide the first detailed mapping of recent disturbances in the region and highlight critical areas where protection forests can no longer offer adequate hazard mitigation. By identifying forests most at risk of losing their protective function, we offer useful information for managers to plan near future interventions in these areas.
How to cite: Dorigatti, E., Mina, M., and Sonnenschein, R.: Mapping forest disturbances and their impacts on protection forests in South Tyrol (Italian Alps), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16695, https://doi.org/10.5194/egusphere-egu25-16695, 2025.
Wildfire risk is an escalating concern across EU territories, amplified by climate change and necessitating proactive management. Addressing this issue requires nature-based solutions, such as fuel management, forest conservation, and restoring fire-adapted ecosystems to their natural fire regimes. This study models forest growth across Europe under climate change and varying management strategies, presenting three scenarios aligned with potential policies. We focus on future wildfire dynamics and their impacts on forests, relying on high-resolution modeling of forest growth and burned areas.
We developed a new model for deadwood and litter dynamics and integrated it with models for forest growth and development and wildfire risks to simulate annual disturbances and post-disturbance management. The Wildfire Climate Impacts and Adaptation model (FLAM) identifies wildfire hotspots under historical, current, and future conditions and projects burned areas under various climate scenarios and management strategies. The Global Forest Model (G4M) simulates large-scale forest changes, accounting for growth, mortality, regeneration, and management activities like thinning, harvesting, and replanting.
Results from integrating and calibrating these models with observed fire events, harvest levels, biomass stocks, and other parameters will be presented. Three management scenarios reflecting key directions in forest management are proposed, linked to climate projections through 2070. This approach provides a robust framework for assessing the impacts of policies and legislation on wildfire dynamics across Europe, enhancing our ability to mitigate risks and adapt to changing conditions.
How to cite: Johnstone, C., Krasovskiy, A., Hyun-Woo, J., Eunbeen, P., Shchepashchenko, D., and Kraxner, F.: Modeling Wildfire Risks and Forest Dynamics in Europe: Strategies for Climate-Resilient Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19761, https://doi.org/10.5194/egusphere-egu25-19761, 2025.
Historic climatic data suggests that severe windstorms have been observed every 10-15 years in Ireland (Gallagher, 1974) and sometimes even more frequently, having a devastating impact on forests in the country. Such severe storms cause a significant number of trees to be uprooted or snapped (McInerney et al. 2016), a phenomenon commonly known as windthrow. Windthrow has extensive consequences for forest management, impacting the operations of forest for timber (wood volume shift to salvage wood, possible quality downgrade due to premature extraction, etc.), the dynamics of forests for nature (light availability, regeneration options, higher deadwood volume, etc.), the safety of forests for public use, the soil dynamics (especially for uprooted trees due to exposed soil), among others. In the context of climate change, natural disturbances such as windthrow might shift forests from carbon sinks to temporary carbon sources (Albrich et al. 2023). In order to understand the impact of windthrow on carbon dynamics in temperate forests, this work uses National Forest Inventory data from county Laois (Ireland) as a study case. Centrally located, county Laois has a forest cover (16.5%) higher than the national average (11.6%), and features diverse conditions, including varied soil types, forest types, as well as management purposes. This study models the impact of windthrow events on carbon pools in county Laois’ temperate forests using the CBM-CFS3 framework (Kurz et al. 2009). Varying disturbance intensities (25%, 50%, 70% and 100% of trees damaged by windthrow) are simulated, and their effects on carbon fluxes across biomass, soil organic carbon, and harvested wood products are analyzed. Management strategies, including salvage logging and natural regeneration, are evaluated to assess both immediate impacts and recovery potential, as well as their role in enhancing carbon resilience.
References
Albrich, K., Seidl, R., Rammer, W., & Thom, D. (2022). From sink to source: changing climate and disturbance regimes could tip the 21st century carbon balance of an unmanaged mountain forest landscape. Forestry: An International Journal of Forest Research, 96(3), 399-409.
Gallagher, G. (1974). Windthrown in state forests in the Republic of Ireland. Irish Forestry, 31(2), 14.
Kurz, W. A., Dymond, C. C., White, T. M., Stinson, G., Shaw, C. H., Rampley, G. J., Smyth, C., Simpson, B. N., Neilson, E. T., Trofymow, J. A., Metsaranta, J., & Apps, M. J. (2009). CBM-CFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecological Modelling, 220(4), 480-504.
McInerney, D., Barrett, F., Landy, J., & McDonagh, M. (2016). A rapid assessment using remote sensing of windblow damage in Irish forests following Storm Darwin. Irish Forestry, 73, 19.
How to cite: Longo, B. L., Tobin, B., and Byrne, K. A.: The impact of windthrow on carbon dynamics in temperate forests: a study case of Co. Laois in Ireland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13709, https://doi.org/10.5194/egusphere-egu25-13709, 2025.
According the the IPCC's 2023 Synthesis Report on Climate Change, global temperatures have risen approximately 1C since the the pre-industrial period, and there is significant uncertainty around future climate projections. Additionally, IPCC and related scientific literature find that the forestry sector is both vulnerable to and already feeling the effects of climate change. This work sets out to accomplish two goals. The first is contribute a new modeling approach that accounts of intra-annual changes in the variability of weather patterns on tree growth using signal processing and statistical modeling techniques. The second uses these models, in conjunction with climate projections, to develop a portfolio view of the forest through the lens of a changing and uncertain climate future. We leverage publicly available data from the USFS's Forest Inventory and Analysis Database, ORNL's DAYMET, and NASA's NEX-GDDP-CMIP6 to train models based on past observation and then simulate future growth based on 88 projections of future climate. Our models consider species-level reactions to site characteristics and weather patterns across the southeastern United States.
Finally, we compare the performance of roughly 4.6 million forest compositions, across four species and two management scenarios, to explore the trade-off between expected return and the variance of said return in a Markowitz Portfolio Selection framework when optimiizing financial returns to timber and carbon production, respectively. Special attention is paid to the performance of different species and their relative prevalence in portfolios along the efficient frontier.
How to cite: Baker, J. and Manner, R.: A Portfolio of Trees in a Changing Climate: Using Signal Processing and Individual Tree Growth Simulations to Develop Mean-Variance Tradeoff Frontiers for Forest Establishment in the Southern United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20362, https://doi.org/10.5194/egusphere-egu25-20362, 2025.
A reliable assessment of forest resource stocks, productivity, and harvesting is a central goal of environmental monitoring programs. More specifically, evaluating appropriate management tools has become increasingly critical for assessing forest sustainability. Understanding how forests respond to various management tools is essential for developing and implementing sustainable strategies that enhance the resilience of forest ecosystems. Chestnut forest management practices differ across regions, with coppicing being one of the most common techniques. However, evidence from coppiced chestnut forests has raised concerns, particularly related to soil erosion and land degradation.
This study explores the use of advanced remote sensing and spectroscopic techniques to address two key aspects of land and soil degradation in Italian forests. The first objective is to utilize multi-temporal hyperspectral and multispectral satellite imagery to develop and test methods for identifying clearcut areas in chestnut forests resulting from coppice treatments, as opposed to other causes of bare soil, such as wildfires. The second objective focuses on monitoring the erosion impacts on land and soil degradation using mid-infrared (MIR) spectroscopy.
To achieve these goals, the study employed large-scale, multi-temporal satellite imagery from PRISMA, Sentinel-2, and Landsat 8, with a focus on developing a robust methodology for accurately delineating clearcut zones in chestnut forests located in central Italy (Campania). A pixel-based approach was used to differentiate between clearcut areas and pixels affected by other disturbances, beginning with a bare soil masking technique to create an annual bare soil composite image, followed by the delineation of clearcut zones.
In addition to remote sensing analysis, a comprehensive soil sampling campaign was conducted at active clearcut sites to evaluate the impact of chestnut management on soil degradation, with a focus on soil organic carbon content. Samples were collected from multiple locations within the clearcut areas to account for spatial variability. This dataset was used to identify areas vulnerable to soil erosion through MIR spectroscopy, offering valuable insights into soil function and the long-term impacts of management techniques on soil health.
The results show that the annual chestnut coppice clearcut areas were mapped with overall accuracies of 80%, 87%, and 92% for Landsat 8, PRISMA, and Sentinel-2, respectively. This approach enabled a detailed, high-resolution assessment of land use changes over time and the identification of clearcut zones due to coppice treatments. The use of MIR spectroscopy also facilitated the assessment and monitoring of erosion-prone areas within chestnut clearcuts.
The findings of this research have significant implications for forest management strategies, particularly regarding sustainable forest management and conservation. This study contributes to enhancing land management strategies by providing a deeper understanding of the environmental consequences of forest systems management techniques and highlighting the potential of remote sensing and spectroscopy for monitoring soil degradation.
How to cite: Mzid, N. and Terribile, F.: Integrating Remote Sensing and Mid-Infrared Spectroscopy to Assess Land and Soil Degradation in Forest Ecosystems: Implications for Sustainable Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19324, https://doi.org/10.5194/egusphere-egu25-19324, 2025.
Posters on site: Wed, 30 Apr, 14:00–15:45 | Hall X1
Tree-related microhabitats (TreMs) are widely recognized in Europe as a strategy to enhance biodiversity in plantation forests. Selective cutting, a common forest management practice, helps maintain forest structure and ecological integrity while balancing economic and ecological needs. This study investigates the short-term effects of selective cutting on TreMs in Cryptomeria japonica plantations. We selected three 0.1 ha square plots within a C. japonica plantation in Xitou, managed by the Experimental Forest of National Taiwan University, and conducted surveys before and six months after selective logging to assess changes in microhabitat availability and heterogeneity. Key TreMs indicators, including cavities, growth deformation, micro-soils, dead branches, bark injuries, and epiphytes, were measured, and a terrestrial LiDAR with a 5-meter grid resolution was used to monitor detailed changes in canopy cover. Modified Hill numbers (q0, q1, q2) were applied to quantify changes in the total types, abundance, and evenness of TreMs. Wilcoxon signed-rank tests were used to compare pre- and post-cutting effects. Results indicated significant increases in Hill numbers (q0, q1, q2), reflecting short-term changes in TreMs. Geometric mean ratios between pre- and post-cutting data showed minimal changes in cavities (0.91, CI: 0.74-1), a moderate increase in growth deformation (1.20, CI: 1-1.41), and no change in micro-soils (1.00, CI: 1-1). In contrast, significant increases were observed in dead branches (1.28, CI: 1.12-1.48), bark injuries (1.11, CI: 1.01-1.22), and epiphytes (1.56, CI: 1.41-1.71), with epiphytes showing the most pronounced change. LiDAR analysis revealed a reduction in canopy cover following logging, which was closely associated with variations in epiphyte abundance, highlighting an interaction between canopy openness and epiphyte colonization. As the first application of the European TreM inventory in Taiwan, this study underscores the importance of further research on microhabitats as indicators of forest ecosystem function and biodiversity at local scales and calls for adaptation of this approach to Taiwan's unique environmental conditions.
Keywords: Cryptomeria japonica Plantation, Tree-related microhabitats (TreMs), Selective cutting, terrestrial LiDAR, Hill numbers, Wilcoxon signed-rank tests.
How to cite: Li, X. Y., Tsai, C.-C., Liu, C., and Cheng, S.-T.: Short-Term Effects of Selective Cutting on Tree-Related Microhabitats in Cryptomeria japonica Plantations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14976, https://doi.org/10.5194/egusphere-egu25-14976, 2025.
The distribution and composition of tree species in forests play a pivotal role in forest ecology, management, and carbon cycling. Consequently, their assessment and prediction are of paramount importance for effective forest management planning and the formulation of climate change adaptation strategies, both at local and national scales. The primary objective of this study was to interpret and forecast patterns of tree species distribution changes observed within Korean forests. To achieve this goal, we utilized data from the 5th to 7th National Forest Inventory to construct basal area data for all tree species within each permanent plot. Subsequently, we conducted a comprehensive analysis of the changing trends exhibited by each tree species. Additionally, we calculated climatic environmental indices highly relevant to tree species distribution using meteorological data provided by the Korea Meteorological Administration. Furthermore, a tree species distribution prediction model was developed by applying the Generalized Additive Model (GAM). Our analysis revealed that prominent tree species with a significant distribution presence in Korean forests included Pinus densiflora (36.2%), Quercus mongolica (14.6%), Quercus variabilis BL (11.0%), Quercus serrata Murray (4.3%), Pinus rigida (3.6%), Larix kaempferi (3.2%), Quercus acutissima (2.8%), and Pinus koraiensis (2.4%), based on basal area. Notably, Pinus densiflora, Quercus mongolica, and Pinus rigida showed a consistent decline in forest area. Furthermore, the results from the GAM analysis highlighted a substantial correlation between changes in basal area among major tree species and climate indices, including the Warmth Index (WI), Precipitation Effectiveness Index (PEI), and Minimum Temperature of the Coldest Month Index (MTCI). Forest age also emerged as a closely associated factor. The findings of this study hold significant implications, as they enable us to anticipate future alterations in tree species distributions attributable to natural selection and climate change. In addition, this is the first research using the individual tree-level for develping the tree species distribution model in South Korea.
∗This work was supported by Korea Environment Industry & Technology Institute through Climate Change R&D Project for New Climate Regime, funded by Korea Ministry of Environment (RS-2022-KE002294).
How to cite: Kim, M., Lee, J., Son, J., Ko, Y., and Hong, M.: Analysis of Species Composition and Distribution Changes in South Korean Forests Using the Individual Tree Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21265, https://doi.org/10.5194/egusphere-egu25-21265, 2025.
Understanding the intricacies of tree growth is crucial for understanding vegetation dynamics, optimizing carbon sequestration, preserving biodiversity, and enhancing climate adaptation within forest ecosystems. Leveraging primarily time-series data from dendrometers and weather stations provided by the International Cooperative Program for Forests (ICP-Forest), this study explores tree growth dynamics across diverse regions in Austria. Despite the value of this data, the nature of its collection introduces noise and errors, posing challenges for analysis. To address this, we employ advanced deep learning models within a machine and human interaction framework to predict tree growth, complemented by state-of-the-art explainability AI techniques (e.g., SHAP and LIME). By analyzing dendrometer and weather data, the study specifically investigates the impact of environmental components’ fluctuations over time on tree growth, offering valuable insights into forest ecosystem dynamics and their response to changing climatic conditions. We show that there is a strong correlation between soil moisture, temperature, and individual tree growth, emphasizing the importance of including these environmental factors in predictive models. Furthermore, we underscore the necessity of calculating tree competition parameters (estimated using terrestrial laser scanning data collected for the project), which play a vital role in accurately modelling tree dynamics and growth patterns. Lastly, initial forecasting results demonstrated high accuracy, providing a robust foundation and serving as a baseline for developing more sophisticated machine learning models. These insights collectively can advance the understanding of forest dynamics and offer a pathway toward enhancing global vegetation models and more effective data-driven decision-making in forestry.
How to cite: Wachsenegger, A., Lampert, J., and Duro, R.: Advancing Tree Growth Prediction with Interactive and eXplainable AI for Tackling Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18956, https://doi.org/10.5194/egusphere-egu25-18956, 2025.
Nitrogen (N) fertilization can enhance carbon (C) sequestration in biomass in boreal forests, which has potential to work as a tool addressing climate change and promoting sustainable forest management. The effects of N fertilization on tree growth have been studied widely in boreal forests in Finland, but a quantitative synthesis is still lacking. Therefore, we performed a quantitative synthesis of the effects of N fertilization on Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stands across Finland, utilizing data from 9 published studies encompassing 108 Scots pine and 57 Norway spruce observations. Our research involved building multivariate linear regression models that reflect the annual volume growth response induced by N fertilization, incorporating factors such as N dosage, site fertility, and climatic conditions. The models demonstrated that the N dose is the most significant predictor of volume growth response, which is positively correlated with average precipitation but negatively correlated with time since fertilization. Notably, site fertility had significant influence on growth increment for Scots pine. These findings underscore the importance of site-specific precision fertilization schemes to sustainably enhance growth and carbon sequestration, addressing key management implications for boreal forest resilience. Furthermore, this work contributes to the broader framework of forest system modeling by integrating multiple environmental variables and offers insights into adaptive management strategies.
How to cite: Jetsonen, J., Laurén, A., Peltola, H., Laurén, K., Launiainen, S., and Palviainen, M.: Volume growth responses of Scots pine and Norway spruce to nitrogen fertilization: quantitative synthesis of fertilization experiments in Finland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19976, https://doi.org/10.5194/egusphere-egu25-19976, 2025.
Changing climatic circumstances bring more frequent and intense extreme weather events that significantly impact forests in various ways. Since forests are the largest terrestrial sinks for carbon, and are among the richest biological environments on Earth, the goals of understanding the related challenges and improving the forest resilience is high on the agenda to mitigate climate change and save biodiversity. Achieving these goals requires access to data to derive vitality and health of trees, monitor and forecast tree growth, environmental conditions data, as well as suitable data modelling approaches.
Within our research, we exploited a wide set of data sources originating and ranging from remote sensing to in-situ measurement equipment allowing us to address the tree growth and health from different spatial and temporal points of view.
The data from dendrometers provided us with the high frequency (hourly), intraday variation of tree radial growth for assessing long-term growth and instantaneous changes in growth. These data are of extreme value, as no other means to monitor trees on such a high temporal resolution with a very high sensitivity exits. However, to understand the variations in these data, which directly show variation in the tree growth, especially in the context of extreme or sudden changes, they are evaluated within the environmental context. The environmental high-quality data were collected directly from forest sites selected from the Europe-wide Forest monitoring program (ICP-Forests), which has been providing high-quality data on the vitality and adaptability of trees, nutrient cycles, water balance, etc.
Furthermore, satellite Earth Observation (EO) data for single-tree detection and monitoring forest disturbances like selective logging and drought impacts have been likewise exploited, to explore if they may have an impact on the individual tree growth. We show that a CNN-based U-Net model trained on Very High Resolution (VHR) imagery demonstrates strong potential for identifying tree crowns and validating changes in forest structure. However, challenges such as limited training data diversity and low resolution for small trees underscore the need for further refinements.
Finally, terrestrial laser scanning (TLS) technique delivers single tree point-clouds not only allowing extraction of traditional tree features like diameters at different heights, tree height and crown dimensions, but also providing the possibility of statistical approaches for calculation of various metrics, e.g., point-cloud percentiles along the tree height and tree competition.
We describe the approaches on leveraging these, the challenges we have encountered (e.g., data gaps, errors in data, co-location), how we approached them, and all in the context of developing predictive AI-based, climate sensitive tree growth models, to support forest management on a local, regional and national level, and thus empowering response to minimize potentially harmful consequences for modern societies in line with the UN Sustainable Development Goals.
How to cite: Duro, R., Wachsenegger, A., Koloszyc, H., Zolles, A., Landivar, C., Gritsch, M., Bronner, G., Posch, L., Villalobos Gasca, A., Lampert, J., Cody, S., Rohrhofer, F. M., and Conti, D.: Advanced Monitoring Techniques and Modelling for Tree Growth under Influence of Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20137, https://doi.org/10.5194/egusphere-egu25-20137, 2025.
The continued introduction of non-native insect species, coupled with the rising threat of extreme wildfire events, poses significant risks to terrestrial ecosystems and the services they offer globally. However, the impact of invasive phloem-feeding insect species on fire severity is not well understood, particularly in terms of how they influence fire behaviour and the likelihood of crown fire ignition. Two experimental designs were set up to investigate how the alien tortoise scale (Toumeyella parvicornis) outbreaks have influenced fire behaviour dynamics and canopy surface reflectance in the Mediterranean P. pinea stands severely burnt in the summer of 2017. We combined Rothermel’s model for fire surface spread and Van Wagner’s crown ignition model to simulate fire behaviour and employed data from the Landsat 8 collection to detect canopy wilt symptoms related to T. parvicornis outbreaks. Simulating fire behaviour in single-storied P. pinea stands indicated that all predicted fires were surface fires. An uncertainty analysis concerning the inputs of the canopy fuel attributes model revealed that fires in thinned stands were entirely classified as surface fires. In contrast, in unthinned stands, only 62.7% were surface fires, with 37.3% categorised as conditional fire types. Among the Landsat 8 reflectance bands, only NIR, Green, and SWIR 2 were sensitive to the abundance of T. parvicornis. Based on these sensitive bands, two-band NIR-multiplied vegetation indexes were significantly associated with the abundance of T. parvicornis from the fall generation onward, when sooty mould consistently covered canopy needles. The divergence between observed and predicted fire behaviour underscores the need to investigate the processes and variables linked to T. parvicornis feeding activity on the trees to improve fire behaviour prediction. Understanding how insect outbreaks can modify fire behaviour in Mediterranean stands is crucial for effective management at stand and landscape levels. The satellite vegetation indexes based on sensitive reflectance bands represent an essential tool for an early recognition of insect outbreak distribution on large spatial scale.
How to cite: Saulino, L., Garonna, A. P., Rego, F. C., Rita, A., Aquino, A., Liuzzi, G., Fornaro, R., Pinelli, E., Silvestro, R., Rossi, S., and Saracino, A.: Outbreaks of invasive phloem feeding Toumeyella parvicornis modified fire behaviour and canopy surface reflectance in Mediterranean Pinus pinea forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7375, https://doi.org/10.5194/egusphere-egu25-7375, 2025.
Soil organic carbon (SOC) is the largest terrestrial reservoir in the active carbon cycle, and it is predicted to be a crucial component of the terrestrial carbon sink in the present day and in future climate scenarios. However, commonly used SOC models have been shown to inadequately represent SOC turnover, as evidenced by their consistent overestimation of the radiocarbon (14C) content in forest soils. This implies that models have too fast turnover rates and do not accurately capture the persistence of carbon in the different soil pools. To reconcile observational data and modeling frameworks, we conduct a detailed 14C-based study of the SOC dynamics across climatic and environmental gradients in 54 forest sites in Switzerland. At each site, we gather 14C data for the organic layers and five chemical and density fractions in the mineral soil. Calibrating a novel SOC model with these layer- and fraction-specific 14C data reveals an improved representation of turnover times and environmental dependencies, contrasting with existing models. In particular, we find that, by ignoring organic carbon respiration in the organic layers, most existing soil models have to effectively increase the turnover rates of SOC to compensate for the strongly overestimated carbon inputs into the mineral soil. Our results have the potential to significantly improve the representation of SOC in models, particularly under climate and environmental change.
How to cite: Brunmayr, A., Moreno Duborgel, M., Minich, L., Mittelbach, B., Eglinton, T., Hagedorn, F., and Graven, H.: Omission of organic layers in soil organic carbon models results in overestimation of carbon turnover rates: a 14C study of temperate and alpine forest soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15428, https://doi.org/10.5194/egusphere-egu25-15428, 2025.
Through photosynthesis, forests absorb significant amounts of CO₂ from the atmosphere while simultaneously releasing CO₂ back through respiration. The net carbon balance of a forest—whether it functions as a carbon sink (absorbing more CO₂ than it emits) or a carbon source (emitting more CO₂ than it absorbs)—depends on the relative magnitudes of these opposing carbon fluxes. The Mean Seasonal Cycle (MSC) provides a comprehensive view of the average carbon fluxes—Net Ecosystem Exchange (NEE), Gross Primary Production (GPP), and ecosystem respiration (Reco)—throughout the year.
In this study, we assessed the ‘Three Dimensional–Coupled Model Carbon Cycle–Forest Ecosystem Module’ (3D—CMCC—FEM) ability to simulate key carbon fluxes. We validated the model against observed data and investigated whether the seasonal carbon sink/source dynamics patterns are affected under two climate change scenarios across five European forest sites. More specifically, daily observed meteorological (1997–2005) data for model validation come from the Fluxnet2015 Dataset, and future climate scenarios (2006–2099) are projected from three Earth System Models. These models are part of the Climate Model Intercomparison Project 5 (CMIP5) and are driven by two Representative Concentration Pathways (RCP), specifically RCP 2.6 and RCP 6.0. The five case studies selected to represent key European forest species are chosen for their presence in the Fluxnet network. These sites include: the temperate European beech (Fagus sylvatica L.) forests at Collelongo, Italy (IT—Col), and Sorø, Denmark (DK—Sor); the maritime pine (Pinus pinaster Ait.) forest at Le Bray, France (FR—Lbr); the boreal Scots pine (Pinus sylvestris L.) forest at Hyytiälä, Finland (FI—Hyy); and the temperate Norway spruce (Picea abies (L.) H. Karst) forest at Bílý Kříž, Czech Republic (CZ—Bk1).
The model, validated under current climate conditions, confirmed the robust predictive ability in estimating NEE, GPP, and Reco across various forest species and climates. Under future climate scenarios, a consistent decline in forests C—sink capabilities is observed, with a more pronounced reduction under RCP 6.0. This decline is particularly pronounced in evergreen forests, which showed a greater decrease in NEE than deciduous forests. Finally, it was found that the number of days when evergreen forests act as C—sink increases over the years, with a forward shift of DoY to C—sink and a backward shift of DoY to C—source. In contrast, deciduous forests maintain a relatively stable number of C—sink (and C—source) days throughout the century (fixed DoY to C—sink or C—source). The DoY for deciduous forests remains constant, as the earlier onset of the growing season, driven by warming temperatures, is offset by an earlier increase in respiration. This indicates that over the long haul, deciduous forests demonstrate greater efficiency in utilizing photosynthates than evergreen forests.
How to cite: vangi, E., Morichetti, M., Dalmonech, D., Grieco, E., and Collalti, A.: Predicted Future Changes in the Mean Seasonal Carbon Cycle: Impacts of Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18906, https://doi.org/10.5194/egusphere-egu25-18906, 2025.
The lack of consistent and accessible forest data in Europe still provides a challenge for large-scale assessments and simulation studies. Here we compare two approaches for providing a detailed description of the current forest state in the complex mountainous forest landscape of Austria. Approach A integrates point-based National forest inventory with climate and remote sensing data to produce detailed gridded forest information (forest type and structural attributes) at 1 x 1 km resolution. In addition to these data sets, approach B integrates high resolution (10 m) remote sensing tree species data, which has recently become available for Austria. A special focus lies on how the detailed tree species maps can be used to improve the description of the current forest state.
How to cite: Pucher, C., Schadauer, K., Neumann, M., Hochauer, C., and Lexer, M. J.: Describing the current forest state in the complex mountainous forest landscape of Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21546, https://doi.org/10.5194/egusphere-egu25-21546, 2025.
How to cite: Rai, K. and Singh, G.: Advanced Backscatter Modeling for Enhanced Detection of Forest Disturbances in the Indian Subcontinent, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-188, https://doi.org/10.5194/egusphere-egu25-188, 2025.
The regeneration phase of forests is a crucial and vulnerable life stage in tree and forest development, which is likely to be intensified by climate change leading to increased drought events. In addition, the functioning of protective forests in mountain areas also needs to be continuously maintained or quickly restored after disturbances. It is widely thought that competition between trees negatively affects tree growth also in early live stages. Therefore, this study examines the effects of tree-tree competition on tree growth at a reforested post-disturbance site in the Northern Calcareous Alps. Despite high precipitation, the severe site conditions namely, shallow soils, steep slopes and southeastern aspect, result in drought-prone forests and site conditions likely to increase under climate change.
Following a windthrow, an experimental afforestation trail was established in 2010 with four coniferous and three broadleaved tree species. To calculate a distance-weighted competition index (CI), the tree height of the focal tree as well as the distances and heights of its three main competitors were measured in 2023. The CI was then calculated from the sum of distance-weighted ratios of the tree’s height to that of its competitors. Due to the high survival rate, this study focuses on results of the European larch (Larix decidua) and the Norway spruce (Picea abies).
Our results show that European larch has the highest growth rate, with mean tree height of 6.4 m after 12 growing seasons. Furthermore, a strong negative correlation (Pearson r = -0.789) is observed between its height and CI, suggesting that competition has a negative effect on growth. The Wilcoxon-Mann-Whitney test confirmed that tree height was significantly lower under high competition. The opposite was observed for Norway spruce, with a median tree height of 2.65 m with low CI and a tree height of 4.7 m at high CI values.
These results highlight the complex interactions in a mixed forest, where pioneer species such as European larch thrive under extreme site conditions and maintain their leading role in early succession stages. Norway spruce, however, appears to benefit from con- and interspecific clustering at this life stage, which we interpret as advantages of favourable microclimate under severe site conditions. These results highlight the dual role of competition: while it limits growth for some species, it can also create favourable microclimatic conditions for others. These different characteristics play a key role in restoration of forests and their ecosystem services after disturbance. Furthermore, the resilience of a mixed forest structure provides essential benefits during early succession and the crucial regeneration phase despite many challenges posed by climate change.
How to cite: Garkisch, S., Geitner, C., and Simon, A.: Effects of tree-tree competition on growth in post-disturbance, drought-prone montane forests , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13405, https://doi.org/10.5194/egusphere-egu25-13405, 2025.
Forests face increased threats from multiple hazards, with clear evidence of rising levels of disturbance across the world. In Europe part of this increased disturbance is from the increasing areas of forest across the continent, part is due to the management of the forests, and part is due to the changing climate. Recently the levels of damage have become alarming, with windstorms causing catastrophic damage, forest fires appearing in new and unexpected locations, and extended droughts followed by bark beetle infestations leading to very high mortality in Norway spruce across Central Europe.
The disturbance agents that affect forests are often linked together so that, for example, drought can lead to bark beetle outbreaks, windstorms will often lead to secondary damage from bark beetles, and dead wood from any disturbance can raise the fuel loading in the forest and increase the risk and intensity of any subsequent forest fires. Usually when forest risk has been studied or modelled each disturbance has been studied and modelled separately. In this paper we present a modelling effort to link together, in the R software environment, existing and new disturbance models for wind (fgr), bark beetles (IpsR), drought (SPEI) and forest fires (cffdrs). When coupled with climate sensitive growth models we are able to investigate predicted levels of damage until the end of the century for different climate scenarios. The disturbance models have been linked to the European Forest Dynamics Model (efdm) to assess levels of risk across Europe, and they have been linked to the 3-PG growth model (r3PG) to assess forest risk across Germany at a finer spatial scale. The results allow us to determine the effect of different forest management options and to search for optimal management approaches that can help in the development of more climate resilient forests.
How to cite: Gardiner, B., Do, T., Jorquera Olave, V., Bourke, R., and Hanewinkel, M.: Modelling Multiple Interconnected Hazards to Forests in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21432, https://doi.org/10.5194/egusphere-egu25-21432, 2025.
