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The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality ("edge effects"). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. [ABSTRACT FROM AUTHOR]

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

A rigorous TomoSAR imaging procedure is proposed to acquire high-resolution L-band images of a forest in a local area of interest. A focusing function is derived to relate the backscattered signals to the reflectivity function of the forest canopies without resorting to calibration. A forest voxel model is compiled to simulate different tree species, with the dielectric constant modeled with the Maxwell-Garnett mixing formula. Five different inverse methods are applied on two forest scenarios under three signal-to-noise ratios in the simulations to validate the efficacy of the proposed procedure. The dielectric-constant profile of trees can be used to monitor the moisture content of the forest. The use of a swarm of unmanned aerial vehicles (UAVs) is feasible to carry out TomoSAR imaging over a specific area to pinpoint potential spots of wildfire hazards. [ABSTRACT FROM AUTHOR]

To understand forest dynamics under today's changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional methods, e.g., remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forest states. In this work, we combine the new-generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results and explore the influence of different calculation methods of mean leaf parameters. For the majority of the forest stands, we generated good reflectances with all levels of forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R 2 = 0.62 ). This work provides a forward modeling approach for relating forest reflectance to forest characteristics. With this tool, it is possible to analyze a large set of forest stands with corresponding reflectances. This opens up the possibility to understand how reflectance is related to succession and different forest conditions. [ABSTRACT FROM AUTHOR]

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality (“edge effects”). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region.

A rigorous TomoSAR imaging procedure is proposed to acquire high-resolution L-band images of a forest in a local area of interest. A focusing function is derived to relate the backscattered signals to the reflectivity function of the forest canopies without resorting to calibration. A forest voxel model is compiled to simulate different tree species, with the dielectric constant modeled with the Maxwell-Garnett mixing formula. Five different inverse methods are applied on two forest scenarios under three signal-to-noise ratios in the simulations to validate the efficacy of the proposed procedure. The dielectric-constant profile of trees can be used to monitor the moisture content of the forest. The use of a swarm of unmanned aerial vehicles (UAVs) is feasible to carry out TomoSAR imaging over a specific area to pinpoint potential spots of wildfire hazards.

Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process-based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological-climate-induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest-based mitigation strategies and should be carefully considered within a portfolio of mitigation options.

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional methods, e.g., remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forest states. In this work, we combine the new-generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results and explore the influence of different calculation methods of mean leaf parameters. For the majority of the forest stands, we generated good reflectances with all levels of forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R2=0.62). This work provides a forward modeling approach for relating forest reflectance to forest characteristics. With this tool, it is possible to analyze a large set of forest stands with corresponding reflectances. This opens up the possibility to understand how reflectance is related to succession and different forest conditions.

Introduction: Among terrestrial ecosystems, forests represent large carbon stocks threatened by changing climatic conditions, deforestation, overexploitation, and forest degradation. Close to nature forestry may help forests to continue to acting as carbon sinks by promoting their resilience against disturbances. The EU decided to carry out carbon accounting of emissions and removals from managed forests under the Paris Agreement (PA) by using a projected Forest Reference Level (FRL) based on the continuation of recent management practices.Methods: We developed four conceptual scenarios that could build the Swiss Forest Reference Level and performed simulations over 50 years using Swiss National Forest Inventory (NFI) data and the empirical forest model MASSIMO. To improve MASSIMO, we further developed a new tree species-specific model for small scale mortality that accounts for the Swiss NFI design. Then, using projected biomass and mortality from MASSIMO, carbon budgets of mineral soil, litter, and dead wood were estimated using the Yasso07 model.Results: The U-shaped mortality model performed well (AUC 0.7). Small as well as large trees had the highest mortality probabilities, reflecting both young trees dying due to self-thinning and old trees from age, pests or abiotic influences. All scenarios matched their given harvesting and growing stock targets, whereby the share of broadleaves increased in all regions of Switzerland. This resulted in decreasing biomass growth, possibly due to a species shift from typically fast growing and more shade tolerant conifers to broadleaves. The CO2-balance of the conceptual scenarios ranged from 1.06 to −3.3 Mt CO2 a–1 under Increased Harvesting and Recent Management Practices (RMP), respectively. Rotation periods are shortened under Increased Harvesting, which is an important climate adaptive management strategy, but forests were predicted to become a net carbon source. In contrast, RMP resulted in similar harvesting amounts and forests as carbon sinks, as reported in the past. Further, the RMP scenario does not involve political assumptions and reflects the idea of the CMP approach used by the EU member states, which makes it comparable to other countries. Therefore, we propose the scenario RMP as a suitable and ideal candidate for the Swiss FRL.

The increasing impacts of climate change on forest ecosystems have triggered multiple model‐based impact assessments for the future, which typically focused either on a small number of stand‐scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high‐resolution assessment of the climate change sensitivity of managed Swiss forests (~10,000 km2), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the Third National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate‐adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (1) climate change scenarios, (2) local site conditions, and (3) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher‐elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low‐elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought‐tolerant species was found advisable across all elevations to mitigate future adverse climate‐induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments. [ABSTRACT FROM AUTHOR]

Tropical forests are an important component of the global carbon cycle, as they store large amounts of carbon. In some tropical regions, the forests are increasingly influenced by disturbances such as fires, which lead to structural changes but also alter species composition, forest succession, and carbon balance. However, the long-term consequences on forest functioning are difficult to assess. The majority of all global forest fires are found in Africa. In this study, a forest model was extended by a fire model to investigate the long-term effects of forest fires on biomass, carbon fluxes, and species composition of tropical forests at Mt. Kilimanjaro (Tanzania). According to this modeling study, forest biomass was reduced by 46% by fires and even by 80% when fires reoccur. Forest regeneration lasted more than 100 years to recover to pre-fire state. Productivity and respiration were up to 4 times higher after the fire than before the fire, which was mainly due to pioneer species in the regeneration phase. Considering the full carbon balance of the regrowing forest, it takes more than 150 years to compensate for the carbon emissions caused by the forest fire. However, functional diversity increases after a fire, as fire-tolerant tree species and pioneer species dominate a fire-affected forest area and thus alter the forest succession. This study shows that forest models can be suitable tools to simulate the dynamics of tropical forests and to assess the long-term consequences of fires. [ABSTRACT FROM AUTHOR]

Global change is shifting disturbance regimes that may rapidly change ecosystems, sometimes causing ecosystems to shift between states. Interactions between disturbances such as fire and disease could have especially severe effects, but experimental tests of multi‐decadal changes in disturbance regimes are rare. Here, we surveyed vegetation for 35 years in a 54‐year fire frequency experiment in a temperate oak savanna–forest ecotone that experienced a recent outbreak of oak wilt. Different fire regimes determined whether plots were savanna or forest by regulating tree abundance (r2 = 0.70), but disease rapidly reversed the effect of fire exclusion, increasing mortality by 765% in unburned forests, but causing relatively minor changes in frequently burned savannas. Model simulations demonstrated that disease caused unburned forests to transition towards a unique woodland that was prone to transition to savanna if fire was reintroduced. Consequently, disease–fire interactions could shift ecosystem resilience and biome boundaries as pathogen distributions change. [ABSTRACT FROM AUTHOR]

Abstract Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process‐based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological‐climate‐induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest‐based mitigation strategies and should be carefully considered within a portfolio of mitigation options.

Abstract Introduction Changes in socio-economy and climate are affecting the demand of wood products globally. At the same time, society requires that forest supporting structures like biodiversity are maintained and preserved while the demand for wood products is also covered. Management support systems, like forest simulation models, that are able to analyze connections as well as quantify trade-offs between forest structure management and biodiversity indicators are highly sought. However, such models are generally developed for the local plot or stand scale only and ecosystem-scale analyses are missing. In this study, we analyzed ways to interpret results from the single-tree forest simulator SILVA from the local to the ecosystem scale. We also analyzed the impacts of forest management on biodiversity using two species diversity indicators, the species profile index and the species intermingling, for scenarios adapted from the GLOBIOM model in the case study “Augsburg Western Forests”, a high productive region in South-Germany. In order to evaluate diversity tendencies across the ecosystem, we applied a moving window methodology. Results The relevance of scale for the interpretation of management effects on species diversity was shown and clear differences between scenarios revealed. The differences between scenarios were particularly visible when comparing the two diversity indicators, especially because the species profile index focuses on vertical and horizontal information and the species intermingling focuses mainly on horizontal structures. Under a multifunctional scenario, biodiversity values could be preserved at all scales in the vertical dimension. However, under a bio-energy-oriented scenario diversity at the local scale was reduced, although at the ecosystem level, and only in the horizontal dimension, diversity remained at relatively high values. Conclusions With this work, we can conclude that integrative modeling, with multiple scenarios, is highly needed to support forestry decision making and towards the evolution of forest management to consider the ecosystem scale, especially when the optimization of diversity is a management priority.

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.

Current criteria to define managed forests are inconsistent among countries’ reports of GHG emissions to UNFCCC. Integrated assessment models used for assessing countries’ mitigation pathways employ a proxy for managed forests that differ from the countries’ criteria. This is one of the reasons for the gap of 5.5 GtCO2 year−1 between the modelled and reported global land-use GHG emissions. Using multiple data, we developed a map of managed forests (0.5 × 0.5 deg), consistent with official GHG inventories. We applied the map in the G4M model for masking a managed forest area and estimating the GHG emissions from that area.

Forests play an important role in climate regulation due to carbon sequestration. However, a deeper understanding of forest carbon flux dynamics is often missing due to a lack of information about forest structure and species composition, especially for non-even-aged and species-mixed forests. In this study, we integrated field inventory data of a species-mixed deciduous forest in Germany into an individual-based forest model to investigate daily carbon fluxes and to examine the role of tree size and species composition for stand productivity. This approach enables to reproduce daily carbon fluxes derived from eddy covariance measurements (R2 of 0.82 for gross primary productivity and 0.77 for ecosystem respiration). While medium-sized trees (stem diameter 30–60 cm) account for the largest share (66%) of total productivity at the study site, small (0–30 cm) and large trees (>60 cm) contribute less with 8.3% and 25.5% respectively. Simulation experiments indicate that vertical stand structure and shading influence forest productivity more than species composition. Hence, it is important to incorporate small-scale information about forest stand structure into modelling studies to decrease uncertainties of carbon dynamic predictions.

Tropical forests are an important component of the global carbon cycle, as they store large amounts of carbon. In some tropical regions, the forests are increasingly influenced by disturbances such as fires, which lead to structural changes but also alter species composition, forest succession, and carbon balance. However, the long-term consequences on forest functioning are difficult to assess. The majority of all global forest fires are found in Africa. In this study, a forest model was extended by a fire model to investigate the long-term effects of forest fires on biomass, carbon fluxes, and species composition of tropical forests at Mt. Kilimanjaro (Tanzania). According to this modeling study, forest biomass was reduced by 46% by fires and even by 80% when fires reoccur. Forest regeneration lasted more than 100 years to recover to pre-fire state. Productivity and respiration were up to 4 times higher after the fire than before the fire, which was mainly due to pioneer species in the regeneration phase. Considering the full carbon balance of the regrowing forest, it takes more than 150 years to compensate for the carbon emissions caused by the forest fire. However, functional diversity increases after a fire, as fire-tolerant tree species and pioneer species dominate a fire-affected forest area and thus alter the forest succession. This study shows that forest models can be suitable tools to simulate the dynamics of tropical forests and to assess the long-term consequences of fires.

Simulation, calibration and validation protocols for the model 3D-CMCC-CNR-FEM: a case study in the Bonis’ watershed (Calabria, Italy). At present, the climate changes issue is perhaps the greatest threat that is affecting people and the environment. Forest ecosystems have a key role in the mitigation of climate change. In this context, the prediction of the evolution and growth dynamics of the forests including carbon and water fluxes, and in relation to forest management has become a primary objective. The present study aims at defining a protocol for data collection and the workflow for using the 3D-CMCC-CNR-FEM model in a small mountain watershed in the Calabria region. Within this work we synergistically integrate data coming from different methods (e.g., LiDAR, eddy covariance and sample area) to predict forest dynamics (growth, carbon and water fluxes). Carbon and water fluxes will be simulated considering also the effects of forest management.

Abstract Background Tropical forests play an important role in the global carbon (C) cycle. However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood. Montane forests are highly endangered due to logging, land-use and climate change. Our objective was to analyse how the carbon balance changes during forest succession. Methods In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models. We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions (ravines and lower slopes vs upper slopes and ridges). Results The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange (NEE) of 9.3 Mg C∙(ha∙yr)−1 during its early successional stage (0–100 years). In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C∙(ha∙yr) –1. The simulated variability of the NEE was within the range of the field data. We discovered several forest attributes (e.g., basal area or the relative amount of pioneer trees) that can serve as predictors for NEE for young forest stands (0–100 years) but not for those in the late successional stage (500–1,000 years). In case of young forest stands these correlations are high, especially between stand basal area and NEE. Conclusion In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity. To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests. With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes. These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change.

Abstract Background Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge. In this study, we tested the capability of an individual-based forest gap model to display carbon fluxes at yearly and daily time scales. The forest model was applied to a spruce forest to simulate the gross primary production (GPP), respiration and net ecosystem exchange (NEE). We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model. Results Six years were simulated at a daily time scale and compared to the observed eddy covariance (EC) data. In general, the seasonal cycle of the individual carbon fluxes was correctly described by the forest model. However, the estimated GPP differed from the observed data on the days of extreme climatic conditions. Two new parameterizations were developed: one resulting from a numerical calibration, and the other resulting from a filtering method. We suggest new parameter values and even a new function for the temperature limitation of photosynthesis. Conclusions The forest model reproduced the observed carbon fluxes of a forest ecosystem quite well. Of the three parameterizations, the calibrated model version performed best. However, the filtering approach showed that calibrated parameter values do not necessarily correctly display the individual functional relations. The concept of simulating forest dynamics at the individual base is a valuable tool for simulating the NEE, GPP and respiration of forest ecosystems.

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time ( τ ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations ( R 2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems., Competing Interests: We declare we have no competing interests., (© 2023 The Authors.)

Summary: Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from the individual to the global scale and constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimisation theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high‐resolution data, such as those derived from airborne laser scanning, with individual‐based forest modelling offers insight into how plant size contributes to large‐scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data‐model fusion, and how individual‐based models can serve as data integrators for dynamic global vegetation models. [ABSTRACT FROM AUTHOR]

• Recent model development, called "Forest Factory 2.0″ which generates various virtual forest stands for different biomes on earth. • Data product (including 700,000 forest stands) to demonstrate the potential of this approach. • Gaining knowledge about forests by analyzing the state space of forests. • Systematic mechanistic analyses of structure-function relationships across biomes. Forests, as one of the most important carbon sinks on earth, are more and more under stress by environmental changes. The dynamics of forests and consequently their functions in general, begin to change. We therefore present a recent model development, called "Forest Factory 2.0″, which generates various virtual forest stands for different biomes on earth. This approach allows to generate forests using the architecture and processes of forest models (here we use the individual-based gap model FORMIND). Using Forest Factory 2.0, we generated 700,000 forest stands in seven different ecoregions. In contrast to the tradition of investigating the development of individual forest stands over time, we used the Forest Factory 2.0 as a tool to gain knowledge about forests by analyzing the state space of forests. We conducted a structural sensitivity analysis to compare the relationships between structural properties and biomass, productivity, as well as species evenness of forests. In this study we analyze the state space of forests in different biomes and demonstrate the potential of this approach for theoretical ecology. [ABSTRACT FROM AUTHOR]

In this paper a new concept for modeling uneven-aged forests (UEAF) is presented. The term UEAF in this article encloses all forests that deviate from the even-aged structure. The matrix model is area-based, in that the forest under study is described by a distribution of areas over fixed state-spaces spanned by stem number and volume per hectare classes. Dynamics is introduced as transitions of areas inside the state-space during the simulation. Harvesting activities and the occurrence of calamities are explicitly handled. The model is designed to be suitable for large-scale analyses. The concept was tested in an application to Austrian National Forest Inventory (NFI) data. Results shown, including a comparison to older inventory data, indicate that it is worth further elaborating on the concept and the model. The work will be continued and in the next step the model concept will be applied in several other countries.

Forest models are increasingly being used to study ecosystem functioning, through simulation of carbon fluxes and productivity in different biomes and plant functional types all over the world. Several forest models based on the concept of Light Use Efficiency (LUE) rely mostly on a simplified mathematical structure and empirical parameters, require little amount of data to be run, and their computations are usually fast. However, possible calibration issues must be investigated in order to ensure reliable results. Here we addressed the important issue of delayed convergence when calibrating LUE models, characterized by a multiplicative structure, with a Bayesian approach. We tested two models (Prelued and the Horn and Schulz (2011a) model), applying three Markov Chain Monte Carlo-based algorithms with different number of iterations, and different sets of prior parameter distributions with increasing information content. The results showed that recently proposed algorithms for adaptive calibration did not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest models used here, and that a high number of iterations is required to stabilize in the convergence region. This can be partly explained by the multiplicative mathematical structure of the models, with high correlations between parameters, and by the use of empirical parameters with neither ecological nor physiological meaning. The information content of the prior distributions of the parameters did not play a major role in reaching convergence with a lower number of iterations. We conclude that there is a need for a more careful approach to calibration to solve potential problems when applying models characterized by a multiplicative mathematical structure. Moreover, the calibration proved time consuming and mathematically difficult, so advantages of using a computationally fast and user-friendly model were lost due to the calibration process needed to obtain reliable results. [ABSTRACT FROM AUTHOR]

Forest development models have been used to predict future harvesting potentials and forest management reference levels under the Kyoto guidelines. This contribution aims at presenting the individual-tree simulator MASSIMO (MAnagement Scenario SImulation Model) and demonstrating its scope of applications with simulations of two possible forest management reference levels (base or business as usual) in an example application. MASSIMO is a suitable tool to predict timber harvesting potentials and forest management reference levels to assess future carbon budgets of Swiss forests. While the current version of MASSIMO accurately accounts for legacy effects and management scenarios, effects of climate and nitrogen deposition on growth, mortality, and regeneration are not yet included. In addition to including climate sensitivity, the software may be further improved by including effects of species mixture on tree growth and assessing ecosystem service provision based on indicators.

The increasing use of satellite faraway sensing for civilian use has proved to be the most cost effective means of mapping and monitoring environmental changes. From this point of view, these new tools are now essential in monitoring operations for vegetation and non-renewable resources, especially in developing countries. In today's literature a series of 262 spectral indices are defined to evaluate the vegetation health for a specific area. For the vast majority of these infrared spectral index values are included. Infrared recording devices are quite expensive. In this respect, the possibility of using only the visible domain should be experienced in order to reduce the operational costs. The main hypothesis comes from the fact that any changes in vegetation status cannot be reduced and described only by the infrared domain changes. This study presents the results of a general aim: to design, to investigate and to confirm a specific analysis method that uses only the visible RGB spectra by using historical recordings. With this main objective of getting a method with a minimal operational cost, in this regard a combination of two classical techniques was tested - satellite image monitor analysis and UAV high resolution images use. The further exposed method in this paper suggests the joining of the two monitoring methods in successive phases: in the first step resolution suitable satellite images were used in order to succeed in building a forest model. The model confirmation and the validation process is done in the second stage by using specific UAV flights for high resolution images acquiring over a series of gauging points from the studied area. The presented case study is that of Bălăbăneşti Forest from Galati County. The study was conducted between 2005 and 2016. Preliminary results on the composition evaluations are promising and the research is ongoing. [ABSTRACT FROM AUTHOR]

At present, the climate changes issue is perhaps the greatest threat that is affecting people and the environment. Forest ecosystems have a key role in the mitigation of climate change. In this context, the prediction of the evolution and growth dynamics of the forests including carbon and water fluxes, and in relation to forest management has become a primary objective. The present study aims at defining a protocol for data collection and the workflow for using the 3D-CMCC-CNR-FEM model in a small mountain watershed in the Calabria region. Within this work we synergistically integrate data coming from different methods (e.g., LiDAR, eddy covariance and sample area) to predict forest dynamics (growth, carbon and water fluxes). Carbon and water fluxes will be simulated considering also the effects of forest management. [ABSTRACT FROM AUTHOR]

The main ecosystem services (ES) central European mountain forests provide are: protection against gravitational hazards, timber production, recreation, biodiversity conservation and carbon storage, which are all in high demand. These demands make managing mountain forests a challenging task, involving manifold synergies and conflicts between the different ES. There is therefore an urgent need for appropriate concepts and tools for support decisions in forest management and planning (FMP) to take into consideration all ES and to manage the wide variety of information types, parameters and uncertainties involved in assessing the sustainability of ES. Multi-criteria decision analysis (MCDA) provides a suitable set of methods for sustainability evaluations. In this study sustainability means the persistent fulfilment of the required ES. To address all the phases of the FMP process, MCDA and forest models should be applied together, with indicators providing the main interfaces to combine them. This paper aims to: i) review assessment approaches in order to select appropriate and widely accepted indicators for measuring and assessing the effects of different silvicultural management alternatives on forest ES, and ii) present additional standardisation approaches (value functions) for each indicator. Standardisations are necessary to make the different ES comparable and to study synergies and trade-offs between different management objectives in MCDA. The main ES in central European mountain regions are considered, with a clear focus on those indicators that are directly derivable from forest model outputs and that can refer to sustainable forest management practices. The scales considered are that of the single forest stand and of the larger forest management unit. A holistic indicator-based analysis framework for FMP in mountain forests can be built using the indicators and value functions described. The influence of different management alternatives on ES can then be evaluated, taking into consideration the instruments and information on forest management (forest models, inventory) available. All indicators are selected according to existing and approved approaches that only require data that is normally available in operational forest management. The framework can thus be an important element in developing a decision support system for FMP in mountain forests. [ABSTRACT FROM AUTHOR]

At present, the climate changes issue is perhaps the greatest threat that is affecting people and the environment. Forest ecosystems have a key role in the mitigation of climate change. In this context, the prediction of the evolution and growth dynamics of the forests including carbon and water fluxes, and in relation to forest management has become a primary objective. The present study aims at defining a protocol for data collection and the workflow for using the 3D-CMCC-CNR-FEM model in a small mountain watershed in the Calabria region. Within this work we synergistically integrate data coming from different methods (e.g., LiDAR, eddy covariance and sample area) to predict forest dynamics (growth, carbon and water fluxes). Carbon and water fluxes will be simulated considering also the effects of forest management. [ABSTRACT FROM AUTHOR]

Background: Tropical forests play an important role in the global carbon (C) cycle. However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood. Montane forests are highly endangered due to logging, land-use and climate change. Our objective was to analyse how the carbon balance changes during forest succession. Methods: In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models. We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions (ravines and lower slopes vs upper slopes and ridges). Results: The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange (NEE) of 9.3 Mg C∙(ha∙yr) during its early successional stage (0-100 years). In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C∙(ha∙yr) . The simulated variability of the NEE was within the range of the field data. We discovered several forest attributes (e.g., basal area or the relative amount of pioneer trees) that can serve as predictors for NEE for young forest stands (0-100 years) but not for those in the late successional stage (500-1,000 years). In case of young forest stands these correlations are high, especially between stand basal area and NEE. Conclusion: In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity. To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests. With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes. These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change. [ABSTRACT FROM AUTHOR]

Background: Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge. In this study, we tested the capability of an individual-based forest gap model to display carbon fluxes at yearly and daily time scales. The forest model was applied to a spruce forest to simulate the gross primary production (GPP), respiration and net ecosystem exchange (NEE). We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model. Results: Six years were simulated at a daily time scale and compared to the observed eddy covariance (EC) data. In general, the seasonal cycle of the individual carbon fluxes was correctly described by the forest model. However, the estimated GPP differed from the observed data on the days of extreme climatic conditions. Two new parameterizations were developed: one resulting from a numerical calibration, and the other resulting from a filtering method. We suggest new parameter values and even a new function for the temperature limitation of photosynthesis. Conclusions: The forest model reproduced the observed carbon fluxes of a forest ecosystem quite well. Of the three parameterizations, the calibrated model version performed best. However, the filtering approach showed that calibrated parameter values do not necessarily correctly display the individual functional relations. The concept of simulating forest dynamics at the individual base is a valuable tool for simulating the NEE, GPP and respiration of forest ecosystems. [ABSTRACT FROM AUTHOR]

Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (ΔH) and the actual changes in aboveground biomass (ΔAGB). Different approaches can be used to accomplish this task: direct approaches link ΔH directly to ΔAGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (>28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating ΔAGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using ΔH in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSEdirect = 18.7 t ha−1, RMSEindirect = 12.6 t ha−1 and RMSEdir+tex = 12.4 t ha−1. Further analyses revealed height-dependent biases in the ΔAGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies.

In many regions of the world, drought is projected to increase under climate change, with potential negative consequences for forests and their ecosystem services (ES). Forest thinning has been proposed as a method for at least temporarily mitigating drought impacts, but its general applicability and longer-term impacts are unclear. We use a processbased forest model to upscale experimental data for evaluating the impacts of forest thinning in a drought-susceptible valley in the interior of the European Alps, with the specific aim of assessing (1) when and where thinning may be most effective and (2) the longer-term implications for forest dynamics. Simulations indicate that forests will be impacted by climate-induced increases in drought across a broad elevation range. At lower elevations, where drought is currently prevalent, thinning is projected to temporarily reduce tree mortality, but to have minor impacts on forest dynamics in the longer term. Thinning may be particularly useful at intermediate and higher elevations as a means of temporarily reducing mortality in drought-sensitive species such as Norway spruce and larch, which currently dominate these elevations. However, in the longer term, even intense thinning will likely not be sufficient to prevent a climate change induced dieback of these species, which is projected to occur under even moderate climate change. Thinning is also projected to have the largest impact on long-term forest dynamics at intermediate elevations, with the magnitude of the impact depending on the timing and intensity of thinning. More intense thinning that is done later is projected to more strongly promote a transition to more drought-tolerant species. We conclude that thinning is a viable option for temporarily reducing the negative drought impacts on forests, but that efficient implementation of thinning should be contingent on a site-specific evaluation of the near term risk of significant drought, and how thinning will impact the rate and direction of climate driven forest conversion. [ABSTRACT FROM AUTHOR]

In this paper a new concept for modeling uneven-aged forests (UEAF) is presented. The term UEAF in this article encloses all forests that deviate from the even-aged structure. The matrix model is area-based, in that the forest under study is described by a distribution of areas over fixed state-spaces spanned by stem number and volume per hectare classes. Dynamics is introduced as transitions of areas inside the state-space during the simulation. Harvesting activities and the occurrence of calamities are explicitly handled. The model is designed to be suitable for large-scale analyses. The concept was tested in an application to Austrian National Forest Inventory (NFI) data. Results shown, including a comparison to older inventory data, indicate that it is worth further elaborating on the concept and the model. The work will be continued and in the next step the model concept will be applied in several other countries. [ABSTRACT FROM AUTHOR]

Tropical forests are vital ecosystems which provide numerous ecological functions and forest models can be used to simulate their dynamics. However, due to the high species diversity of tropical forests and the common lack of detailed knowledge about these species, simulating the behavior of each species separately is not feasible. Therefore, species with common characteristics are usually aggregated into species groups. Although the number of species groups is likely to be an important characteristic of forest models, little research has been done on its effect on the results of the model. In this article, we compare the effect of the number of species groups using a physiological forest gap model and review our results using 28 years of field data from a 50 ha forest plot in Panama as well as a chronosequence over four centuries from the same area. The number of simulated groups ranged from 1 to 4, 9 and 16. The parameterization with a single species group is a "neutral" parameterization with all simulated trees being ecophysiologically identical. The number of groups turned out to be an important characteristic of the model which influences its results on a fundamental level. Steady-states characteristics of forests like stem-numbers and stem-size distributions can be successfully simulated with even a single species group while modeling the long-term dynamics requires a higher number of groups. On the downside, a higher number of groups makes parameterization and fitting of the model more difficult. We conclude that the number of species groups is a vital characteristic of a forest model which has far-reaching consequences for its results and needs to be chosen with care. [ABSTRACT FROM AUTHOR]

Abstract: Forest models can be useful tools to improve our understanding of forest dynamics and to evaluate potential impacts of climate change. There is an ongoing debate how drought events influence the dynamics of tropical forests. In this study, we explored the role of changes in precipitation on tropical forests in Madagascar. Therefore, we derived a new parameterization of the process-based forest model FORMIND using local forest inventory measurements. This model was extended by a drought sensitivity module based on a water use efficiency concept. The objective of this study is to evaluate how different levels of water availability modify forest productivity, and net ecosystem exchange as a function of mean annual precipitation. Our simulation results indicate that a moderate precipitation decline (0%–30% of current precipitation conditions) has only minor impact on forest carbon stocks and exchange. A rainfall decline below 30% of current precipitation conditions would change forest structure considerably. [Copyright &y& Elsevier]