National Forest Inventory Data Research Articles

Soil and climate‐dependent ingrowth inference: broadleaves on their slow way to conquer Swiss forests

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present‐day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio‐geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid‐21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast‐changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Spatial Distribution of Soil Organic Carbon in the Forests of Nepal

Soil organic carbon (SOC) is the major constituent of the soil organic matter. SOC stocks are determined by several factors such as altitude, slope, aspect, canopy cover, and vegetation type. Using the Third National Forest Inventory (2010–2014) data of Nepal, we assessed SOC status in forests at a national scale for the better understanding of the SOC distribution within Nepal. In this study, we estimated SOC against different factors and tested the spatial distribution of SOC using analysis of variance (ANOVA). The results showed that the forests located at a higher altitude have higher SOC accumulation. In particular, broadleaved forests exhibit a higher amount of carbon stock compared to other forest types. Moreover, forests with a larger canopy cover, located on a higher slope, and with a cooler aspect are associated with a higher accumulation of SOC. The SOC stock in the forest varies according to altitude, slope, aspect, canopy cover, and forest type, which might be attributed to the change in the microclimate of the area. The significant increase in SOC amount with the increase in slope, altitude, and crown cover helps to understand the extent of SOC distribution in forests. Broadleaved forests with a larger canopy cover in the higher altitude region have a higher SOC retention potential, which is likely to contribute to mitigating the impacts of climate change by sinking more carbon into the soil.

Harmonised statistics and maps of forest biomass and increment in Europe

Forest biomass is an essential resource in relation to the green transition and its assessment is key for the sustainable management of forest resources. Here, we present a forest biomass dataset for Europe based on the best available inventory and satellite data, with a higher level of harmonisation and spatial resolution than other existing data. This database provides statistics and maps of the forest area, biomass stock and their share available for wood supply in the year 2020, and statistics on gross and net volume increment in 2010–2020, for 38 European countries. The statistics of most countries are available at a sub-national scale and are derived from National Forest Inventory data, harmonised using common reference definitions and estimation methodology, and updated to a common year using a modelling approach. For those counties without harmonised statistics, data were derived from the State of Europe’s Forest 2020 Report at the national scale. The maps are coherent with the statistics and depict the spatial distribution of the forest variables at 100 m resolution.

National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data

Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystem services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large-area tree species maps. Here, the high temporal resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology, information that may also be valuable for improving forest tree species mapping.The objective of this study was to map the main tree species in Germany using combined Sentinel-1 and Sentinel-2 time series, and to identify and address challenges related to the use of National Forest Inventory (NFI) data in remote sensing applications. We generated cloud free time series with 5-day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites. Further, we incorporate information on topography, meteorology, and climate to account for environmental gradients. To use NFI data for training machine learning models, we address the following challenges: 1) link satellite pixels with variable radius NFI plots, for which the precise area is unknown, and 2) efficiently utilize mixed-species NFI plots for model training and validation. In the past, accuracies for pixel-level species maps were often estimated solely for homogeneous pure-species stands. In this study, we assess how well pixel-level maps generalize to mixed plot conditions.Our results show the potential of combined Sentinel-2 and Sentinel-1 time series with NFI data for tree species mapping in large, environmentally diverse landscapes. Classification accuracy in pure stands ranged between 72% and 97% (F1-score) for five dominant species, while mapping less frequent species remained challenging. When including mixed forest stands in the accuracy assessment, accuracy decreased by 4–14 percentage points for the most dominant species groups. Our study highlights the importance of including mixed-forest stands when training and validating tree species maps. Based on these results, we discuss potentials and remaining challenges for tree species mapping at the national level. Our findings allow to further improve national-level tree species mapping with medium to high resolution data and provide guidance for similar approaches in other countries where ground-based inventory data are available.

Land use change and forest management effects on soil carbon stocks in the Northeast U.S.

BackgroundIn most regions and ecosystems, soils are the largest terrestrial carbon pool. Their potential vulnerability to climate and land use change, management, and other drivers, along with soils’ ability to mitigate climate change through carbon sequestration, makes them important to carbon balance and management. To date, most studies of soil carbon management have been based at either large or site-specific scales, resulting in either broad generalizations or narrow conclusions, respectively. Advancing the science and practice of soil carbon management requires scientific progress at intermediate scales. Here, we conducted the fifth in a series of ecoregional assessments of the effects of land use change and forest management on soil carbon stocks, this time addressing the Northeast U.S. We used synthesis approaches including (1) meta-analysis of published literature, (2) soil survey and (3) national forest inventory databases to examine overall effects and underlying drivers of deforestation, reforestation, and forest harvesting on soil carbon stocks. The three complementary data sources allowed us to quantify direction, magnitude, and uncertainty in trends.ResultsOur meta-analysis findings revealed regionally consistent declines in soil carbon stocks due to deforestation, whether for agriculture or urban development. Conversely, reforestation led to significant increases in soil C stocks, with variation based on specific geographic factors. Forest harvesting showed no significant effect on soil carbon stocks, regardless of place-based or practice-specific factors. Observational soil survey and national forest inventory data generally supported meta-analytic harvest trends, and provided broader context by revealing the factors that act as baseline controls on soil carbon stocks in this ecoregion of carbon-dense soils. These factors include a range of soil physical, parent material, and topographic controls, with land use and climate factors also playing a role.ConclusionsForest harvesting has limited potential to alter forest soil C stocks in either direction, in contrast to the significant changes driven by land use shifts. These findings underscore the importance of understanding soil C changes at intermediate scales, and the need for an all-lands approach to managing soil carbon for climate change mitigation in the Northeast U.S.

Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

ABSTRACT Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained.

Use of national forest inventory data to develop stand density driven models for understorey shrubs and overstorey fuel variables and associated temporal dynamics in commercial plantations

Biomass tends to accumulate both in overstorey and understorey layers of forests growing in productive areas. The rapid growth of forest plantations established in productive areas can thus also lead to the creation of fire-prone landscapes. In this study, we assessed the extent to which overstorey stand density determines current values of the fine shrub fuel load (Wshr_G1) in the understorey and also two major canopy fuel characteristics related to crown fire activity -canopy base height (CBH) and canopy bulk density (CBD)- and the temporal dynamics of these. For this purpose, we developed two types of models for the three fuel complex variables: i) extreme response models, to define the upper limit of Wshr_G1 and CBD and the lower limit of CBH, the values of which may depend on stand density; and ii) dynamic models, to estimate the probability of increasing fuel loads and rate of change when a more hazardous situation is predicted. Data were obtained from 8087 plots, measured in the third, fourth and fifth Spanish National Forest Inventories, in plantations dominated by the major commercial species in northern Spain (Pinus pinaster, Pinus radiata, Pinus sylvestris and Eucalyptus globulus). The extreme response models generally explain more than 60% of the observed variability in the three fuel complex variables. According to these models, maximum shrub fuel load and CBD and minimum CBH in stands of the four tree species were limited by stand basal area, with different responses by different species. The dynamics of the fuel complex variables were determined by canopy cover, estimated as a proxy for stand density. The use of a two-step regression approach enabled realistic modelling of the dynamics of the fuel complex variables, allowing for a decrease in the shrub fuel biomass and in CBD between two time points. The dynamic models developed are age-independent and are therefore useful for practical applications because they can be applied to any stand without the need to know the stand age. Overall, the study findings provide insights into the complex relationship between both understorey and overstorey fuels and stand density in commercial plantations. As assessment of forest fuel complex variables is a prerequisite for most fire management activities, the study findings have important implications for forest and fuel management and planning in the region.

On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

Country-wide assessment of biodiversity, naturalness and old-growth status using national forest inventory data

Assessing forest biodiversity, naturalness and old-growth status (B-N-OG) is crucial for supporting sustainable forest planning, yet comprehensive monitoring networks specifically designed for such purposes are lacking in many countries. National Forest Inventories (NFIs) are the official source of statistics on status and trends of forests. While initially designed for wood production assessment, NFI data may be pivotal for ecological forest monitoring, thanks to their robust sampling protocols—enabling statistical inference—and regular field campaigns that ensure continuous information updates. As a consequence, in this study we explore the potential of NFIs for estimating B-N-OG indexes, aiming to establish compatible, scientifically relevant, and cost-effective indicators using existing NFI data at a European level. Based on data from the 2005 Italian NFI collected from 6563 plots, 18 indicators were selected following previous experiences and then used to estimate B-N-OG aggregated indexes. Relationships between the 18 indicators and the three indexes were investigated, along with comparisons of their relationships relative to forest type categories, management types and protected versus non-protected areas. Results confirm that NFI data offer valuable insights into specific B-N-OG indexes, especially concerning forest structure and deadwood. Moreover, the indexes contribute to developing meaningful relationships across geographic regions, forest categories and types of management. However, limitations in NFI field protocols are evident, as they are not explicitly designed for certain indicators. The study suggests the potential for NFIs to evolve into accessible, harmonized European reference networks for B-N-OG assessment for better supporting sustainable forest management, planning and conservation decisions related to forest ecosystems.

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach

Abstract. The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha−1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha−1, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an R2 of 0.63 with an MAE of 30 m3 ha−1. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023).

Predicting forest damage using relative abundance of multiple deer species and national forest inventory data

Deer, both native and non-native, can damage forest ecosystems when occurring at high densities, impacting biodiversity and ecological functioning at multiple levels. The ecological drivers of forest damage and the roles of single and multiple co-occurring deer species are not well understood due to a scarcity of simultaneous high resolution multi-species deer distribution and forest damage data. Here, we aimed to disentangle the relationship between forest damage, forest characteristics and the roles deer play in damaging forest ecosystems. To achieve this, we integrated novel high resolution deer distribution data for multiple deer species (native and non-native) with forest inventory data collected in 1,681 sampling stations across Ireland to (i) understand environmental drivers of damage, and (ii) demonstrate the utility of this approach in providing predicted damage scenarios based on the relative abundance of different co-occurring deer species. We found that forest characteristics played a key role in the severity and type of damage risk that deer posed. All damage types were more prevalent in forests with greater tree densities where deer are more likely to find refuge from human disturbance. Bark stripping damage was more prevalent in mature forests with high tree diversity and ground level flora (e.g., bryophytes, herbs, and shrubs). Similarly, browsing damage was more prevalent in forests with greater tree richness but with understorey vegetation dominated by grass and ferns. Fraying damage was more common in mixed woodlands with understories dominated by bryophytes and grass. Crucially, we found that type and severity of forest damage were shaped by the interaction of multiple deer species occurring simultaneously, particularly at high densities, suggesting subtle inter-species competition and exclusion/partition dynamics that require further investigation to understand the ecological mechanism. Finally, we developed predicted damage scenarios to precisely predict where damage is likely to occur based on relative densities of two co-occurring deer species. We predicted high levels of damage in sika and/or red deer hotspots, matching areas of highly concentrated deer distributions. Our study highlights the ecological drivers of deer-caused forest damage, the role that co-occurring native and non-native deer species may play on forest damage within a large spatial scale and describes an approach that can be replicated in other countries with similar datasets. By combining reliable species distribution models with the national forest inventory data, we can now provide a useful tool for practitioners to help alleviate and mitigate forest damage and human wildlife conflicts.

Current state of mixed forests available for wood supply in Finland and Sweden

ABSTRACT This study’s objectives were to suggest harmonised criteria for the definition of mixed forests for two Nordic countries, describe their principal mixture types, and provide an overview of their current extent. We used national forest inventory data compiled in Finland and Sweden, considering the forest available for wood supply (FAWS), excluding seedling and sapling plots before canopy closure. The definition of the mixed forest was based on the threshold criteria, which indicate the basal area proportion of the dominant tree species of the total in a stand. The proportion of mixed forests increased with higher threshold criteria: 21% to 42% in Finland and 24% to 49% in Sweden, as the threshold criterion was changed from 65% to 85%. With a threshold criterion of 75%, the area of mixed FAWS was 5.6 million ha (31% of FAWS) in Finland and 6.5 million ha (36%) in Sweden. The dominant mixture type was the pine-spruce-birches mixture (31%) in Finland and the pine-spruce mixture (29%) in Sweden. The proportion of peatland forest of mixed forests was similar in the countries: 9–10%. The mixed forests proportion increased from north boreal to hemiboreal, increasing with more mature development classes.

Considering both sexual and clonal reproduction could help loosen the conflict of use over Robinia pseudoacacia

Black locust (Robinia pseudocacacia) is a widely planted tree for wood production; however, it is a very problematic invasive species through Europe. At local scale, this tree is able to invades adjacent ecosystems owing to its strong ability of asexual reproduction through root suckers. Due to this phenomenon, the role of sexual reproduction has been quite neglected at local scale. Here, we propose a double approach (i) to evaluate the ability of the species to develop under forest cover using French national forest inventory data (ii) to quantify the role of sexual and asexual reproduction at local scale using a simple visual assessment method. We evidenced that juvenile black locust regenerates in broadleaved forests where R. pseudoacacia did not contribute to the adult canopy; moreover, a significant higher presence in riparian forests relatively to non-riparian ones was demonstrated using national forest inventory data. Regeneration from sexual reproduction mode significantly contributes to local scale invasion (38% of the up-rooted juvenile plants) with a significantly higher ratio of sexual reproduction within oak dominated forests compared to pine dominated ones. We conclude that black locust forestry should consider that both reproduction modes contribute to the species natural regeneration and that both modes can occur under both coniferous and broadleaved forest cover. Thus, we propose to set up safety buffers around sensitive ecosystems such as natural reserves or riparian areas, to maintain black locust cultivation while minimising undesired invasions in natural areas.

A large carbon sink induced by the implementation of the largest afforestation program on Earth

BackgroundThree-North Afforestation Program (TNAP) in China is the largest ecological restoration project on Earth (ongoing from 1978 to 2050), harboring a huge area of newly planted forests, which provides a wealth of goods and ecosystem services that benefit society at levels ranging from region to East Asia. This project-induced carbon (C) sink has been expected to be large, but its size and location remain uncertain.ResultsIn this study, we investigated the changes in the C stocks of biomass, soil C and the C accumulation benefited from the ecological effects in the project areas from 1978 to 2017 within the Three-North regions (4.069 × 106 km2), and evaluated its project-induced C sequestration. Using a combination of remote sensing images, field observations and national forest inventory data, we estimated a total ecosystem sink of 47.06 Tg C per year (1 Tg = 1012 g) increased by the TNAP implementation. Importantly, we first found that the C sink via the ecological effects of this project could contribute to a high proportion up to 15.94%, indicating a critical role of ecological effects in shaping the distribution of C stocks in the protective forests. This finding suggests that it is necessary to explicitly consider carbon sequestration benefited from the ecological effects when estimating C sink and parameterizing C models of the restoration projects in China and globally.ConclusionsOur results update the estimates of C pools in the world's largest ecological restoration project area, demonstrating that this project has substantially contributed to mitigating the climate change.

Competition‐induced tree mortality across Europe is driven by shade tolerance, proportion of conspecifics and drought

Abstract Forest stand densities are increasing in the boreal and temperate biomes, suggesting that tree‐tree competition is intensifying. Anticipating the consequences of this intensified competition is difficult because competition‐induced mortality may depend not only on the occurrence of extreme climatic events such as drought, but also on stand composition, since tree species differ in their ability to compete and tolerate competition. A better understanding of the effects of stand composition and drought on competition‐induced mortality would help to anticipate future changes in forest ecosystems. We studied the tree‐level probability of competition‐induced mortality using National Forest Inventory data from three European countries (Finland, France and Germany), covering a latitudinal gradient from the Mediterranean to the Arctic. We investigated how (i) the proportion of conspecifics, (ii) the shade tolerance (ST) of the focal tree and its competitors and (iii) drought events modify the effect of competition on tree mortality. We used a generalized linear mixed model on a dataset of 461,109 trees representing 39 species on 48,088 individual plots. Competition, measured as the basal area of larger trees, was a stronger driver of background mortality (BM) than tree size and climate. A higher proportion of conspecifics increased the competition effect on mortality, showing that conspecific individuals had a higher competitive effect compared to heterospecific individuals. The competition effect on mortality also increased as a function of the ST of neighbouring trees, suggesting an increased shading effect. A higher ST of a focal tree decreased the competition effect on mortality. Drought anomalies increased the competition effect, resulting in a higher mortality probability for the most suppressed trees. Synthesis. Competition was the main driver of background mortality. Increasing stand density increased competition‐induced tree mortality in both monospecific and mixed stands, but to different extents depending on the proportion of conspecifics and tree species shade tolerance (ST). Drought periods increase mortality, especially among the most suppressed trees, suggesting an interaction between competitive status and drought. Incorporating more detailed information on stand composition and tree species ST into tree mortality models will improve our understanding of forest dynamics in a changing climate.

Tree size variation induced by stand age mainly regulates aboveground biomass across three major stands of temperate forests in South Korea

Forest biomass and biodiversity are the most important elements of forest functions and ecosystem services. In this study, we explore the possibilities and ways to enhance ecosystem functions and services related to biomass and biodiversity. Biotic drivers (i.e., species, phylogenetic and functional diversity, stand structural attributes, and community-weighted mean of trait values), abiotic drivers (i.e., topography and climate), and stand age were extracted as independent variables to explain aboveground biomass (AGB). Using South Korea’s 7th National Forest Inventory data, we analyzed 2,070 plots belonging to the natural forests consisting of 394 plots (19.0%) of coniferous stands, 829 plots (40.0%) of broadleaved stands, and 847 plots (40.9%) of mixed stands. Multimodel inference test and model-averaging approaches were conducted to determine the most significant control variables on AGB in each stand type, and piecewise structural equation modeling was conducted to quantify the relationships and directions between the variables. Abiotic drivers, including stand age and climate moisture index, control tree size variation in all stand types, but biotic drivers control AGB through different mechanisms depending on the stand type. Our results show that there were differences in the composition of variables for controlling AGB among stand types. Across all forest types and total stands, we found that increasing the tree size variation is the key driver of increasing AGB as stand age increases. Our study suggests that forest carbon accumulation by stand type can be enhanced if the key drivers of each stand type are properly managed across forest succession, and different forest management plans that consider different regulation factors among stand types are required. Moreover, it is important to adapt resource use patterns for each stand type with considering environmental conditions to maintain healthy and sustainable forests.

Effects of cluster plot design parameters on landscape fragmentation estimates: A case study using data from the Swedish national forest inventory

Forest fragmentation is commonly characterized using indices derived from analyses of classified land cover maps. An alternative is to use data obtained from sampling, such as those from a national forest inventory (NFI). The main objective of the current study is fill knowledge gaps on the performance of sample-based forest fragmentation metrics calculated with different cluster plot designs and under different forest conditions. A set of NFI cluster plot designs, each with different geometric properties, was created from Swedish NFI data. Each member of the set was used to calculate the fragmentation metrics mean patch size (MPS) and perimeter-area ratio (PA). Impacts of plot design parameters on metric estimates and their precision were assessed.Important differences in metric values were observed both within and between regions under different plot design scenarios; within regions, ranges of PA and MPS values were large, and confidence intervals for the minimum and maximum metric values did not overlap. Weighted least squares regression significance testing results suggest that subplot separation distance was an impactful design factor whereas number of subplots and cluster shape were less important. However, cluster plots with more and widely-separated subplots yielded estimates that were more precise (lower relative sampling errors) than smaller, more compact clusters. We suggest that care should be taken when interpreting the physical meaning of the metrics under study.

Integrated LiDAR-supported valuation of biomass and litter in forest ecosystems. A showcase in Spain

Belowground components (biomass and soils) can stock as much carbon as the aboveground component of forest ecosystems. In this study, we present a fully-integrated assessment of the biomass budget and the three pools evaluated: aboveground (AGBD) and belowground biomass in root systems (BGBD) and litter (LD). We turned National Forest Inventory data, airborne Light Detection and Ranging (LiDAR) data actionable to map three biomass compartments at 25-m resolution over more than 2.7 million ha of Mediterranean forests in the South-West of Spain. We assessed distributions and balanced among the three modelled components for the entire region of Extremadura and specifically for five representative forest types. Our results showed belowground biomass and litter represent an important 61 % of the AGBD stock. Among forest types, AGBD stocks were the dominant pool in pine-dominated areas while its lowers contribution was found over sparse oak forests. The three biomass pools estimated at the same resolution were used to produce ratio-based indicators to highlight areas where the contribution of belowground biomass and litter can exceed AGBD and where carbon-sequestration and conservation practices should acknowledge belowground-oriented carbon management. The recognition and valuation of biomass and carbon stocks beyond the AGBD is a must step forward that the scientific community must support in order to properly assess living components of the ecosystem such as root systems sustaining AGBD stocks and to value carbon-oriented ecosystem services related to soil-water dynamics and soil biodiversity. This study aims at enforcing a change of paradigm in forest carbon accounting, advocating for a better recognition and broader integration of living biomass in land carbon mapping.